research project ideas in computer science

Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: CompSci Research Topics

Algorithms & data structures
Artificial intelligence ( AI )
Computer networking
Database systems
Human-computer interaction
Information security (IS)
Software engineering
Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
An evaluation of machine learning algorithms used for recommender systems in streaming services
A review of approximation algorithm approaches for solving NP-hard problems
An analysis of parallel algorithms for high-performance computing of genomic data
The influence of data structures on optimal algorithm design and performance in Fintech
A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
A comparison of streaming algorithm performance for the detection of elephant flows
A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
Exploring the feasibility of applying information theory to feature extraction in retail datasets.
Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

Applying deep learning algorithms for speech recognition in speech-impaired children
A review of the impact of artificial intelligence on decision-making processes in stock valuation
An evaluation of reinforcement learning algorithms used in the production of video games
An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
An analysis of the ethical and social implications of artificial intelligence-based automated marking
The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
An examination of the use of artificial intelligence in orthopaedic surgery
The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

An analysis of the impact of 5G technology on internet penetration in rural Tanzania
Assessing the role of software-defined networking (SDN) in modern cloud-based computing
A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
Exploring the influence of cloud computing on security risks in fintech.
An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
An evaluation of the challenges and opportunities in 6G wireless network adoption
The role of network congestion control algorithms in improving network performance on streaming platforms
An analysis of network coding-based approaches for data security
Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

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Topics & Ideas: Database Systems

An analysis of big data management systems and technologies used in B2B marketing
The impact of NoSQL databases on data management and analysis in smart cities
An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
Exploring the role of data warehousing and business intelligence in global consultancies
An analysis of the use of graph databases for data modelling and analysis in recommendation systems
The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
An examination of the challenges and opportunities of distributed databases in supply chain management
Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
An exploration of how artificial intelligence is changing human-computer interaction patterns in children
An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
Assessing the influence of virtual and augmented reality on consumer purchasing patterns
An examination of the use of gesture-based interfaces in architecture
Exploring the impact of ease of use in wearable technology on geriatric user
Evaluating the ramifications of gamification in the Metaverse
A systematic review of user experience (UX) design advances associated with Augmented Reality
A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

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Topics & Ideas: Information Security

A bibliometric review of current trends in cryptography for secure communication
An analysis of secure multi-party computation protocols and their applications in cloud-based computing
An investigation of the security of blockchain technology in patient health record tracking
A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
A systematic review of secure data storage solutions used for cloud computing in the fintech industry
An analysis of intrusion detection and prevention systems used in the healthcare sector
Assessing security best practices for IoT devices in political offices
An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
A comparative study of digital signature schemes adoption in property transfers
An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

A study of agile software development methodologies and their impact on project success in pharmacology
Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
A study of the impact of DevOps practices on software development and delivery in the healthcare sector
An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
An investigation of software testing techniques and methodologies for subscription-based offerings
A review of software security practices and techniques for protecting against phishing attacks from social media
An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
Exploring the impact of software development outsourcing on project success in multinational contexts
An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

An array-based optimization framework for query processing and data analytics (Chen, 2021)
Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
Embedding constructural documentation in unit tests (Nassif, 2019)
PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
A state-based approach to context modeling and computing (Yue, 2019)
A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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Research topics and ideas about data science and big data analytics

Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Home » 500+ Computer Science Research Topics

500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

Using machine learning to detect and prevent cyber attacks
Developing algorithms for optimized resource allocation in cloud computing
Investigating the use of blockchain technology for secure and decentralized data storage
Developing intelligent chatbots for customer service
Investigating the effectiveness of deep learning for natural language processing
Developing algorithms for detecting and removing fake news from social media
Investigating the impact of social media on mental health
Developing algorithms for efficient image and video compression
Investigating the use of big data analytics for predictive maintenance in manufacturing
Developing algorithms for identifying and mitigating bias in machine learning models
Investigating the ethical implications of autonomous vehicles
Developing algorithms for detecting and preventing cyberbullying
Investigating the use of machine learning for personalized medicine
Developing algorithms for efficient and accurate speech recognition
Investigating the impact of social media on political polarization
Developing algorithms for sentiment analysis in social media data
Investigating the use of virtual reality in education
Developing algorithms for efficient data encryption and decryption
Investigating the impact of technology on workplace productivity
Developing algorithms for detecting and mitigating deepfakes
Investigating the use of artificial intelligence in financial trading
Developing algorithms for efficient database management
Investigating the effectiveness of online learning platforms
Developing algorithms for efficient and accurate facial recognition
Investigating the use of machine learning for predicting weather patterns
Developing algorithms for efficient and secure data transfer
Investigating the impact of technology on social skills and communication
Developing algorithms for efficient and accurate object recognition
Investigating the use of machine learning for fraud detection in finance
Developing algorithms for efficient and secure authentication systems
Investigating the impact of technology on privacy and surveillance
Developing algorithms for efficient and accurate handwriting recognition
Investigating the use of machine learning for predicting stock prices
Developing algorithms for efficient and secure biometric identification
Investigating the impact of technology on mental health and well-being
Developing algorithms for efficient and accurate language translation
Investigating the use of machine learning for personalized advertising
Developing algorithms for efficient and secure payment systems
Investigating the impact of technology on the job market and automation
Developing algorithms for efficient and accurate object tracking
Investigating the use of machine learning for predicting disease outbreaks
Developing algorithms for efficient and secure access control
Investigating the impact of technology on human behavior and decision making
Developing algorithms for efficient and accurate sound recognition
Investigating the use of machine learning for predicting customer behavior
Developing algorithms for efficient and secure data backup and recovery
Investigating the impact of technology on education and learning outcomes
Developing algorithms for efficient and accurate emotion recognition
Investigating the use of machine learning for improving healthcare outcomes
Developing algorithms for efficient and secure supply chain management
Investigating the impact of technology on cultural and societal norms
Developing algorithms for efficient and accurate gesture recognition
Investigating the use of machine learning for predicting consumer demand
Developing algorithms for efficient and secure cloud storage
Investigating the impact of technology on environmental sustainability
Developing algorithms for efficient and accurate voice recognition
Investigating the use of machine learning for improving transportation systems
Developing algorithms for efficient and secure mobile device management
Investigating the impact of technology on social inequality and access to resources
Machine learning for healthcare diagnosis and treatment
Machine Learning for Cybersecurity
Machine learning for personalized medicine
Cybersecurity threats and defense strategies
Big data analytics for business intelligence
Blockchain technology and its applications
Human-computer interaction in virtual reality environments
Artificial intelligence for autonomous vehicles
Natural language processing for chatbots
Cloud computing and its impact on the IT industry
Internet of Things (IoT) and smart homes
Robotics and automation in manufacturing
Augmented reality and its potential in education
Data mining techniques for customer relationship management
Computer vision for object recognition and tracking
Quantum computing and its applications in cryptography
Social media analytics and sentiment analysis
Recommender systems for personalized content delivery
Mobile computing and its impact on society
Bioinformatics and genomic data analysis
Deep learning for image and speech recognition
Digital signal processing and audio processing algorithms
Cloud storage and data security in the cloud
Wearable technology and its impact on healthcare
Computational linguistics for natural language understanding
Cognitive computing for decision support systems
Cyber-physical systems and their applications
Edge computing and its impact on IoT
Machine learning for fraud detection
Cryptography and its role in secure communication
Cybersecurity risks in the era of the Internet of Things
Natural language generation for automated report writing
3D printing and its impact on manufacturing
Virtual assistants and their applications in daily life
Cloud-based gaming and its impact on the gaming industry
Computer networks and their security issues
Cyber forensics and its role in criminal investigations
Machine learning for predictive maintenance in industrial settings
Augmented reality for cultural heritage preservation
Human-robot interaction and its applications
Data visualization and its impact on decision-making
Cybersecurity in financial systems and blockchain
Computer graphics and animation techniques
Biometrics and its role in secure authentication
Cloud-based e-learning platforms and their impact on education
Natural language processing for machine translation
Machine learning for predictive maintenance in healthcare
Cybersecurity and privacy issues in social media
Computer vision for medical image analysis
Natural language generation for content creation
Cybersecurity challenges in cloud computing
Human-robot collaboration in manufacturing
Data mining for predicting customer churn
Artificial intelligence for autonomous drones
Cybersecurity risks in the healthcare industry
Machine learning for speech synthesis
Edge computing for low-latency applications
Virtual reality for mental health therapy
Quantum computing and its applications in finance
Biomedical engineering and its applications
Cybersecurity in autonomous systems
Machine learning for predictive maintenance in transportation
Computer vision for object detection in autonomous driving
Augmented reality for industrial training and simulations
Cloud-based cybersecurity solutions for small businesses
Natural language processing for knowledge management
Machine learning for personalized advertising
Cybersecurity in the supply chain management
Cybersecurity risks in the energy sector
Computer vision for facial recognition
Natural language processing for social media analysis
Machine learning for sentiment analysis in customer reviews
Explainable Artificial Intelligence
Quantum Computing
Blockchain Technology
Human-Computer Interaction
Natural Language Processing
Cloud Computing
Robotics and Automation
Augmented Reality and Virtual Reality
Cyber-Physical Systems
Computational Neuroscience
Big Data Analytics
Computer Vision
Cryptography and Network Security
Internet of Things
Computer Graphics and Visualization
Artificial Intelligence for Game Design
Computational Biology
Social Network Analysis
Bioinformatics
Distributed Systems and Middleware
Information Retrieval and Data Mining
Computer Networks
Mobile Computing and Wireless Networks
Software Engineering
Database Systems
Parallel and Distributed Computing
Human-Robot Interaction
Intelligent Transportation Systems
High-Performance Computing
Cyber-Physical Security
Deep Learning
Sensor Networks
Multi-Agent Systems
Human-Centered Computing
Wearable Computing
Knowledge Representation and Reasoning
Adaptive Systems
Brain-Computer Interface
Health Informatics
Cognitive Computing
Cybersecurity and Privacy
Internet Security
Cybercrime and Digital Forensics
Cloud Security
Cryptocurrencies and Digital Payments
Machine Learning for Natural Language Generation
Cognitive Robotics
Neural Networks
Semantic Web
Image Processing
Cyber Threat Intelligence
Secure Mobile Computing
Cybersecurity Education and Training
Privacy Preserving Techniques
Cyber-Physical Systems Security
Virtualization and Containerization
Machine Learning for Computer Vision
Network Function Virtualization
Cybersecurity Risk Management
Information Security Governance
Intrusion Detection and Prevention
Biometric Authentication
Machine Learning for Predictive Maintenance
Security in Cloud-based Environments
Cybersecurity for Industrial Control Systems
Smart Grid Security
Software Defined Networking
Quantum Cryptography
Security in the Internet of Things
Natural language processing for sentiment analysis
Blockchain technology for secure data sharing
Developing efficient algorithms for big data analysis
Cybersecurity for internet of things (IoT) devices
Human-robot interaction for industrial automation
Image recognition for autonomous vehicles
Social media analytics for marketing strategy
Quantum computing for solving complex problems
Biometric authentication for secure access control
Augmented reality for education and training
Intelligent transportation systems for traffic management
Predictive modeling for financial markets
Cloud computing for scalable data storage and processing
Virtual reality for therapy and mental health treatment
Data visualization for business intelligence
Recommender systems for personalized product recommendations
Speech recognition for voice-controlled devices
Mobile computing for real-time location-based services
Neural networks for predicting user behavior
Genetic algorithms for optimization problems
Distributed computing for parallel processing
Internet of things (IoT) for smart cities
Wireless sensor networks for environmental monitoring
Cloud-based gaming for high-performance gaming
Social network analysis for identifying influencers
Autonomous systems for agriculture
Robotics for disaster response
Data mining for customer segmentation
Computer graphics for visual effects in movies and video games
Virtual assistants for personalized customer service
Natural language understanding for chatbots
3D printing for manufacturing prototypes
Artificial intelligence for stock trading
Machine learning for weather forecasting
Biomedical engineering for prosthetics and implants
Cybersecurity for financial institutions
Machine learning for energy consumption optimization
Computer vision for object tracking
Natural language processing for document summarization
Wearable technology for health and fitness monitoring
Internet of things (IoT) for home automation
Reinforcement learning for robotics control
Big data analytics for customer insights
Machine learning for supply chain optimization
Natural language processing for legal document analysis
Artificial intelligence for drug discovery
Computer vision for object recognition in robotics
Data mining for customer churn prediction
Autonomous systems for space exploration
Robotics for agriculture automation
Machine learning for predicting earthquakes
Natural language processing for sentiment analysis in customer reviews
Big data analytics for predicting natural disasters
Internet of things (IoT) for remote patient monitoring
Blockchain technology for digital identity management
Machine learning for predicting wildfire spread
Computer vision for gesture recognition
Natural language processing for automated translation
Big data analytics for fraud detection in banking
Internet of things (IoT) for smart homes
Robotics for warehouse automation
Machine learning for predicting air pollution
Natural language processing for medical record analysis
Augmented reality for architectural design
Big data analytics for predicting traffic congestion
Machine learning for predicting customer lifetime value
Developing algorithms for efficient and accurate text recognition
Natural Language Processing for Virtual Assistants
Natural Language Processing for Sentiment Analysis in Social Media
Explainable Artificial Intelligence (XAI) for Trust and Transparency
Deep Learning for Image and Video Retrieval
Edge Computing for Internet of Things (IoT) Applications
Data Science for Social Media Analytics
Cybersecurity for Critical Infrastructure Protection
Natural Language Processing for Text Classification
Quantum Computing for Optimization Problems
Machine Learning for Personalized Health Monitoring
Computer Vision for Autonomous Driving
Blockchain Technology for Supply Chain Management
Augmented Reality for Education and Training
Natural Language Processing for Sentiment Analysis
Machine Learning for Personalized Marketing
Big Data Analytics for Financial Fraud Detection
Cybersecurity for Cloud Security Assessment
Artificial Intelligence for Natural Language Understanding
Blockchain Technology for Decentralized Applications
Virtual Reality for Cultural Heritage Preservation
Natural Language Processing for Named Entity Recognition
Machine Learning for Customer Churn Prediction
Big Data Analytics for Social Network Analysis
Cybersecurity for Intrusion Detection and Prevention
Artificial Intelligence for Robotics and Automation
Blockchain Technology for Digital Identity Management
Virtual Reality for Rehabilitation and Therapy
Natural Language Processing for Text Summarization
Machine Learning for Credit Risk Assessment
Big Data Analytics for Fraud Detection in Healthcare
Cybersecurity for Internet Privacy Protection
Artificial Intelligence for Game Design and Development
Blockchain Technology for Decentralized Social Networks
Virtual Reality for Marketing and Advertising
Natural Language Processing for Opinion Mining
Machine Learning for Anomaly Detection
Big Data Analytics for Predictive Maintenance in Transportation
Cybersecurity for Network Security Management
Artificial Intelligence for Personalized News and Content Delivery
Blockchain Technology for Cryptocurrency Mining
Virtual Reality for Architectural Design and Visualization
Natural Language Processing for Machine Translation
Machine Learning for Automated Image Captioning
Big Data Analytics for Stock Market Prediction
Cybersecurity for Biometric Authentication Systems
Artificial Intelligence for Human-Robot Interaction
Blockchain Technology for Smart Grids
Virtual Reality for Sports Training and Simulation
Natural Language Processing for Question Answering Systems
Machine Learning for Sentiment Analysis in Customer Feedback
Big Data Analytics for Predictive Maintenance in Manufacturing
Cybersecurity for Cloud-Based Systems
Artificial Intelligence for Automated Journalism
Blockchain Technology for Intellectual Property Management
Virtual Reality for Therapy and Rehabilitation
Natural Language Processing for Language Generation
Machine Learning for Customer Lifetime Value Prediction
Big Data Analytics for Predictive Maintenance in Energy Systems
Cybersecurity for Secure Mobile Communication
Artificial Intelligence for Emotion Recognition
Blockchain Technology for Digital Asset Trading
Virtual Reality for Automotive Design and Visualization
Natural Language Processing for Semantic Web
Machine Learning for Fraud Detection in Financial Transactions
Big Data Analytics for Social Media Monitoring
Cybersecurity for Cloud Storage and Sharing
Artificial Intelligence for Personalized Education
Blockchain Technology for Secure Online Voting Systems
Virtual Reality for Cultural Tourism
Natural Language Processing for Chatbot Communication
Machine Learning for Medical Diagnosis and Treatment
Big Data Analytics for Environmental Monitoring and Management.
Cybersecurity for Cloud Computing Environments
Virtual Reality for Training and Simulation
Big Data Analytics for Sports Performance Analysis
Cybersecurity for Internet of Things (IoT) Devices
Artificial Intelligence for Traffic Management and Control
Blockchain Technology for Smart Contracts
Natural Language Processing for Document Summarization
Machine Learning for Image and Video Recognition
Blockchain Technology for Digital Asset Management
Virtual Reality for Entertainment and Gaming
Natural Language Processing for Opinion Mining in Online Reviews
Machine Learning for Customer Relationship Management
Big Data Analytics for Environmental Monitoring and Management
Cybersecurity for Network Traffic Analysis and Monitoring
Artificial Intelligence for Natural Language Generation
Blockchain Technology for Supply Chain Transparency and Traceability
Virtual Reality for Design and Visualization
Natural Language Processing for Speech Recognition
Machine Learning for Recommendation Systems
Big Data Analytics for Customer Segmentation and Targeting
Cybersecurity for Biometric Authentication
Artificial Intelligence for Human-Computer Interaction
Blockchain Technology for Decentralized Finance (DeFi)
Virtual Reality for Tourism and Cultural Heritage
Machine Learning for Cybersecurity Threat Detection and Prevention
Big Data Analytics for Healthcare Cost Reduction
Cybersecurity for Data Privacy and Protection
Artificial Intelligence for Autonomous Vehicles
Blockchain Technology for Cryptocurrency and Blockchain Security
Virtual Reality for Real Estate Visualization
Natural Language Processing for Question Answering
Big Data Analytics for Financial Markets Prediction
Cybersecurity for Cloud-Based Machine Learning Systems
Artificial Intelligence for Personalized Advertising
Blockchain Technology for Digital Identity Verification
Virtual Reality for Cultural and Language Learning
Natural Language Processing for Semantic Analysis
Machine Learning for Business Forecasting
Big Data Analytics for Social Media Marketing
Artificial Intelligence for Content Generation
Blockchain Technology for Smart Cities
Virtual Reality for Historical Reconstruction
Natural Language Processing for Knowledge Graph Construction
Machine Learning for Speech Synthesis
Big Data Analytics for Traffic Optimization
Artificial Intelligence for Social Robotics
Blockchain Technology for Healthcare Data Management
Virtual Reality for Disaster Preparedness and Response
Natural Language Processing for Multilingual Communication
Machine Learning for Emotion Recognition
Big Data Analytics for Human Resources Management
Cybersecurity for Mobile App Security
Artificial Intelligence for Financial Planning and Investment
Blockchain Technology for Energy Management
Virtual Reality for Cultural Preservation and Heritage.
Big Data Analytics for Healthcare Management
Cybersecurity in the Internet of Things (IoT)
Artificial Intelligence for Predictive Maintenance
Computational Biology for Drug Discovery
Virtual Reality for Mental Health Treatment
Machine Learning for Sentiment Analysis in Social Media
Human-Computer Interaction for User Experience Design
Cloud Computing for Disaster Recovery
Quantum Computing for Cryptography
Intelligent Transportation Systems for Smart Cities
Cybersecurity for Autonomous Vehicles
Artificial Intelligence for Fraud Detection in Financial Systems
Social Network Analysis for Marketing Campaigns
Cloud Computing for Video Game Streaming
Machine Learning for Speech Recognition
Augmented Reality for Architecture and Design
Natural Language Processing for Customer Service Chatbots
Machine Learning for Climate Change Prediction
Big Data Analytics for Social Sciences
Artificial Intelligence for Energy Management
Virtual Reality for Tourism and Travel
Cybersecurity for Smart Grids
Machine Learning for Image Recognition
Augmented Reality for Sports Training
Natural Language Processing for Content Creation
Cloud Computing for High-Performance Computing
Artificial Intelligence for Personalized Medicine
Virtual Reality for Architecture and Design
Augmented Reality for Product Visualization
Natural Language Processing for Language Translation
Cybersecurity for Cloud Computing
Artificial Intelligence for Supply Chain Optimization
Blockchain Technology for Digital Voting Systems
Virtual Reality for Job Training
Augmented Reality for Retail Shopping
Natural Language Processing for Sentiment Analysis in Customer Feedback
Cloud Computing for Mobile Application Development
Artificial Intelligence for Cybersecurity Threat Detection
Blockchain Technology for Intellectual Property Protection
Virtual Reality for Music Education
Machine Learning for Financial Forecasting
Augmented Reality for Medical Education
Natural Language Processing for News Summarization
Cybersecurity for Healthcare Data Protection
Artificial Intelligence for Autonomous Robots
Virtual Reality for Fitness and Health
Machine Learning for Natural Language Understanding
Augmented Reality for Museum Exhibits
Natural Language Processing for Chatbot Personality Development
Cloud Computing for Website Performance Optimization
Artificial Intelligence for E-commerce Recommendation Systems
Blockchain Technology for Supply Chain Traceability
Virtual Reality for Military Training
Augmented Reality for Advertising
Natural Language Processing for Chatbot Conversation Management
Cybersecurity for Cloud-Based Services
Artificial Intelligence for Agricultural Management
Blockchain Technology for Food Safety Assurance
Virtual Reality for Historical Reenactments
Machine Learning for Cybersecurity Incident Response.
Secure Multiparty Computation
Federated Learning
Internet of Things Security
Blockchain Scalability
Quantum Computing Algorithms
Explainable AI
Data Privacy in the Age of Big Data
Adversarial Machine Learning
Deep Reinforcement Learning
Online Learning and Streaming Algorithms
Graph Neural Networks
Automated Debugging and Fault Localization
Mobile Application Development
Software Engineering for Cloud Computing
Cryptocurrency Security
Edge Computing for Real-Time Applications
Natural Language Generation
Virtual and Augmented Reality
Computational Biology and Bioinformatics
Internet of Things Applications
Robotics and Autonomous Systems
Explainable Robotics
3D Printing and Additive Manufacturing
Distributed Systems
Parallel Computing
Data Center Networking
Data Mining and Knowledge Discovery
Information Retrieval and Search Engines
Network Security and Privacy
Cloud Computing Security
Data Analytics for Business Intelligence
Neural Networks and Deep Learning
Reinforcement Learning for Robotics
Automated Planning and Scheduling
Evolutionary Computation and Genetic Algorithms
Formal Methods for Software Engineering
Computational Complexity Theory
Bio-inspired Computing
Computer Vision for Object Recognition
Automated Reasoning and Theorem Proving
Natural Language Understanding
Machine Learning for Healthcare
Scalable Distributed Systems
Sensor Networks and Internet of Things
Smart Grids and Energy Systems
Software Testing and Verification
Web Application Security
Wireless and Mobile Networks
Computer Architecture and Hardware Design
Digital Signal Processing
Game Theory and Mechanism Design
Multi-agent Systems
Evolutionary Robotics
Quantum Machine Learning
Computational Social Science
Explainable Recommender Systems.
Artificial Intelligence and its applications
Cloud computing and its benefits
Cybersecurity threats and solutions
Internet of Things and its impact on society
Virtual and Augmented Reality and its uses
Blockchain Technology and its potential in various industries
Web Development and Design
Digital Marketing and its effectiveness
Big Data and Analytics
Software Development Life Cycle
Gaming Development and its growth
Network Administration and Maintenance
Machine Learning and its uses
Data Warehousing and Mining
Computer Architecture and Design
Computer Graphics and Animation
Quantum Computing and its potential
Data Structures and Algorithms
Computer Vision and Image Processing
Robotics and its applications
Operating Systems and its functions
Information Theory and Coding
Compiler Design and Optimization
Computer Forensics and Cyber Crime Investigation
Distributed Computing and its significance
Artificial Neural Networks and Deep Learning
Cloud Storage and Backup
Programming Languages and their significance
Computer Simulation and Modeling
Computer Networks and its types
Information Security and its types
Computer-based Training and eLearning
Medical Imaging and its uses
Social Media Analysis and its applications
Human Resource Information Systems
Computer-Aided Design and Manufacturing
Multimedia Systems and Applications
Geographic Information Systems and its uses
Computer-Assisted Language Learning
Mobile Device Management and Security
Data Compression and its types
Knowledge Management Systems
Text Mining and its uses
Cyber Warfare and its consequences
Wireless Networks and its advantages
Computer Ethics and its importance
Computational Linguistics and its applications
Autonomous Systems and Robotics
Information Visualization and its importance
Geographic Information Retrieval and Mapping
Business Intelligence and its benefits
Digital Libraries and their significance
Artificial Life and Evolutionary Computation
Computer Music and its types
Virtual Teams and Collaboration
Computer Games and Learning
Semantic Web and its applications
Electronic Commerce and its advantages
Multimedia Databases and their significance
Computer Science Education and its importance
Computer-Assisted Translation and Interpretation
Ambient Intelligence and Smart Homes
Autonomous Agents and Multi-Agent Systems.

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100 Great Computer Science Research Topics Ideas for 2023

Being a computer student in 2023 is not easy. Besides studying a constantly evolving subject, you have to come up with great computer science research topics at some point in your academic life. If you’re reading this article, you’re among many other students that have also come to this realization.

Interesting Computer Science Topics

Awesome research topics in computer science, hot topics in computer science, topics to publish a journal on computer science.

Controversial Topics in Computer Science

Fun AP Computer Science Topics

Exciting computer science ph.d. topics, remarkable computer science research topics for undergraduates, incredible final year computer science project topics, advanced computer science topics, unique seminars topics for computer science, exceptional computer science masters thesis topics, outstanding computer science presentation topics.

Key Computer Science Essay Topics

Main Project Topics for Computer Science

We Can Help You with Computer Science Topics

Whether you’re earnestly searching for a topic or stumbled onto this article by accident, there is no doubt that every student needs excellent computer science-related topics for their paper. A good topic will not only give your essay or research a good direction but will also make it easy to come up with supporting points. Your topic should show all your strengths as well.

Fortunately, this article is for every student that finds it hard to generate a suitable computer science topic. The following 100+ topics will help give you some inspiration when creating your topics. Let’s get into it.

One of the best ways of making your research paper interesting is by coming up with relevant topics in computer science . Here are some topics that will make your paper immersive:

Evolution of virtual reality
What is green cloud computing
Ways of creating a Hopefield neural network in C++
Developments in graphic systems in computers
The five principal fields in robotics
Developments and applications of nanotechnology
Differences between computer science and applied computing

Your next research topic in computer science shouldn’t be tough to find once you’ve read this section. If you’re looking for simple final year project topics in computer science, you can find some below.

Applications of the blockchain technology in the banking industry
Computational thinking and how it influences science
Ways of terminating phishing
Uses of artificial intelligence in cyber security
Define the concepts of a smart city
Applications of the Internet of Things
Discuss the applications of the face detection application

Whenever a topic is described as “hot,” it means that it is a trendy topic in computer science. If computer science project topics for your final years are what you’re looking for, have a look at some below:

Applications of the Metaverse in the world today
Discuss the challenges of machine learning
Advantages of artificial intelligence
Applications of nanotechnology in the paints industry
What is quantum computing?
Discuss the languages of parallel computing
What are the applications of computer-assisted studies?

Perhaps you’d like to write a paper that will get published in a journal. If you’re searching for the best project topics for computer science students that will stand out in a journal, check below:

Developments in human-computer interaction
Applications of computer science in medicine
Developments in artificial intelligence in image processing
Discuss cryptography and its applications
Discuss methods of ransomware prevention
Applications of Big Data in the banking industry
Challenges of cloud storage services in 2023

Controversial Topics in Computer Science

Some of the best computer science final year project topics are those that elicit debates or require you to take a stand. You can find such topics listed below for your inspiration:

Can robots be too intelligent?
Should the dark web be shut down?
Should your data be sold to corporations?
Will robots completely replace the human workforce one day?
How safe is the Metaverse for children?
Will artificial intelligence replace actors in Hollywood?
Are social media platforms safe anymore?

Are you a computer science student looking for AP topics? You’re in luck because the following final year project topics for computer science are suitable for you.

Standard browser core with CSS support
Applications of the Gaussian method in C++ development in integrating functions
Vital conditions of reducing risk through the Newton method
How to reinforce machine learning algorithms.
How do artificial neural networks function?
Discuss the advancements in computer languages in machine learning
Use of artificial intelligence in automated cars

When studying to get your doctorate in computer science, you need clear and relevant topics that generate the reader’s interest. Here are some Ph.D. topics in computer science you might consider:

Developments in information technology
Is machine learning detrimental to the human workforce?
How to write an algorithm for deep learning
What is the future of 5G in wireless networks
Statistical data in Maths modules in Python
Data retention automation from a website using API
Application of modern programming languages

Looking for computer science topics for research is not easy for an undergraduate. Fortunately, these computer science project topics should make your research paper easy:

Ways of using artificial intelligence in real estate
Discuss reinforcement learning and its applications
Uses of Big Data in science and medicine
How to sort algorithms using Haskell
How to create 3D configurations for a website
Using inverse interpolation to solve non-linear equations
Explain the similarities between the Internet of Things and artificial intelligence

Your dissertation paper is one of the most crucial papers you’ll ever do in your final year. That’s why selecting the best ethics in computer science topics is a crucial part of your paper. Here are some project topics for the computer science final year.

How to incorporate numerical methods in programming
Applications of blockchain technology in cloud storage
How to come up with an automated attendance system
Using dynamic libraries for site development
How to create cubic splines
Applications of artificial intelligence in the stock market
Uses of quantum computing in financial modeling

Your instructor may want you to challenge yourself with an advanced science project. Thus, you may require computer science topics to learn and research. Here are some that may inspire you:

Discuss the best cryptographic protocols
Advancement of artificial intelligence used in smartphones
Briefly discuss the types of security software available
Application of liquid robots in 2023
How to use quantum computers to solve decoherence problem
macOS vs. Windows; discuss their similarities and differences
Explain the steps taken in a cyber security audit

When searching for computer science topics for a seminar, make sure they are based on current research or events. Below are some of the latest research topics in computer science:

How to reduce cyber-attacks in 2023
Steps followed in creating a network
Discuss the uses of data science
Discuss ways in which social robots improve human interactions
Differentiate between supervised and unsupervised machine learning
Applications of robotics in space exploration
The contrast between cyber-physical and sensor network systems

Are you looking for computer science thesis topics for your upcoming projects? The topics below are meant to help you write your best paper yet:

Applications of computer science in sports
Uses of computer technology in the electoral process
Using Fibonacci to solve the functions maximum and their implementations
Discuss the advantages of using open-source software
Expound on the advancement of computer graphics
Briefly discuss the uses of mesh generation in computational domains
How much data is generated from the internet of things?

A computer science presentation requires a topic relevant to current events. Whether your paper is an assignment or a dissertation, you can find your final year computer science project topics below:

Uses of adaptive learning in the financial industry
Applications of transitive closure on graph
Using RAD technology in developing software
Discuss how to create maximum flow in the network
How to design and implement functional mapping
Using artificial intelligence in courier tracking and deliveries
How to make an e-authentication system

Key Computer Science Essay Topics

You may be pressed for time and require computer science master thesis topics that are easy. Below are some topics that fit this description:

What are the uses of cloud computing in 2023
Discuss the server-side web technologies
Compare and contrast android and iOS
How to come up with a face detection algorithm
What is the future of NFTs
How to create an artificial intelligence shopping system
How to make a software piracy prevention algorithm

One major mistake students make when writing their papers is selecting topics unrelated to the study at hand. This, however, will not be an issue if you get topics related to computer science, such as the ones below:

Using blockchain to create a supply chain management system
How to protect a web app from malicious attacks
Uses of distributed information processing systems
Advancement of crowd communication software since COVID-19
Uses of artificial intelligence in online casinos
Discuss the pillars of math computations
Discuss the ethical concerns arising from data mining

We Can Help You with Computer Science Topics, Essays, Thesis, and Research Papers

We hope that this list of computer science topics helps you out of your sticky situation. We do offer other topics in different subjects. Additionally, we also offer professional writing services tailor-made for you.

We understand what students go through when searching the internet for computer science research paper topics, and we know that many students don’t know how to write a research paper to perfection. However, you shouldn’t have to go through all this when we’re here to help.

Don’t waste any more time; get in touch with us today and get your paper done excellently.

Latest Computer Science Research Topics for 2024

Home Blog Programming Latest Computer Science Research Topics for 2024

Everybody sees a dream—aspiring to become a doctor, astronaut, or anything that fits your imagination. If you were someone who had a keen interest in looking for answers and knowing the “why” behind things, you might be a good fit for research. Further, if this interest revolved around computers and tech, you would be an excellent computer researcher!

As a tech enthusiast, you must know how technology is making our life easy and comfortable. With a single click, Google can get you answers to your silliest query or let you know the best restaurants around you. Do you know what generates that answer? Want to learn about the science going on behind these gadgets and the internet?

For this, you will have to do a bit of research. Here we will learn about top computer science thesis topics and computer science thesis ideas.

Why is Research in Computer Science Important?

Computers and technology are becoming an integral part of our lives. We are dependent on them for most of our work. With the changing lifestyle and needs of the people, continuous research in this sector is required to ease human work. However, you need to be a certified researcher to contribute to the field of computers. You can check out Advance Computer Programming certification to learn and advance in the versatile language and get hands-on experience with all the topics of C# application development.

1. Innovation in Technology

Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world. Through research, scientists and engineers can create new hardware, software, and algorithms that improve the functionality, performance, and usability of computers and other digital devices.

2. Problem-Solving Capabilities

From disease outbreaks to climate change, solving complex problems requires the use of advanced computer models and algorithms. Computer science research enables scholars to create methods and tools that can help in resolving these challenging issues in a blink of an eye.

3. Enhancing Human Life

Computer science research has the potential to significantly enhance human life in a variety of ways. For instance, researchers can produce educational software that enhances student learning or new healthcare technology that improves clinical results. If you wish to do Ph.D., these can become interesting computer science research topics for a PhD.

4. Security Assurance

As more sensitive data is being transmitted and kept online, security is our main concern. Computer science research is crucial for creating new security systems and tactics that defend against online threats.

1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges

Welcome to the era of seamless connectivity and unparalleled efficiency! Blockchain and edge computing are two cutting-edge technologies that have the potential to revolutionize numerous sectors. Blockchain is a distributed ledger technology that is decentralized and offers a safe and transparent method of storing and transferring data.

As a young researcher, you can pave the way for a more secure, efficient, and scalable architecture that integrates blockchain and edge computing systems. So, let's roll up our sleeves and get ready to push the boundaries of technology with this exciting innovation!

Blockchain helps to reduce latency and boost speed. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Integrating edge computing with blockchain technologies can help to achieve safer, more effective, and scalable architecture.

Moreover, this research title for computer science might open doors of opportunities for you in the financial sector.

2. A Survey on Edge Computing Systems and Tools

With the rise in population, the data is multiplying by manifolds each day. It's high time we find efficient technology to store it. However, more research is required for the same.

Say hello to the future of computing with edge computing! The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing.

Edge computing systems bring processing power closer to the data source, resulting in faster and more efficient computing. But what tools are available to help us harness the power of edge computing?

As a part of this research, you will look at the newest edge computing tools and technologies to see how they can improve your computing experience. Here are some of the tools you might get familiar with upon completion of this research:

Apache NiFi: A framework for data processing that enables users to gather, transform, and transfer data from edge devices to cloud computing infrastructure.
Microsoft Azure IoT Edge: A platform in the cloud that enables the creation and deployment of cutting-edge intelligent applications.
OpenFog Consortium: An organization that supports the advancement of fog computing technologies and architectures is the OpenFog Consortium.

3. Machine Learning: Algorithms, Real-world Applications, and Research Directions

Machine learning is the superset of Artificial Intelligence; a ground-breaking technology used to train machines to mimic human action and work. ML is used in everything from virtual assistants to self-driving cars and is revolutionizing the way we interact with computers. But what is machine learning exactly, and what are some of its practical uses and future research directions?

To find answers to such questions, it can be a wonderful choice to pick from the pool of various computer science dissertation ideas.

You will discover how computers learn several actions without explicit programming and see how they perform beyond their current capabilities. However, to understand better, having some basic programming knowledge always helps. KnowledgeHut’s Programming course for beginners will help you learn the most in-demand programming languages and technologies with hands-on projects.

During the research, you will work on and study

Algorithm: Machine learning includes many algorithms, from decision trees to neural networks.
Applications in the Real-world: You can see the usage of ML in many places. It can early detect and diagnose diseases like cancer. It can detect fraud when you are making payments. You can also use it for personalized advertising.
Research Trend: The most recent developments in machine learning research, include explainable AI, reinforcement learning, and federated learning.

While a single research paper is not enough to bring the light on an entire domain as vast as machine learning; it can help you witness how applicable it is in numerous fields, like engineering, data science & analysis, business intelligence, and many more.

Whether you are a data scientist with years of experience or a curious tech enthusiast, machine learning is an intriguing and vital field that's influencing the direction of technology. So why not dig deeper?

4. Evolutionary Algorithms and their Applications to Engineering Problems

Imagine a system that can solve most of your complex queries. Are you interested to know how these systems work? It is because of some algorithms. But what are they, and how do they work? Evolutionary algorithms use genetic operators like mutation and crossover to build new generations of solutions rather than starting from scratch.

This research topic can be a choice of interest for someone who wants to learn more about algorithms and their vitality in engineering.

Evolutionary algorithms are transforming the way we approach engineering challenges by allowing us to explore enormous solution areas and optimize complex systems.

The possibilities are infinite as long as this technology is developed further. Get ready to explore the fascinating world of evolutionary algorithms and their applications in addressing engineering issues.

5. The Role of Big Data Analytics in the Industrial Internet of Things

Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights! Big Data Analytics is the transformative process of extracting valuable knowledge and patterns from vast and complex datasets, boosting innovation and informed decision-making.

This field allows you to transform the enormous amounts of data produced by IoT devices into insightful knowledge that has the potential to change how large-scale industries work. It's like having a crystal ball that can foretell.

Big data analytics is being utilized to address some of the most critical issues, from supply chain optimization to predictive maintenance. Using it, you can find patterns, spot abnormalities, and make data-driven decisions that increase effectiveness and lower costs for several industrial operations by analyzing data from sensors and other IoT devices.

The area is so vast that you'll need proper research to use and interpret all this information. Choose this as your computer research topic to discover big data analytics' most compelling applications and benefits. You will see that a significant portion of industrial IoT technology demands the study of interconnected systems, and there's nothing more suitable than extensive data analysis.

6. An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy

Are you concerned about the security and privacy of your Internet of Things (IoT) devices? As more and more devices become connected, it is more important than ever to protect the security and privacy of data. If you are interested in cyber security and want to find new ways of strengthening it, this is the field for you.

ELIB is a cutting-edge solution that offers private and secure communication between IoT devices by fusing the strength of blockchain with lightweight cryptography. This architecture stores encrypted data on a distributed ledger so only parties with permission can access it.

But why is ELIB so practical and portable? ELIB uses lightweight cryptography to provide quick and effective communication between devices, unlike conventional blockchain models that need complicated and resource-intensive computations.

Due to its increasing vitality, it is gaining popularity as a research topic as someone aware that this framework works and helps reinstate data security is highly demanded in financial and banking.

7. Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics

Welcome to the world where machines decode the beauty of the human language. With natural language processing (NLP) techniques, we can analyze the interactions between humans and computers to reveal valuable insights for development research topics. It is also one of the most crucial PhD topics in computer science as NLP-based applications are gaining more and more traction.

Etymologically, natural language processing (NLP) is a potential technique that enables us to examine and comprehend natural language data, such as discussions between people and machines. Insights on user behaviour, preferences, and pain areas can be gleaned from these encounters utilizing NLP approaches.

But which specific areas should we leverage on using NLP methods? This is precisely what you’ll discover while doing this computer science research.

Gear up to learn more about the fascinating field of NLP and how it can change how we design and interact with technology, whether you are a UX designer, a data scientist, or just a curious tech lover and linguist.

8. All One Needs to Know About Fog Computing and Related Edge Computing Paradigms: A Complete Survey

If you are an IoT expert or a keen lover of the Internet of Things, you should leap and move forward to discovering Fog Computing. With the rise of connected devices and the Internet of Things (IoT), traditional cloud computing models are no longer enough. That's where fog computing and related edge computing paradigms come in.

Fog computing is a distributed approach that brings processing and data storage closer to the devices that generate and consume data by extending cloud computing to the network's edge.

As computing technologies are significantly used today, the area has become a hub for researchers to delve deeper into the underlying concepts and devise more and more fog computing frameworks. You can also contribute to and master this architecture by opting for this stand-out topic for your research.

Tips and Tricks to Write Computer Research Topics

Before starting to explore these hot research topics in computer science you may have to know about some tips and tricks that can easily help you.

Know your interest.
Choose the topic wisely.
Make proper research about the demand of the topic.
Get proper references.
Discuss with experts.

By following these tips and tricks, you can write a compelling and impactful computer research topic that contributes to the field's advancement and addresses important research gaps.

From machine learning and artificial intelligence to blockchain, edge computing, and big data analytics, numerous trending computer research topics exist to explore.

One of the most important trends is using cutting-edge technology to address current issues. For instance, new IIoT security and privacy opportunities are emerging by integrating blockchain and edge computing. Similarly, the application of natural language processing methods is assisting in revealing human-computer interaction and guiding the creation of new technologies.

Another trend is the growing emphasis on sustainability and moral considerations in technological development. Researchers are looking into how computer science might help in innovation.

With the latest developments and leveraging cutting-edge tools and techniques, researchers can make meaningful contributions to the field and help shape the future of technology. Going for Full-stack Developer online training will help you master the latest tools and technologies.

Frequently Asked Questions (FAQs)

Research in computer science is mainly focused on different niches. It can be theoretical or technical as well. It completely depends upon the candidate and his focused area. They may do research for inventing new algorithms or many more to get advanced responses in that field.

Yes, moreover it would be a very good opportunity for the candidate. Because computer science students may have a piece of knowledge about the topic previously. They may find Easy thesis topics for computer science to fulfill their research through KnowledgeHut.

There are several scopes available for computer science. A candidate can choose different subjects such as AI, database management, software design, graphics, and many more.

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Research projects

Find a postgraduate research project in your area of interest by exploring the research projects that we offer in the Department of Computer Science.

We have a broad range of research projects for which we are seeking doctoral students. Browse the list of projects on this page or follow the links below to find information on doctoral training opportunities, or applying for a postgraduate research programme.

Doctoral training opportunities
How to apply

Alternatively, if you would like to propose your own project then please include a research project proposal and the name of a possible supervisor with your application.

Available projects

List by research theme List by supervisor

Future computing systems projects

A Multi-Tenancy FPGA Cloud Infrastructure and Runtime System
A New Generation of Terahertz Emitters: Exploiting Electron Spin
Balancing security and privacy with data usefulness and efficiency in wireless sensor networks
Blockchain-based Local Energy Markets
Cloud Computing Security
Design and Exploration of a Memristor-enabled FPGA Architecture
Design and Implementation of an FPGA-Accelerated Data Analytics Database
Designing Safe & Explainable Neural Models in NLP
Dynamic Resource Management for Intelligent Transportation System Applications
Evaluating Systems for the Augmentation of Human Cognition
Exploring Unikernel Operating Systems Running on reconfigurable Softcore Processors
Finding a way through the Fog from the Edge to the Cloud
Guaranteeing Reliability for IoT Edge Computing Systems
Hardware Aware Training for AI Systems
Hybrid Fuzzing Concurrent Software using Model Checking and Machine Learning
Job and Task Scheduling and Resource Allocation on Parallel/Distributed systems including Cloud, Edge, Fog Computing
Machine Learning with Bio-Inspired Neural Networks
Managing the data deluge for Big Data, Internet-of-Things and/or Industry 4.0 environments
Pervasive Technology for Multimodal Human Memory Augmentation
Power Management Methodologies for IoT Edge Devices
Power Transfer Methods for Inductively Coupled 3-D ICs
Problems in large graphs representing social networks
Programmable Mixed-Signal Fabric for Machine Learning Applications
Scheduling, Resource Management and Decision Making for Cloud / Fog / Edge Computing
Security and privacy in p2p electricity trading
Skyrmion-based Electronics
Smart Security for Smart Services in an IoT Context
Spin waves dynamics for spintronic computational devices
Technology-driven Human Memory Degradation
Ultrafast spintronics with synthetic antiferromagnets

Human centred computing projects

Advising on the Use and Misuse of Collaborative Coding Workflows
Automatic Activity Analysis, Detection and Recognition
Automatic Emotion Detection, Analysis and Recognition
Automatic Experimental Design with Human in the Loop (2025 entry onward)
Biases in Physical Activity Tracking
Computer Graphics - Material Appearance Modeling and Physically Based Rendering
Design principles for glancing at information by visually disabled users
Extending Behavioural Algorithmics as a Predictor of Type 1 Diabetes Blood Glucose Highs
Geo-location as a Predictor of Type 1 Diabetes Blood Glucose
Learning of user models in human-in-the-loop machine learning (2025 entry onward)
Machine Learning and Cognitive Modelling Applied to Video Games
Models of Bio-Sensed Body Temperature and Environment as a Refinement of Type 1 Diabetes Blood Glucose Prediction Algorithmics
Music Generation and Information Processing via Deep Learning
Understanding the role of the Web on Memory for Programming Concepts
User Modeling for Physical Activity Tracking

Artificial intelligence projects

(MRC DTP) Unlocking the research potential of unstructured patient data to improve health and treatment outcomes
Abstractive multi-document summarisation
Applying Natural Language Processing to real-world patient data to optimise cancer care
Automated Repair of Deep Neural Networks
Automatic Learning of Latent Force Models
Biologically-Plausible Continual Learning
Cognitive Robotics and Human Robot Interaction
Collaborative Probabilistic Machine Learning (2025 entry onward)
Computational Modelling of Child Language Learning
Contextualised Multimedia Information Retrieval via Representation Learning
Controlled Synthesis of Virtual Patient Populations with Multimodal Representation Learning
Data Integration & Exploration on Data Lakes
Data Lake Exploration with Modern Artificial Intelligence Techniques
Data-Science Approaches to Better Understand Multimorbidity and Treatment Outcomes in Patients with Rheumatoid Arthritis
Deep Learning for Temporal Information Processing
Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
Event Coreference at Document Level
Explainable and Interpretable Machine Learning
Formal Verification for Robot Swams and Wireless Sensor Networks
Formal Verification of Robot Teams or Human Robot Interaction
Foundations and Advancement of Subontology Generation for Clinically Relevant Information
Generating Goals from Responsibilities for Long Term Autonomy
Generating explainable answers to fact verification questions
Integrated text and table mining
Interpretable machine learning for healthcare applications
Knowledge Graph Construction via Learning and Reasoning
Knowledge Graph for Guidance and Explainability in Machine Learning
Machine Learning for Vision and Language Understanding
Multi-task Learning and Applications
Neuro-sybolic theorem proving
Ontology Informed Machine Learning for Computer Vision
Optimization and verification of systems modelled using neural networks
Probabilistic modelling and Bayesian machine learning (2025 entry onward)
Representation Learning and Its Applications
Software verification with contrained Horn clauses and first-order theorem provers
Solving PDEs via Deep Neural Nets: Underpinning Accelerated Cardiovascular Flow Modelling with Learning Theory
Solving mathematical problems using automated theorem provers
Solving non-linear constraints over continuous functions
Symmetries and Automated Theorem Proving
Text Analytics and Blog/Forum Analysis
Theorem Proving for Temporal Logics
Trustworthy Multi-source Learning (2025 entry onward)
Verification Based Model Extraction Attack and Defence for Deep Neural Networks
Zero-Shot Learning and Applications

Software and e-infrastructure projects

Automatic Detection and Repair of Software Vulnerabilities in Unmanned Aerial Vehicles
Combining Concolic Testing with Machine Learning to Find Software Vulnerabilities in the Internet of Things
Component-based Software Development.
Effective Teaching of Programming: A Detailed Investigation
Exploiting Software Vulnerabilities at Large Scale
Finding Vulnerabilities in IoT Software using Fuzzing, Symbolic Execution and Abstract Interpretation
Using Program Synthesis for Program Repair in IoT Security
Verifying Cyber-attacks in CUDA Deep Neural Networks for Self-Driving Cars

Theory and foundations projects

Application Level Verification of Solidity Smart Contracts
Categorical proof theory
Formal Methods: Hybrid Event-B and Rodin
Formal Methods: Mechanically Checking the Semantics of Hybrid Event-B
Formal Semantics of the Perfect Language
Mathematical models for concurrent systems

James Elson projects

Data science projects.

Data Wrangling
Fishing in the Data Lake
Specifying and Optimising Data Wrangling Tasks

Sophia Ananiadou projects

Mauricio alvarez projects, richard banach projects, riza batista-navarro projects, ke chen projects, sarah clinch projects, angelo cangelosi projects, jiaoyan chen projects, lucas cordeiro projects, louise dennis projects, clare dixon projects, suzanne embury projects, marie farrell projects, alejandro frangi projects, andre freitas projects, michael fisher projects, gareth henshall projects, simon harper projects, caroline jay projects, samuel kaski projects, dirk koch projects, konstantin korovin projects, kung-kiu lau projects, zahra montazeri projects, christoforos moutafis projects, tingting mu projects, anirbit mukherjee projects, mustafa mustafa projects, goran nenadic projects, paul nutter projects, nhung nguyen projects, pierre olivier projects, norman paton projects, vasilis pavlidis projects, pavlos petoumenos projects, steve pettifer projects, oliver rhodes projects, giles reger projects, rizos sakellariou projects, uli sattler projects, andrea schalk projects, renate schmidt projects, robert stevens projects, sandra sampaio projects, viktor schlegel projects, youcheng sun projects, tom thomson projects, junichi tsujii projects, markel vigo projects, ning zhang projects, liping zhao projects, hongpeng zhou projects.

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The Top 10 Most Interesting Computer Science Research Topics

Computer science touches nearly every area of our lives. With new advancements in technology, the computer science field is constantly evolving, giving rise to new computer science research topics. These topics attempt to answer various computer science research questions and how they affect the tech industry and the larger world.

Computer science research topics can be divided into several categories, such as artificial intelligence, big data and data science, human-computer interaction, security and privacy, and software engineering. If you are a student or researcher looking for computer research paper topics. In that case, this article provides some suggestions on examples of computer science research topics and questions.

Find your bootcamp match

What makes a strong computer science research topic.

A strong computer science topic is clear, well-defined, and easy to understand. It should also reflect the research’s purpose, scope, or aim. In addition, a strong computer science research topic is devoid of abbreviations that are not generally known, though, it can include industry terms that are currently and generally accepted.

Tips for Choosing a Computer Science Research Topic

Brainstorm . Brainstorming helps you develop a few different ideas and find the best topic for you. Some core questions you should ask are, What are some open questions in computer science? What do you want to learn more about? What are some current trends in computer science?
Choose a sub-field . There are many subfields and career paths in computer science . Before choosing a research topic, ensure that you point out which aspect of computer science the research will focus on. That could be theoretical computer science, contemporary computing culture, or even distributed computing research topics.
Aim to answer a question . When you’re choosing a research topic in computer science, you should always have a question in mind that you’d like to answer. That helps you narrow down your research aim to meet specified clear goals.
Do a comprehensive literature review . When starting a research project, it is essential to have a clear idea of the topic you plan to study. That involves doing a comprehensive literature review to better understand what has been learned about your topic in the past.
Keep the topic simple and clear. The topic should reflect the scope and aim of the research it addresses. It should also be concise and free of ambiguous words. Hence, some researchers recommended that the topic be limited to five to 15 substantive words. It can take the form of a question or a declarative statement.

What’s the Difference Between a Research Topic and a Research Question?

A research topic is the subject matter that a researcher chooses to investigate. You may also refer to it as the title of a research paper. It summarizes the scope of the research and captures the researcher’s approach to the research question. Hence, it may be broad or more specific. For example, a broad topic may read, Data Protection and Blockchain, while a more specific variant can read, Potential Strategies to Privacy Issues on the Blockchain.

On the other hand, a research question is the fundamental starting point for any research project. It typically reflects various real-world problems and, sometimes, theoretical computer science challenges. As such, it must be clear, concise, and answerable.

How to Create Strong Computer Science Research Questions

To create substantial computer science research questions, one must first understand the topic at hand. Furthermore, the research question should generate new knowledge and contribute to the advancement of the field. It could be something that has not been answered before or is only partially answered. It is also essential to consider the feasibility of answering the question.

2. The Influence of Extraction Methods on Big Data Mining

Data mining has drawn the scientific community’s attention, especially with the explosive rise of big data. Many research results prove that the extraction methods used have a significant effect on the outcome of the data mining process. However, a topic like this analyzes algorithms. It suggests strategies and efficient algorithms that may help understand the challenge or lead the way to find a solution.

3. Integration of 5G with Analytics and Artificial Intelligence

According to the International Finance Corporation, 5G and AI technologies are defining emerging markets and our world. Through different technologies, this research aims to find novel ways to integrate these powerful tools to produce excellent results. Subjects like this often spark great discoveries that pioneer new levels of research and innovation. A breakthrough can influence advanced educational technology, virtual reality, metaverse, and medical imaging.

4. Leveraging Asynchronous FPGAs for Crypto Acceleration

To support the growing cryptocurrency industry, there is a need to create new ways to accelerate transaction processing. This project aims to use asynchronous Field-Programmable Gate Arrays (FPGAs) to accelerate cryptocurrency transaction processing. It explores how various distributed computing technologies can influence mining cryptocurrencies faster with FPGAs and generally enjoy faster transactions.

5. Cyber Security Future Technologies

Cyber security is a trending topic among businesses and individuals, especially as many work teams are going remote. Research like this can stretch the length and breadth of the cyber security and cloud security industries and project innovations depending on the researcher’s preferences. Another angle is to analyze existing or emerging solutions and present discoveries that can aid future research.

6. Exploring the Boundaries Between Art, Media, and Information Technology

The field of computers and media is a vast and complex one that intersects in many ways. They create images or animations using design technology like algorithmic mechanism design, design thinking, design theory, digital fabrication systems, and electronic design automation. This paper aims to define how both fields exist independently and symbiotically.

7. Evolution of Future Wireless Networks Using Cognitive Radio Networks

This research project aims to study how cognitive radio technology can drive evolution in future wireless networks. It will analyze the performance of cognitive radio-based wireless networks in different scenarios and measure its impact on spectral efficiency and network capacity. The research project will involve the development of a simulation model for studying the performance of cognitive radios in different scenarios.

8. The Role of Quantum Computing and Machine Learning in Advancing Medical Predictive Systems

In a paper titled Exploring Quantum Computing Use Cases for Healthcare , experts at IBM highlighted precision medicine and diagnostics to benefit from quantum computing. Using biomedical imaging, machine learning, computational biology, and data-intensive computing systems, researchers can create more accurate disease progression prediction, disease severity classification systems, and 3D Image reconstruction systems vital for treating chronic diseases.

9. Implementing Privacy and Security in Wireless Networks

Wireless networks are prone to attacks, and that has been a big concern for both individual users and organizations. According to the Cyber Security and Infrastructure Security Agency CISA, cyber security specialists are working to find reliable methods of securing wireless networks . This research aims to develop a secure and privacy-preserving communication framework for wireless communication and social networks.

10. Exploring the Challenges and Potentials of Biometric Systems Using Computational Techniques

Much discussion surrounds biometric systems and the potential for misuse and privacy concerns. When exploring how biometric systems can be effectively used, issues such as verification time and cost, hygiene, data bias, and cultural acceptance must be weighed. The paper may take a critical study into the various challenges using computational tools and predict possible solutions.

Computer Research Questions

Why are there so many programming languages?
Is there a better way to enhance human-computer interactions in computer-aided learning?
How safe is cloud computing, and what are some ways to enhance security?
Can computers effectively assist in the sequencing of human genes?
How valuable is SCRUM methodology in Agile software development?

Choosing the Right Computer Science Research Topic

Computer science research is a vast field, and it can be challenging to choose the right topic. There are a few things to keep in mind when making this decision. Choose a topic that you are interested in. This will make it easier to stay motivated and produce high-quality research for your computer science degree .

Select a topic that is relevant to your field of study. This will help you to develop specialized knowledge in the area. Choose a topic that has potential for future research. This will ensure that your research is relevant and up-to-date. Typically, coding bootcamps provide a framework that streamlines students’ projects to a specific field, doing their search for a creative solution more effortless.

Computer Science Research Topics FAQ

To start a computer science research project, you should look at what other content is out there. Complete a literature review to know the available findings surrounding your idea. Design your research and ensure that you have the necessary skills and resources to complete the project.

The first step to conducting computer science research is to conceptualize the idea and review existing knowledge about that subject. You will design your research and collect data through surveys or experiments. Analyze your data and build a prototype or graphical model. You will also write a report and present it to a recognized body for review and publication.

You can find computer science research jobs on the job boards of many universities. Many universities have job boards on their websites that list open positions in research and academia. Also, many Slack and GitHub channels for computer scientists provide regular updates on available projects.

There are several hot topics and questions in AI that you can build your research on. Below are some AI research questions you may consider for your research paper.

Will it be possible to build artificial emotional intelligence?
Will robots replace humans in all difficult cumbersome jobs as part of the progress of civilization?
Can artificial intelligence systems self-improve with knowledge from the Internet?

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25 of today’s coolest network and computing research projects

Latest concoctions from university labs include language learning website, a newfangled internet for mobile devices and even ip over xylophones.

University labs, fueled with millions of dollars in funding and some of the biggest brains around, are bursting with new research into computer and networking technologies.

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networks, computer and a general focus on shrinking things and making them faster are among the hottest areas, with some advances already making their way into the market. Here’s a roundup of 25 such projects that caught our eyes:

This free website, Duolingo, from a pair of Carnegie Mellon University computer scientists serves double duty: It helps people learn new languages while also translating the text on Web pages into different languages.

CMU’s Luis von Ahn and Severin Hacker have attracted more than 100,000 people in a beta test of the system, which initially offered free language lessons in English, Spanish, French and German, with the computer offering advice and guidance on unknown words. Using the system could go a long way toward translating the Web, many of whose pages are unreadable by those whose language skills are narrow.

Von Ahn is a veteran of such crowdsourcing technologies, having created online reCAPTCHA puzzles to cut down on spam while simultaneously digitizing old books and periodicals. Von Ahn’s spinoff company, reCAPTCHA, was acquired by Google in 2009. Duolingo, spun off in November to offer commercial and free translation services, received $3.3 million in funding from Union Square Ventures, actor Ashton Kutcher and others.

Princeton University Computer Science researchers envision an Internet that is more flexible for operators and more useful to mobile users. Princeton’s Serval system is what Assistant Professor of Computer Science Michael Freedman calls a Service Access Layer that sits between the IP Network Layer (Layer 3) and Transport Layer (Layer 4), where it can work with unmodified network devices. Serval’s purpose is to make Web services such as Gmail and Facebook more easily accessible, regardless of where an end user is, via a services naming scheme that augments what the researchers call an IP address set-up “designed for communication between fixed hosts with topology-dependent addresses.” Data center operators could benefit by running Web servers in virtual machines across the cloud and rely less on traditional load balancers.

Serval, which Freedman describes as a “replacement” technology, will likely have its first production in service-provider networks. “Its largest benefits come from more dynamic settings, so its features most clearly benefit the cloud and mobile spaces,” he says.

If any of this sounds similar to software-defined networking (SDN), there are in fact connections. Freedman worked on an SDN/OpenFlow project at Stanford University called Ethane that was spun out into a startup called Nicira for which VMware recently plunked down $1.26 billion.

WiFi routers to the rescue

Researchers at Germany’sTechnical University in Darmstadt have described a way for home Wi-Fi routers to form a backup mesh network to be used by the police, firefighters and other emergency personnel in the case of a disaster or other incident that wipes out standard cell and phone systems.

The proliferation of Wi-Fi routers makes the researchers confident that a dense enough ad hoc network could be created, but they noted that a lack of unsecured routers would require municipalities to work with citizens to allow for the devices to be easily switched into emergency mode. The big question is whether enough citizens would really allow such access, even if security was assured.

Hyperspeed signaling

University of Tulsa engineers want to slow everything down, for just a few milliseconds, to help network administrations avoid cyberattacks.

By slowing traffic, the researchers figure more malware can be detected and then headed off via an algorithm that signals at hyperspeed to set up defenses. Though researcher Sujeet Shenoi told the publication New Scientist that it might not be cheap to set up such a defense system, between the caching system and reserved data pipes needed to support the signals.

Control-Alt-Hack

University of Washington researchers have created a card game called Control-Alt-Hack that’s designed to introduce computer science students to security topics.

The game, funded in part by Intel Labs and the National Science Foundation, made its debut at the Black Hat security conference in Las Vegas over the summer. The tabletop game involves three to six players working for an outfit dubbed Hackers, Inc., that conducts security audits and consulting, and players are issued challenges, such as hacking a hotel mini bar payment system or wireless medical implant, or converting a robotic vacuum cleaner into a toy. The game features cards (including descriptions of well-rounded hackers who rock climb, ride motorcycles and do more than sit at their computers), dice, mission cards, “hacker cred tokens” and other pieces, and is designed for players ages 14 and up. It takes about an hour to play a game. No computer security degree needed.

“We went out of our way to incorporate humor,” said co-creator Tamara Denning, a UW doctoral student in computer science and engineering, referring to the hacker descriptions and challenges on the cards. “We wanted it to be based in reality, but more importantly we want it to be fun for the players.”

Ghost-USB-Honeypot project

This effort, focused on nixing malware like Flame that spreads from computer to computer via USB storage drives, got its start based on research from Sebastian Poeplau at Bonn University’s Institute of Computer Science. Now it’s being overseen by the broader Honeynet Project.

The breakthrough by Poeplau and colleagues was to create a virtual drive that runs inside a USB drive to snag malware . According to the project website: “Basically, the honeypot emulates a USB storage device. If your machine is infected by malware that uses such devices for propagation, the honeypot will trick it into infecting the emulated device.”

One catch: the security technology only works on XP 32 bit, for starters.

IP over Xylophone Players (IPoXP)

Practical applications for running IP over xylophones might be a stretch, but doing so can teach you a few things about the truly ubiquitous protocol.

A University of California Berkeley researcher named R. Stuart Geiger led this project, which he discussed earlier this year at the Association for Computing Machinery’s Conference on Human Factors in Computing Systems . Geiger’s Internet Protocol over Xylophone Players (IPoXP) provides a fully compliant IP connection between two computers. His setup uses a pair of Arduino microcontrollers, some sensors, a pair of xylophones and two people to play the xylophones.

The exercise provided some insights into the field of Human-Computer Interaction (HCI). It emulates a technique HCI specialists use to design interfaces called umwelt, which is a practice of imagining what the world must look like to the potential users of the interface. This experiment allowed participants to get the feel for what it would be like to be a circuit.

“I don’t think I realized how robust and modular the OSI model is,” Geiger said. “The Internet was designed for much more primitive technologies, but we haven’t been able to improve on it, because it is such a brilliant model.”

Making software projects work

San Francisco State University and other researchers are puzzling over why so many software projects wind up getting ditched, fail or get completed, but late and over budget. The key, they’ve discovered, is rethinking how software engineers are trained and managed to ensure they can work as teams.

The researchers, also from Florida Atlantic University and Fulda University in Germany, are conducting a National Science Foundation-funded study with their students that they hope will result in a software model that can predict whether a team is likely to fail. Their study will entail collecting information on how often software engineering students – teamed with students at the same university and at others — meet, email each other, etc.

“We want to give advice to teachers and industry leaders on how to manage their teams,” says Dragutin Petkovic, professor and chair of SF State’s Computer Science Department. “Research overwhelmingly shows that it is ‘soft skills,’ how people work together, that are the most critical to success.”

Ultra low-power wireless

Forget about 3G, 4G and the rest: University of Arkansas engineering researchers are focused on developing very low-power wireless systems that can grab data from remote sensors regardless of distortion along the network path.

These distortion-tolerant systems would enable sensors, powered by batteries or energy-harvesting, to remain in the field for long periods of time and withstand rough conditions to monitor diverse things such as tunnel stability and animal health. By tolerating distortion, the devices would expend less energy on trying to clean up communications channels.

“If we accept the fact that distortion is inevitable in practical communication systems, why not directly design a system that is naturally tolerant to distortion?” says Jingxian Wu, assistant professor of electrical engineering.

The National Science Foundation is backing this research with $280,000 in funding.

2-way wireless

University of Waterloo engineering researchers have developed a way for wireless voice and data signals to be sent and received simultaneously on a single radio channel frequency, a breakthrough they say could make for better performing, more easily connected and more secure networks.

“This means wireless companies can increase the bandwidth of voice and data services by at least a factor of two by sending and receiving at the same time, and potentially by a much higher factor through better adaptive transmission and user management in existing networks,” said Amir Khandani, a Waterloo electrical and computer engineering professor, in a statement. He says the cost for hardware and antennas to support such a system wouldn’t cost any more than for current one-way systems.

Next up is getting industry involved in bringing such technology into the standards process.

Next steps require industry involvement by including two-way in forthcoming standards to enable wide spread implementation.

The Waterloo research was funded in part by the Canada Foundation for Innovation and the Ontario Ministry of Research and Innovation.

Spray-on batteries

Researchers at Rice University in Houston have developed a prototype spray-on battery that could allow engineers to rethink the way portable electronics are designed.

The rechargeable battery boasts similar electrical characteristics to the lithium ion batteries that power almost every mobile gadget, but it can be applied in layers to almost any surface with a conventional airbrush, said Neelam Singh, a Rice University graduate student who led a team working on the technology for more than a year.

Current lithium ion batteries are almost all variations on the same basic form: an inflexible block with electrodes at one end. Because they cannot easily be shaped, they sometimes restrict designers, particularly when it comes to small gadgets with curved surfaces, but the Rice prototypes could change that. “Today, we only have a few form factors of batteries, but this battery can be fabricated to fill the space available,” said Singh.

The battery is sprayed on in five layers: two current collectors sandwich a cathode, a polymer separator and an anode. The result is a battery that can be sprayed on to plastics, metal and ceramics.

The researchers are hoping to attract interest from electronics companies, which Singh estimates could put it into production relatively easily. “Airburshing technology is well-established. At an industrial level it could be done very fast,” she said.

Mobile Mosh pit

Two MIT researchers formally unveiled over the summer a protocol called State Synchronization Protocol (SSP) and a remote log-in program using it dubbed Mosh (for mobile shell) that’s intended as an alternative to Secure Shell (SSH) for ensuring good connectivity for mobile clients even when dealing with low bandwidth connections. SSP and Mosh have been made available for free, on GNU/, FreeBSD and OS X, via an MIT website.

SSH, often used by network and system admins for remotely logging into servers, traditionally connects computers via TCP, but it’s that use of TCP that creates headaches for mobile users, since TCP assumes that the two endpoints are fixed, says Keith Winstein, a graduate student with MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), and Mosh’s lead developer. “This is not a great way to do real-time communications,” Winstein says. SSP uses UDP, a connectionless, stateless transport mechanism that could be useful for stabilizing mobile usage of apps from Gmail to Skype.

Network Coding

Researchers from MIT, California Institute of Technology and University of Technology in Munich are putting network coding and error-correction coding to use in an effort to measure capacity of wired, and more challengingly, even small wireless networks (read their paper here for the gory details).

The researchers have figured out a way to gauge the upper and lower bounds of capacity in a wireless network. Such understanding could enable enterprises and service providers to design more efficient networks regardless of how much noise is on them (and wireless networks can get pretty darn noisy).

More details from MIT press office.

100 terahertz level

A University of Pittsburgh research team is claiming a communications breakthrough that they say could be used to speed up electronic devices such as and laptops in a big way. Their advance is a demonstrated access to more than 100 terahertz of bandwidth (electromagnetic spectrum between infrared and microwave light), whereas electronic devices traditionally have been limited to bandwidth in the gigahertz realm.

Researchers Hrvoje Petek of the University of Pittsburgh and visiting professor Muneaki Hase of the University of Tsukuba in Japan, have published their NSF-funded research findings in a paper in Nature Photonics. The researchers “detail their success in generating a frequency comb-dividing a single color of light into a series of evenly spaced spectral lines for a variety of uses-that spans a more than 100 terahertz bandwidth by exciting a coherent collective of atomic motions in a semiconductor silicon crystal.”

Petek says the advance could result in devices that carry a thousand-fold more information.

Separately, IBM researchers have developed a prototype optical chip that can transfer data at 1Tbps, the equivalent of downloading 500 high-definition movies, using light pulses rather than by sending electrons over wires.

The Holey Optochip is described as a parallel optical transceiver consisting of a transmitter and a receiver, and designed to handle gobs of data on corporate and consumer networks.

Cooling off with graphene

Graphene is starting to sound like a potential wonder material for the electronics business. Researchers from the University of California at Riverside, the University of Texas at Dallas and Austin, and Xiamen University in China have come up with a way to engineer graphene so that it has much better thermal properties. Such an isotopically-engineered version of graphene could be used to build cooler-running laptops, wireless gear and other equipment. The need for such a material has grown as electronic devices have gotten more powerful but shrunk in size.

“The important finding is the possibility of a strong enhancement of thermal conduction properties of isotopically pure graphene without substantial alteration of electrical, optical and other physical properties,” says UC Riverside Professor of Electrical Engineering Alexander Balandin, in a statement. “Isotopically pure graphene can become an excellent choice for many practical applications provided that the cost of the material is kept under control.”

Such a specially engineered type of graphene would likely first find its way into some chip packaging materials as well into photovoltaic solar cells and flexible displays, according to UC Riverside. Beyond that, it could be used with silicon in computer chips, for interconnect wiring to to spread heat.

Industry researchers have been making great strides on the graphene front in recent years. IBM, for example, last year said it had created the first graphene-based integrated circuit. Separately, two Nobel Prize winning scientists out of the U.K. have come up with a new way to use graphene – the thinnest material in the world – that could make Internet pipes feel a lot fatter.

Keeping GPS honest

Cornell University researchers are going on the offense against those who would try to hack GPS systems like those used in everything from cars to military drones to cellphone systems and power grids. Over the summer, Cornell researchers tested their system for outsmarting GPS spoofers during a Department of Homeland Security-sponsored demo involving a mini helicopter in the New Mexico desert at the White Sands Missile Range.

Cornell researchers have come up with GPS receiver modifications that allow the systems to distinguish between real and bogus signals that spoofers would use to trick cars, airplanes and other devices into handing over control. They emphasized that the threat of GPS spoofing is very real, with Iran last year claiming to have downed a GPS-guided American drone using such techniques.

Getting smartphones their ZZZZs

Purdue University researchers have come up with a way to detect smartphone bugs that can drain batteries while they’re not in use.

“These energy bugs are a silent battery killer,” says Y. Charlie Hu, a Purdue University professor of electrical and computer engineering. “A fully charged phone battery can be drained in as little as five hours.”

The problem is that app developers aren’t perfect when it comes to building programs that need to perform functions when phones are asleep and that use APIs provided by smartphone makers. The researchers, whose work is funded in part by the National Science Foundation, investigated the problem on Android phones, and found that about a quarter of some 187 apps contained errors that could drain batteries. The tools they’re developing to detect such bugs could be made available to developers to help them cut down on battery-draining mistakes.

Quantum leap in search

University of Southern California and University of Waterloo researchers are exploring how quantum computing technology can be used to speed up the math calculations needed to make Internet search speedy even as the gobs of data on the Web expands.

The challenge is that Google’s page ranking algorithm is considered by some to be the largest numerical calculation carried out worldwide, and no quantum computer exists to handle that. However, the researchers have created models of the web to simulate how quantum computing could be used to slice and dice the Web’s huge collection of data. Early findings have been encouraging, with quantum computers shown through the models to be faster at ranking the most important pages and improving as more pages needed to be ranked.

The research was funded by the NSF, NASA Ames Research Center, Lockheed Martin’s University Research Initiative and a Google faculty research award.

Sharing malware in a good way

Georgia Tech Research Institute security specialists have built a system called Titan designed to help corporate and government officials anonymously share information on malware attacks they are fighting, in hopes of fighting back against industrial espionage.

The threat analysis system plows through a repository of some 100,000 pieces of malicious code per day, and will give contributors quick feedback on malware samples that can be reverse-engineered by the Titan crew. Titan will also alert members of new threats, such as targeted spear-phishing attacks, and will keep tabs on not just Windows threats, but also those to MacIntosh and iOS, and Google Android systems.

“As a university, Georgia Tech is uniquely positioned to take this white hat role in between industry and government,” said Andrew Howard, a GTRI research scientist who is part of the Titan project . “We want to bring communities together to break down the walls between industry and government to provide a trusted, sharing platform.”

Touch-feely computing

Researchers from the University of Notre Dame, MIT and the University of Memphis are working on educational software that can respond to students’ cognitive and emotional states, and deliver the appropriate content based on how knowledgeable a student is about a subject, or even how bored he or she is with it.

AutoTutor and Affective AutoTutor get a feel for students’ mood and capabilities based on their responses to questions, including their facial expressions, speech patterns and hand movements.

“Most of the 20th-century systems required humans to communicate with computers through windows, icons, menus and pointing devices,” says Notre Dame Assistant Professor of Psychology Sidney D’Mello, an expert in human-computer interaction and AI in education . “But humans have always communicated with each other through speech and a host of nonverbal cues such as facial expressions, eye contact, posture and gesture. In addition to enhancing the content of the message, the new technology provides information regarding the cognitive states, motivation levels and social dynamics of the students.”

Mobile nets on the move

For emergency responders and others who need to take their mobile networks with them, even in fast-moving vehicles, data transmission quality can be problematic. North Carolina State University researchers say they’ve come up with a way to improve the quality of these Mobile ad hoc networks (MANET).

“Our goal was to get the highest data rate possible, without compromising the fidelity of the signal,” says Alexandra Duel-Hallen, a professor of electrical and computer engineering at NC State whose work is outlined in the paper “ Enabling Adaptive Rate and Relay Selection for 802.11 Mobile Ad Hoc Networks .”

The challenge is that fast moving wireless nodes make it difficult for relay paths to be identified by the network, as channel power tends to fluctuate much more in fast-moving vehicles. The researchers have come up with an algorithm for nodes to choose the best data relay and transmission paths, based on their experience with recent transmissions.

Tweet the Street

Researchers from the University of California, Riverside and Yahoo Research Barcelona have devised a model that uses data about volumes to predict how financial markets will behave. Their model bested other baseline strategies by 1.4% to 11% and outperformed the Dow Jones Industrial Average during a four-month simulation.

“These findings have the potential to have a big impact on market investors,” said Vagelis Hristidis, an associate professor at the Bourns College of Engineering. “With so much data available from social media, many investors are looking to sort it out and profit from it.”

The research, focused on what Twitter volumes, retweets and who is doing the tweeting might say about individual stocks, differs from that of earlier work focused on making sense of the broader market based on positive and negative sentiments in tweets.

As with so many stock-picking techniques, the researchers here tossed out plenty of caveats about their system, which they said might work quite differently, for example, during a period of overall market growth rather than the down market that their research focused on.

Franken-software

University of Texas, Dallas scientists have developed software dubbed Frankenstein that’s designed to be even more monstrous than the worst malware in the wild so that such threats can be understood better and defended against. Frankenstein can disguise itself as it swipes and messes with data, and could be used as a cover for a virus or other malware by stitching together pieces of such data to avoid antivirus detection methods.

“[Mary] Shelley’s story [about Dr. Frankenstein and his monster] is an example of a horror that can result from science, and similarly, we intend our creation as a warning that we need better detections for these types of intrusions,” said Kevin Hamlen, associate professor of computer science at UT Dallas who created the software, along with doctoral student Vishwath Mohan. “Criminals may already know how to create this kind of software, so we examined the science behind the danger this represents, in hopes of creating countermeasures.”

Such countermeasures might include infiltrating terrorist computer networks, the researchers say. To date, they’ve used the NSF and Air Force Office of Scientific Research-funded technology on benign algorithms, not any production systems.

Safer e-wallets

While e-wallets haven’t quite taken off yet, University of Pittsburgh researchers are doing their part to make potential e-wallet users more comfortable with the near-field communications (NRC) and/or RFID-powered technology.

Security has been a chief concern among potential users, who are afraid thieves could snatch their credit card numbers through the air. But these researchers have come up with a way for e-wallet credit cards to turn on and off, rather than being always on whenever in an electromagnetic field.

“Our new design integrates an antenna and other electrical circuitry that can be interrupted by a simple switch, like turning off the lights in the home or office,” says Marlin Mickle, the Nickolas A. DeCecco Professor of Engineering and executive director of the RFID Center for Excellence in the Swanson School. “The RFID or NFC credit card is disabled if left in a pocket or lying on a surface and unreadable by thieves using portable scanners.”

Mickle claims the advance is both simple and inexpensive, and once the researchers have received what they hope will be patent approval, they expect the technology to be adopted commercially.

Digging into Big Data

The University of California, Berkeley has been handed $10 million by the National Science Foundation as part of a broader $200 million federal government effort to encourage the exploration and better exploitation of massive amounts of information dubbed Big Data collected by far-flung wireless sensors, social media systems and more.

UC Berkeley has five years to use its funds for a project called the Algorithms, Machines and People (AMP) Expedition, which will focus on developing tools to extract important information from Big Data, such as trends that could predict everything from earthquakes to cyberattacks to epidemics.

“Buried within this flood of information are the keys to solving huge societal problems and answering the big questions of science,” said Michael Franklin, director of the AMP Expedition team and a UC Berkeley professor of electrical engineering and computer sciences, in a statement . “Our goal is to develop a new generation of data analysis tools that provide a quantum leap in our ability to make sense of the world around us.”

AMP Expedition researchers are building an open-source software stack called the Berkeley Data Analysis System (BDAS) that boasts large-scale machine-learning and data analysis methods, infrastructure that lets programmers take advantage of cloud and cluster computing, and crowdsourcing (in other words, human intelligence). It builds on the AMPLab formed early last year, with backing from Google, SAP and others.

Bob Brown tracks network research in his and Facebook page, as well on Twitter and Google + .

IDG News Service and other IDG publications contributed to this report

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Computer Science Projects

Computer science is a popular topic of study today, with numerous applications spanning a wide range. Final-year students frequently find it difficult to select the appropriate computer science project. On the final day of graduation, projects are the only thing that matters. Any IT-related industry where projects have a substantial impact can be chosen for a job or further education. Project work indicates knowledge depth as well as some soft skills like creativity and problem-solving. Your interview prospects will also improve as a result of your final year projects. As a result, in their last year of graduation, students are required to complete a project.

Best Domain to Choose for Conducting the Projects

Artificial intelligence
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OpenCV C++ Program for coin detection
OpenCV C++ Program to blur an image
OpenCV C++ Program to create a single colored blank image
OpenCV C++ Program to blur a Video
OpenCV C++ Program to play a video
Creating a PortScanner in C
Student Data Management in C++
OpenGL program for Simple Ball Game
Implementation of Minesweeper Game
Finding cabs nearby using Great Circle Distance formula
Program to remotely Power On a PC over the internet using the Wake-on-LAN protocol.

Java Projects :

A Group chat application in Java
Generating Password and OTP in Java
Creative Programming In Processing | Set 1 (Random Walker)
Creative Programming In Processing | Set 2 (Lorenz Attractor)

Python Projects :

Make Notepad using Tkinter
Color game using Tkinter in Python
Python | Message Encode-Decode using Tkinter
XML parsing in Python
Desktop Notifier in Python
Hangman Game in Python
Junk File Organizer in Python
Browser Automation Using Selenium
Tracking bird migration using Python-3
Twitter Sentiment Analysis using Python
Image Classifier using CNN
Implementing Photomosaics
Working with Images in Python
OpenCV Python Program to blur an image
Opencv Python program for Face Detection
Cartooning an Image using OpenCV – Python
OpenCV Python Program to analyze an image using Histogram
OpenCV Python program for Vehicle detection in a Video frame
DNA to Protein in Python 3
Viruses – From Newbie to pro
Handling Ajax request in Django
Working with zip files in Python
Morse Code Translator In Python
Simple Chat Room using Python
Creating a Proxy Webserver in Python | Set 1
Creating a Proxy Webserver in Python | Set 2
Project Idea | Audio to Sign Language Translator
Understanding Code Reuse and Modularity in Python 3
Multi-Messenger : A python project, messaging via Terminal
Movie recommendation based on emotion in Python
Implementing Web Scraping in Python with BeautifulSoup
Computer Vision module application for finding a target in a live camera

Web Development Projects

Design an Event Webpage using HTML & CSS
Design a Parallax Webpage using HTML & CSS
Design a Webpage like Technical Documentation using HTML & CSS
Design Homepages like Facebook using HTML and CSS
Page for online food delivery system using HTML and CSS
Responsive sliding login and registration forms using HTML CSS and JavaScript?
Design a Student Grade Calculator using JavaScript
Slide Down a Navigation Bar on Scroll using HTML, CSS, and JavaScript
Design a BMI Calculator using JavaScript
Task Tracker Project

Project Ideas :

Project Idea | (Static Code Checker for C++)
Project Idea | (Dynamic Hand Gesture Recognition using neural network)
Project Idea | God’s Eye
Project Idea | (Ca-solutions)
Project Idea | College Connect
Project Idea | Empower Illiterate
Project Idea | (Remote Lab Assistance)
Project Idea | (Project Approval System)
Project Idea | (Online Course Registration)
Project Idea | (Universal Database Viewer)
Project Idea | Sun Rise/Set Time Finder
Project Idea | Automatic Youtube Playlist Downloader
Project Idea | Aadhaar Thumb: A Platform to All Services
Project Idea | (Health services & Medical outcome monitoring)
Project Idea| (Magical Hangouts: An Android Messaging App)
Project Idea | JamFree
Project Idea | AI Therapist
Project Idea | Get Your Logo
Project Idea | ( Client Master)
Project Idea | (A Game of Anagrams )
Project Idea | Breakout game in Python
Project Idea | (Games using Hand Gestures)
Project Idea | Amanda: A Smart Enquiry Chatbot
Project Idea | (A.T.L.A.S: App Time Limit Alerting System)
Project Idea | Sign Language Translator for Speech-Impaired
Project Idea | Personality Analysis using hashtags from tweets
Project Idea | Recommendation System based on Graph Database
Creating a C/C++ Code Formatting tool with help of Clang tools
Project Idea (Augmented Reality – QR Code Scanner)
Project Idea (Augmented Reality – ARuco Code Detection and Estimation)
Project Idea | (CSE Webnode)
Project Idea | College Network
Project Idea | (Online UML Designing Tool)
Project Idea | Voice Based Email for Visually Challenged
Project Idea | Assist Bot
Project Idea | Social-Cop
Project Idea | MediTrack
Project Idea | (CAPTURED)
Project Idea | LinkBook
Project Idea | (Trip Planner)
Project Idea | EveMythra Bot
Project Idea | Green Rides
Project Idea | E-Ration Shop
Project Idea | Smart Elevator
Project Idea | Get Me Through
Project Idea | Innovate Email
Project Idea | NextVAC Platform
Project Idea | League of Fitness
Project Idea | (A Personal Assistant)
Project Idea | (Smart Restaurants)
Project | Scikit-learn – Whisky Clustering
Creating a Calculator for Android devices
Project Idea | Airport Security Using Beacon
Project Experience | (Brain Computer Interface)
Project Idea | ( True Random Number Generator)
Project Idea | Distributed Downloading System
Project Idea | (Personalized real-time update system)
Project Idea | Attendance System Using Smart Card
Project Idea | (Detection of Malicious Network activity)
Project Idea | Smart Waste Management System
Project Idea – Bio-Hashing : Two factor authentication
Project Idea | noteSort (Classify handwritten notes)
Project Idea | Health Application powered by IBM Watson
Project Idea | Collaborative Editor Framework in Real Time
Project Idea | Department Data Analysis Mobile Application
Project Idea | Analysis of Emergency 911 calls using Association Rule Mining
Crop monitoring and smart farming using IoT
MyHelper (Access your phone from anywhere without Internet)
Project Idea | (Robust Pedestrian detection)
Project Idea | ( Character Recognition from Image )
Project Idea | (Model based Image Compression of Medical Images)
Project Idea | Motion detection using Background Subtraction Techniques
Project Idea | (Optimization of Object-Based Image Analysis with Super-Pixel for Land Cover Mapping)
A Number Link Game
Designing Use Cases for a Project
Building a Basic Chrome Extension
How to write a good SRS for your Project
Creating WYSIWYG Document Editor | Natural Language Programming

Computer Science – FAQs

1. what is computer science .

Computer science (CS) is the study of computers and algorithmic processes including their principles, their hardware and software designs, their applications, and their impact on society.

2. Which is the best project in the final year?

The best final-year project is subjective and depends on your interests and skills. Choose a project that appeals to your interests, challenges you, and provides real learning possibilities.

3. How do I choose a major project for CSE?

To choose a major project for Computer Science Engineering (CSE), follow these steps: Identify your interests and strengths within CSE. Research current trends and emerging technologies in the field. Discuss project ideas with professors, peers, and industry professionals. Consider the project’s feasibility, scope, and potential impact. Select a project that excites you and aligns with your academic goals.

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Research projects.

The CS department has resources available for research projects. The following is a quick start guide to research projects in Computer Science Department. This quick start guide does not cover all topics and it is recommended that you consult the CS Guide for more information.

This guide is designed to help those beginning a research project by pointing to appropriate sections of the CS Guide for typical start-up tasks. Research projects typically need the following: storage space that can be shared by members of the research group, a web presence (possibly driven by a back-end database), mailing lists, code/document repositories. Here is how each of these are implemented and requested in the Computer Science Department.

Project Disk Space - We encourage projects (even single-person projects) to use disk space outside of the user home directory filesystem. This has several benefits. First, the quota is separate from any particular project member and can be much larger than we allow for home directories. Second, project members can be added and removed to change access without moving the files themselves. Third, users can collaborate and share files without having to give others access to their home directory. Finally, by keeping projects in separate partitions, CS Staff can manage our storage more efficiently. For more details, please see the Disk Space page. To request disk space, use the "Project Disk Space" form link on the left. Note that if you specify additional project members in the request form, we will automatically create a unix group consisting of you and the listed users and set the setgid flag on the project directory.
Project Web Space - To set-up a web page or web site for the project, first request project disk space and then use the "Project Web Space" form to the left to request that a subdirectory of the project space be mapped to a web URL. Project web space will give you the ability to host your research group or project-related content at its own subdomain (e.g. http://project.cs.princeton.edu/ ). Even if you are only requesting project disk space for the sole purpose of hosting a project web site, we recommend that you choose a subdirectory (e.g., public_html ) within the project disk space. This will give you the flexibility in the future to also use the project disk space for other purposes.
Project Database - If your project needs a MySQL database (perhaps as a back-end store for a web site), use the "Database" request form at the left and specify a collaborative database.
Mailing Lists - Research projects typically create one or more mailing lists to manage their communication.
Source Repository - If your group will be collaboratively developing code or writing papers, you may want to request an SVN repository from OIT (requires Princeton OIT authentication).
Rack Space for Servers - If you have physical rack-mount servers, they can be housed either in Room 002 of the CS Building or at the University data center at 151 Forrestal . Contact CS Staff for availability and additional details.
Role Accounts / Mail Aliases - please note that we do not create role accounts or provide email aliases. By properly configuring access control, role accounts should not be necessary. Email aliases can be mimicked by requesting a mailing list and selecting the "Mail Alias" type in the form.
getting started

Princeton University Links

Princeton University
Office of Information Technology (OIT)
Office of the Registrar
Finance & Treasury
Human Resources

Department of Computer Science Room 226 35 Olden Street, Princeton, NJ 08540

Hours: 8:45 a.m. to 5:00 p.m. walk-ins daily: 9:30 a.m. to 11:30 a.m., 1:30 p.m. to 3:30 p.m..

[email protected]

© 2013- document.write(new Date().getFullYear()) The Trustees of Princeton University

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200+ Computer Science Research Project Ideas for College Students in Kenya

The future depends on computational technologies and there is no better time to be a computer scientist than now. Here are some of the interesting computer science projects and research topics you can consider for your academic (or non-academic) work. Have fun selecting and building the projects.

Cyber Security Research Project Ideas for College Students

Effective encryption technology and techniques
The need for data security and cloud computing
The prevention of data loss
Tracing breaches to their source by using behavioral analytics
The use of security assertion make up language to regain corporate traffic
Necessity of access management
Techniques and tools of hackers
Handling messaging threat
Proven ways to detect emerging threats
Strategies of risk management
Mitigating against DDoS attacks
Improving network service visibility
Evaluating and managing of IoT security issues
Curbing serverless security issues
Use of firewalls to prevent network crimes
The relationship between files download and computer security
Justification for building reliable anti-malware devices
How cookies make computer security vulnerable
Necessary internet antivirus software for commercial purposes
History, effect, and remedies of ransomware
Detection and prevention of attacks by anti-malware software
How top operating systems implement security systems
Ensuring privacy of online dating apps users
Advantages and disadvantages of unified user profiles
Learning safe internet habits and why it is important
Reasons for the bring your device (BYOD) policy
Why the clean desk policy remains indispensable
The danger of social networking
The implications of malware on devices
Cyber security and children
The need for secure passwords on online platforms
Effective self-protection strategies against cybercrime
Getting rid of malware on personal computers
Data Breaches: How they happen
Software patches and updates: Why they are important for cyber security
How to secure one’s digital footprint online
Effective scam detection methods on the internet
Security of synchronized devices
Exploring the reasons for cyber crimes
The importance of social engineering
Early detection and prevention of network intrusion
The essence of coding viruses
Installation of applications on mobile phones, tablets, and computers
Security precautions needed for the safe running of Windows, Unix and macOS computers
Optimizing lost data restoration to prevent loss of vital information
Evaluating and optimizing the processes involved for user authentication

Interesting Computer Science Design Project Ideas for Finalists

Application of face detection technologies in crime deterrence
The role of an online auction system in preventing bribery
Application of computing technologies to improve academic performance
Shortcomings of the e-authentication systems
Effects of basing a system’s object movement on RGB
Application of data mining algorithms in crime prediction
Vitality of patent rights when developing computer systems
Application of computer science knowledge in social sciences
How can YouTube enhance system design and development?
Enhancing the web design process
Application of the android battery saver system

Computer Science Project Ideas for Forward Thinking Students

Effects of using chatbots on company’s response systems
How Kenya’s education system is enhancing computer science innovations
The role of coding skills in system design and development
Latest inventions in the CCTV sector
Implications of 5G technology and associated innovations
The role of biometric databases in busy workplaces
Enhancing traffic flow through computer assisted systems at the toll stations
How computers can ease traffic in busy and congested cities
Trends in mobile phone systems: A case study of Android
The role of computers in enhancing healthcare systems
How computer systems can cause harm to a society
How computer science innovations shape the world
The role of computer science in vaccine development and administration
How computer systems have led to the loss of human labor
The effects of having robots on the streets
How terrorists are using computer science to identify and attack their targets
Computer systems in developed versus developing nations
Implications of having CCTVs in public places
Why does the government have the right to access personal data on databases?
The effects of having distributed server systems in different countries
Working from the cloud: Its effects on distributed work systems
The impact of computer science symposiums and conferences
Why universities should enroll more students in computer science fields

Genius Computer Science Project Ideas for High Achievers

How to develop mobile apps for matching fingerprints
Using computer science to develop flowcharts
Evaluate the naming rules and conventions in Computer Science
Compare and contrast between dynamic and static typing
Procedural 3D tree creation in computer science and its effects
Create a basic program structure from scratch
The syntax rules and pseudo-codes for programs
How to effectively conduct documentation, comments, and coding styles
How is scoping essential in the study of Computer Science?
Order of precedence in computer science
Identification and use of numeric operators in computer science
Effectiveness of cloud computing in saving on computer storage
How to operate complex computer systems
Procedure of conducting conformance testing
Necessity of data and informatics in the world today
The role of computational science in a pandemic
Effects of breaches in cyber-physical systems
Application of computer science in cancer treatment
How often should companies conduct interoperability testing?
Factors considered in conducting a successful software research
The role of computer science in video analytics
How IT has transformed voting systems in Kenya
Usability and human factors in computer systems
Effects of virtual/augmented reality
How computer systems invade privacy without the user’s knowledge
Should websites request personal information from users?
Effects of cybersecurity policies in developed countries
How IoT is changing the world
The role of computer science in globalization
How computer science enhances sporting activities
Preservation of culture through computer science
Impacts of over-reliance on computer systems in a company

Stellar Computer Science Project for Exemplary Final Year Project

Visualization of scientific data through IT
Importance of integrating IT in social and physical sciences
The role of artificial intelligence in economic growth
New risks that IT brings to the world today
The role of innovation hubs in developing inventions
Effects of Robot Process Automation in industries
Effectiveness of using CAPTCHA in deterring spam on websites and applications
How to effectively implement honey pot for non-obtrusive spam deterrence
How is edge computing affecting the world?
The role of quantum computing in qualitative analysis
Discuss the part of blockchain in computing
How 5G will transform the mobile industry in Africa
Analyze the various techniques for processing statistical data
The role of the US as an international data hub and its implications to the global economy
The human brain versus a computer’s processor
Are computer robots going to replace human labor?
The place of compassion and empathy in computing
Compare various operating systems
Latest hacking techniques used in espionage and cyberbullying
How can the government regulate computer usage without infringing on user’s rights of expression?
How do manufacturers determine the RAM and ROM of a particular mobile phone?
How developers work with programmers to achieve a computer system
The effects of free WIFI on hacking and data protection policies in Kenya
Implications of clearing your caches immediately after use
Why is Windows operating system more popular than Linux and Ubuntu?
Troubleshooting recursive transition networks in computing
Drawbacks of the substitution model of evaluation
Why should developers care about the history of computing machines?
How to determine the analyzing procedures: A case of input size
Interface layers: Hardware, operating system, and applications
History and pragmatics of the Java platform
The essence of systematic knowledge in computer science
What it takes to be a skilled programmer
Difficulties encountered in networking and distributed computing
Challenges involved in human-computer interaction
What are search algorithms and how do they work?
Explain the evolution of search algorithms
The hazards of most computer viruses
Is SCRUM methodology the best computer science invention?
How useful is networking in the development of future computer systems?
Evolution of AI over the years
How unique is software development for mobile gadgets?
Pros and cons of cloud storage
Limits of computation and communication
Practical ways to identify lapses and improve computer data security
Discuss database management and architecture
Relationship between computer science and {a subject of interest}
Privacy, memory, and security in the cloud storage era
Overview of quantum computing and its future
How can DDOS attacks be prevented? What are the hazards?
Why is having several programming languages important?
Importance of usability in human-computer interactions

Some Interesting Topics in Computer Science You Might Like

Connection between human perception and virtual reality
The future of computer-assisted education
High-dimensional data modeling and computer science
Use of artificial intelligence and blockchain for algorithmic regulations
Computer science: Declarative versus imperative languages
Discuss blockchain technology and the banking industry
Parallel computing and languages- Discuss
Use of mesh generation in computational domains
How can a persistent data structure be optimized?
Effects of machine architecture on the coding efficiency
What is phishing and how can it be eliminated?
Overview of software security
The most efficient protocols for cryptography
Effects of computational thinking on science
Network economics and game theory
Systems programming languages development
Computer graphics development
Cyber-physical system versus sensor networks
Non-photorealistic rendering case in computer science
Programming language and floating-point

Interesting Computer Science Research Topics for Undergraduates

Can computers understand natural and human language?
How relevant is HTML5 technology today?
Role of computers in the development of operations research
What is the Internet of Things? How does it impact life?
Can AI diagnosis systems be an alternative to doctors?
Benefits of VOIP phone systems
How data mining can help in fighting crime
Advantages and disadvantages of open-source software
Advanced web design technology and how it benefits visually impaired persons
Applications and roles of artificial intelligence
Application of micro-chips in pet security
Application of the computer science knowledge to explain time travel
Computer gaming and virtual reality
Advantages and disadvantages of blockchain technology
Analyze ATMs and advanced bank security
Advantages and disadvantages of biometric systems
How to improve human-computer interactions
Advancement and evolution of torrents in the data sharing field
Quality elements in digital forensics
Relationship between computer games and physics
Discuss the principles of computer programs and programming
What is ethical hacking? Discuss its importance.
Discuss advanced computer programs and programming systems
Importance of big data analysis for an established business
Neutral networks and deep learning
Fate of robotics, computers, and computing in the next x years

Controversial Research/Project Topics in Computer Science

Long-term effects of sustained computer usage
Effects of growing up in a computer-driven world?
Discuss (with a relevant example) a privacy-centric operating system
Potential threats of the new computer viruses
How does virtual reality impact human perception? What are the pros and cons?
Challenges facing data security
Over-reliance on computers has made people less social
Online medicine applications cannot substitute real doctors. Discuss
Discuss the future of the 5G wireless systems
How computer science facilitates gene editing
Discuss why log in sites should not request users for personal data
Do eye biometrics cause cancer?
Effects of computing on critical thinking
Are computers causing more harm than good today?
Should elementary school children use computer systems for study?
Differences between functional and imperative programming
Philosophical controversies in computer engineering
Effects of solid encryption on system security
Does phishing amount to unlawful/unethical discrimination?
Effects of the ‘big data’ on people’s privacy

Research Topics in Computer Science for PhD’s

Ethical issues surrounding the use of big data banks to store human DNA
Can computer application lead to human worker obsolescence?
Application of computer science to solve health problems
The future of quantum computers
Computer viruses and associated risks/hazards
Application of robotics and artificial intelligence in enhancing human capabilities
Application of latest computing technologies in education
Business process modeling technology
Big data analytics
The working principle of machine learning and pattern recognition
Using machine learning to analyse medical images
Distributed computing and algorithms
Audio, language, and speech processing
Computer security and forensics
Communication and computation limits
Environments and programming languages
Computer systems security and support for the digital democracy

265+ Project Management Research Topics for College Students in Kenya

Explore this list of 265+ project management research topics for college students in Kenya to get the perfect topic for your next project

270+ Management Research Topics for College Students in Kenya

ContentsCyber Security Research Project Ideas for College StudentsInteresting Computer Science Design Project Ideas for FinalistsComputer Science Project Ideas for Forward Thinking StudentsGenius Computer Science Project…

320+ Excellent Sociology Research Topics for College Students in Kenya

330+ Ethical Research Topics for College Students in Kenya

The need for ethics in research is critical to the integrity of the research process. The basic principles of ethics provide a framework

nursing research topics for college students in kenya

290+ Nursing Research Topics & Project Ideas for College Students in Kenya

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Research Opportunities

Undergraduate research in computer science.

For specific information on undergraduate research opportunities in Computer Science visit https://csadvising.seas.harvard.edu/research/ .

General Information about Undergraduate Research

Opportunities for undergraduates to conduct research in engineering, the applied sciences, and in related fields abound at Harvard. As part of your coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to take part in or participate in some extraordinary projects covering topics ranging from bioengineering to cryptography to environmental engineering.

Our dedicated undergraduate research facilities and Active Learning Labs also provide opportunities for students to engage in hands-on learning. We encourage undergraduates from all relevant concentrations to tackle projects during the academic year and/or over the summer.

Keep in mind, many students also pursue summer research at private companies and labs as well as at government institutions like the National Institutes of Health.

If you have any questions, please contact or stop by the Office of Academic Programs, located in the Science and Engineering Complex, Room 1.101, in Allston.

Research FAQs

The SEAS website has a wealth of information on the variety of cross-disciplinary research taking place at SEAS. You can view the concentrations available at SEAS here , as well as the research areas that faculty in these concentrations participate in. Note that many research areas span multiple disciplines; participating in undergraduate research is an excellent way to expand what you learn beyond the content of the courses in your concentration!

To view which specific faculty conduct research in each area, check out the All Research Areas section of the website. You can also find a helpful visualization tool to show you the research interests of all the faculty at SEAS, or you can filter the faculty directory by specific research interests. Many faculty’s directory entry will have a link to their lab’s website, where you can explore the various research projects going on in their lab.

The Centers & Initiatives page shows the many Harvard research centers that SEAS faculty are members of (some based at SEAS, some based in other departments at Harvard).

Beyond the website, there are plenty of research seminars and colloquia happening all year long that you can attend to help you figure out what exactly you are interested in. Keep an eye on the calendar at https://events.seas.harvard.edu !

There are several events that are designed specifically for helping undergraduate students get involved with research at SEAS, such as the Undergraduate Research Open House and Research Lightning Talks . This event runs every fall in early November and is a great opportunity to talk to representatives from research labs all over SEAS. You can find recordings from last year’s Open House on the SEAS Undergraduate Research Canvas site .

Most of our faculty have indicated that curiosity, professionalism, commitment and an open mind are paramount. Good communication skills, in particular those that align with being professional are critical. These skills include communicating early with your mentor if you are going to be late to or miss a meeting, or reaching out for help if you are struggling to figure something out. Good writing skills and math (calculus in particular) are usually helpful, and if you have programming experience that may be a plus for many groups. So try to take your math and programming courses early (first year) including at least one introductory concentration class, as those would also add to your repertoire of useful skills.

Adapted from the Life Sciences Research FAQs

Start by introducing yourself and the purpose of your inquiry (e.g. you’d like to speak about summer research opportunities in their lab). Next, mention specific aspects of their research and state why they interest you (this requires some background research on your part). Your introduction will be stronger if you convey not only some knowledge of the lab’s scientific goals, but also a genuine interest in their research area and technical approaches.

In the next paragraph tell them about yourself, what your goals are and why you want to do research with their group. Describe previous research experience (if you have any). Previous experience is, of course, not required for joining many research groups, but it can be helpful. Many undergraduates have not had much if any previous experience; professors are looking for students who are highly motivated to learn, curious and dependable.

Finally, give a timeline of your expected start date, how many hours per week you can devote during the academic term, as well as your summer plans.

Most faculty will respond to your email if it is clear that you are genuinely interested in their research and have not simply sent out a generic email. If you don’t receive a response within 7-10 days, don’t be afraid to follow up with another email. Faculty are often busy and receive a lot of emails, so be patient.

There are several ways that undergraduate research can be funded at SEAS. The Program for Research in Science and Engineering ( PRISE ) is a 10-week summer program that provides housing in addition to a stipend for summer research. The Harvard College Research Program ( HCRP ) is available during the academic year as well as the summer. The Harvard University Center for the Environment ( HUCE ) has a summer undergraduate research program. The Harvard College Office of Undergraduate Research and Fellowships ( URAF ) has more information on these, as well as many other programs.

Students that were granted Federal Work Study as part of their financial aid package can use their Work Study award to conduct undergraduate research as well (research positions should note that they are work-study eligible to utilize this funding source).

Research labs may have funding available to pay students directly, though we encourage you to seek out one of the many funding options available above first.

Yes! Some students choose to do research for course credit instead of for a stipend. To do so for a SEAS concentrations, students must enroll in one of the courses below and submit the relevant Project Application Form on the Course’s Canvas Page:

Applied Mathematics 91r (Supervised Reading and Research)
Computer Science 91r (Supervised Reading and Research)
Engineering Sciences 91r (Supervised Reading and Research)

In general, you should expect to spend a minimum of one semester or one summer working on a project. There are many benefits to spending a longer period of time dedicated to a project. It’s important to have a conversation early with your research PI (“Principal Investigator”, the faculty who runs your research lab or program) to discuss the intended timeline of the first phase of your project, and there will be many additional opportunities to discuss how it could be extended beyond that.

For students who are satisfied with their research experience, remaining in one lab for the duration of their undergraduate careers can have significant benefits. Students who spend two or three years in the same lab often find that they have become fully integrated members of the research group. In addition, the continuity of spending several years in one lab group often allows students to develop a high level of technical expertise that permits them to work on more sophisticated projects and perhaps produce more significant results, which can also lead to a very successful senior thesis or capstone design project.

However, there is not an obligation to commit to a single lab over your time at Harvard, and there are many reasons you may consider a change:

your academic interests or concentration may have changed and thus the lab project is no longer appropriate
you would like to study abroad (note that there is no additional cost in tuition for the term-time study abroad and Harvard has many fellowships for summer study abroad programs)
your mentor may have moved on and there is no one in the lab to direct your project (it is not unusual for a postdoctoral fellow who is co-mentoring student to move as they secure a faculty position elsewhere)
the project may not be working and the lab hasn’t offered an alternative
or there may be personal reasons for leaving. It is acceptable to move on

If you do encounter difficulties, but you strongly prefer to remain in the lab, get help. Talk to your PI or research mentor, your faculty advisor or concentration advisor, or reach out to [email protected] for advice. The PI may not be aware of the problem and bringing it to their attention may be all that is necessary to resolve it.

Accepting an undergraduate into a research group and providing training for them is a very resource-intensive proposition for a lab, both in terms of the time commitment required from the lab mentors as well as the cost of laboratory supplies, reagents, computational time, etc. It is incumbent upon students to recognize and respect this investment.

One way for you to acknowledge the lab’s investment is to show that you appreciate the time that your mentors set aside from their own experiments to teach you. For example, try to be meticulous about letting your mentor know well in advance when you are unable to come to the lab as scheduled, or if you are having a hard time making progress.
On the other hand, showing up in the lab at a time that is not on your regular schedule and expecting that your mentor will be available to work with you is unrealistic because they may be in the middle of an experiment that cannot be interrupted for several hours.
In addition to adhering to your lab schedule, show you respect the time that your mentor is devoting to you by putting forth a sincere effort when you are in the lab. This includes turning off your phone, ignoring text messages, avoiding surfing the web and chatting with your friends in the lab etc. You will derive more benefit from a good relationship with your lab both in terms of your achievements in research and future interactions with the PI if you demonstrate a sincere commitment to them.
There will be “crunch” times, maybe even whole weeks, when you will be unable to work in the lab as many hours as you normally would because of midterms, finals, paper deadlines, illness or school vacations. This is fine and not unusual for students, but remember to let your mentor know in advance when you anticipate absences. Disappearing from the lab for days without communicating with your mentor is not acceptable. Your lab mentor and PI are much more likely to be understanding about schedule changes if you keep the lines of communication open but they may be less charitable if you simply disappear for days or weeks at a time. From our conversations with students, we have learned that maintaining good communication and a strong relationship with the lab mentor and/or PI correlates well with an undergraduate’s satisfaction and success in the laboratory.
Perhaps the best way for you to demonstrate your appreciation of the lab’s commitment is to approach your project with genuine interest and intellectual curiosity. Regardless of how limited your time in the lab may be, especially for first-years and sophomores, it is crucial to convey a sincere sense of engagement with your project and the lab’s research goals. You want to avoid giving the impression that you are there merely to fulfill a degree requirement or as a prerequisite for a post-graduate program.

There are lots of ways to open a conversation around how to get involved with research.

For pre-concentrators: Talk to a student who has done research. The Peer Concentration Advisor (PCA) teams for Applied Math , Computer Science and Engineering mention research in their bios and would love to talk about their experience. Each PCA team has a link to Find My PCA which allows you to be matched with a PCA based on an interest area such as research.
For SEAS concentrators: Start a conversation with your ADUS, DUS, or faculty advisor about faculty that you are interested in working with. If you don’t have a list already, start with faculty whose courses you have taken or faculty in your concentration area. You may also find it helpful to talk with graduate student TFs in your courses about the work they are doing, as well as folks in the Active Learning Labs, as they have supported many students working on research and final thesis projects.
For all students: Attend a SEAS Research Open House event to be connected with lab representatives that are either graduate students, postdocs, researchers or the PI for the labs. If you can’t attend the event, contact information is also listed on the Undergraduate Research Canvas page for follow-up in the month after the event is hosted.

For any student who feels like they need more support to start the process, please reach out to [email protected] so someone from the SEAS Taskforce for Undergraduate Research can help you explore existing resources on the Undergraduate Research Canvas page . We especially encourage first-generation and students from underrepresented backgrounds to reach out if you have any questions.

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155 Final Year Project Ideas For Computer Science Students

Are you a computer science student about to embark on your final year project journey? If so, you’re in for an exciting and challenging ride! Your final year project is a chance to apply what you’ve learned throughout your academic journey and showcase your skills to potential employers. To help you get started, we’ve compiled a list of 155 final year project ideas for computer science students, presented in the simplest language possible.

150+ Final Year Project Ideas For Computer Science Students

Table of Contents

Web Development Projects:

E-commerce Website : Create an online store with features like product catalog, shopping cart, and secure payment processing.
Content Management System (CMS) : Build a user-friendly platform for managing website content.
Blog Platform : Develop a blogging website with user profiles, comments, and likes.
Event Management System : Design a system to manage and promote events.
Portfolio Website : Create a website to showcase your own work and achievements.

Mobile App Development:

Expense Tracker : Build an app to help users manage their finances.
To-Do List App : Create a task management app with priority levels and reminders.
Recipe App : Develop an app for sharing and discovering recipes.
Fitness Tracker : Build an app to track workouts and nutrition.
Weather App : Create an app that provides real-time weather forecasts.

Data Analysis and Machine Learning:

Stock Market Predictor : Use historical data to predict stock prices.
Sentiment Analysis : Analyze social media data to gauge public sentiment on a topic.
Recommendation System : Build a system that suggests products or content based on user behavior.
Healthcare Analytics : Analyze medical data to identify trends and improve patient care.
Image Recognition : Develop an image recognition system for objects or faces.

Networking and Security:

Network Monitoring Tool : Create a tool to monitor network traffic and detect anomalies.
Intrusion Detection System (IDS) : Build a system to identify and respond to network intrusions.
Secure Messaging App : Develop an encrypted messaging app for privacy-conscious users.
Firewall Management : Create a firewall management tool with user-friendly controls.
Password Manager : Build a secure password manager for storing and generating strong passwords.

Artificial Intelligence and Robotics:

Chatbot : Create a chatbot that can answer user questions and engage in conversations.
Autonomous Drone : Build a drone that can navigate and perform tasks autonomously.
Gesture Recognition : Develop a system that recognizes hand gestures for controlling devices.
AI-Based Game : Create a computer game with intelligent non-player characters (NPCs).
Natural Language Processing (NLP) : Work on an NLP project like language translation or sentiment analysis.

Database Projects:

Online Library System : Design a database system for managing library resources.
Inventory Management : Create a database for tracking product inventory in a store.
Student Information System : Develop a system for managing student records and grades.
Hospital Management : Build a database system for hospital patient records and appointments.
E-Voting System : Create an electronic voting system with secure database management.

Web Security:

Cross-Site Scripting (XSS) Prevention : Develop a tool or technique to prevent XSS attacks on websites.
SQL Injection Prevention : Create a system to protect databases from SQL injection attacks.
Firewall Rules Analyzer : Build a tool that analyzes firewall rules for vulnerabilities.
Secure Authentication : Work on improving user authentication methods for websites.
Data Encryption : Develop a system for encrypting and decrypting sensitive data.

Augmented and Virtual Reality (AR/VR):

AR Navigation App : Create an app that provides augmented reality navigation instructions.
VR Game : Develop a virtual reality game or experience.
Architectural Visualization : Design an AR/VR tool for visualizing architectural plans.
Education in VR : Build an educational VR application for immersive learning.
Medical Training Simulations : Create medical training simulations using AR/VR.

Internet of Things (IoT):

Smart Home Automation : Build a system to control home appliances remotely.
IoT-based Health Monitoring : Develop a device for monitoring vital signs and sending alerts.
Smart Agriculture : Create a system for monitoring and controlling farm conditions.
Traffic Management : Build a smart traffic management system using IoT devices.
Environmental Monitoring : Create IoT sensors for monitoring air quality, water quality, etc.

Software Development Tools:

Code Editor : Create a code editor with features like syntax highlighting and auto-completion.
Version Control System : Build a version control system like Git.
Bug Tracking System : Develop a tool for tracking and managing software bugs.
Continuous Integration (CI) Pipeline : Design a CI/CD pipeline for automated software testing and deployment.
IDE for a Specific Language : Create an integrated development environment (IDE) for a specific programming language.

Blockchain:

Cryptocurrency Wallet : Build a digital wallet for managing cryptocurrencies.
Supply Chain Tracking : Create a blockchain-based system for tracking the supply chain.
Blockchain Voting System : Develop a secure online voting system using blockchain technology.
Smart Contracts : Work on smart contracts for automating transactions.
Blockchain-Based Authentication : Build a secure authentication system using blockchain.

Natural Language Processing (NLP):

Language Translation Tool : Create a tool that translates text between languages.
Chatbot for Customer Support : Develop an NLP-based chatbot for customer service.
Text Summarization : Build a system that summarizes long texts or articles.
Named Entity Recognition : Create a tool that identifies names, dates, and other entities in text.
Speech Recognition : Work on a speech recognition system for converting spoken language into text.

Game Development:

2D Platformer Game : Create a classic 2D platformer game with levels and challenges.
RPG Game : Develop a role-playing game with quests, characters, and a storyline.
Multiplayer Online Game : Build a multiplayer game that can be played over the internet.
VR Game : As mentioned earlier, create a virtual reality game.
Augmented Reality Game : Design an AR game that combines the real world with virtual elements.

Robotics and Automation:

Robotic Arm Control : Build a system for controlling a robotic arm for various tasks.
Autonomous Robot : Create a robot that can navigate and perform tasks autonomously.
Voice-Controlled Robot : Develop a robot that responds to voice commands.
AI-Powered Robot : Work on a robot that can learn and adapt to different environments.
Robotic Vacuum Cleaner : Build a robotic vacuum cleaner with obstacle avoidance.

Cloud Computing:

Cloud-Based File Storage : Create a secure file storage system in the cloud.
Serverless Computing : Develop applications using serverless computing platforms like – .
Cloud-Based Machine Learning : Implement machine learning models in the cloud for scalability.
Distributed Systems : Work on projects that involve distributed computing and data processing.
Cloud Security : Develop tools or techniques for enhancing cloud security.

Cybersecurity:

Vulnerability Scanner : Create a tool that scans networks or websites for vulnerabilities.
Password Cracking Detection : Build a system to detect and prevent password cracking attempts.
Phishing Detection : Develop a phishing detection system for emails and websites.
Network Traffic Analysis : Analyze network traffic for signs of malicious activity.
Malware Detection : Create a system that identifies and removes malware from systems.

Computer Vision:

Facial Recognition System : Build a system that recognizes faces for security or authentication.
Object Detection : Create a system that can identify and locate objects within images or videos.
Traffic Sign Recognition : Develop a system that recognizes and interprets traffic signs.
Gesture Recognition : As mentioned earlier, work on gesture recognition for human-computer interaction.
Medical Image Analysis : Analyze medical images like X-rays or MRIs for diagnosis.

Data Visualization:

Interactive Dashboard : Create an interactive dashboard for visualizing data.
Geospatial Data Visualization : Visualize geographic data on maps.
Real-time Data Visualization : Develop a system that updates data visualizations in real time.
Stock Market Data Visualization : Visualize stock market trends and data.
Healthcare Data Visualization : Visualize healthcare data for better decision-making.

Social Media and Networking:

Social Media Analytics : Analyze social media data to gain insights into user behavior.
Friend Recommendation System : Build a system that suggests friends or connections on social networks.
Social Media Sentiment Analysis : Analyze sentiment on social media platforms.
Online Dating Platform : Create a platform for online dating with matching algorithms.
Social Networking App : Develop a new social networking app with unique features.

Human-Computer Interaction (HCI):

User Interface Design : Work on improving the user interfaces of existing software.
Voice User Interface (VUI) : Create a voice-controlled interface for a software application.
Gestural User Interface : Develop a user interface that responds to gestures.
Accessibility Tools : Build tools to make software more accessible to people with disabilities.
Virtual Reality User Interface : Design a user interface for VR applications.
Big Data Analytics : Analyze large datasets to extract valuable insights.
Real-time Data Processing : Develop systems for processing real-time data streams.
Data Warehousing : Create a data warehousing solution for storing and retrieving data.
Big Data Visualization : Visualize big data in meaningful ways.
Predictive Analytics : Use big data to build predictive models for various applications.

Internet Security:

Secure File Transfer : Develop a secure file transfer protocol or application.
Email Encryption : Create a system for encrypting email communications.
Identity Verification : Build a system for secure online identity verification.
Secure Online Payments : Work on enhancing the security of online payment systems.
Network Security Audit : Develop tools for conducting security audits on computer networks.

Mobile Security:

Mobile App Security Scanner : Create a tool to scan mobile apps for security vulnerabilities.
Anti-Malware App : Develop a mobile app that detects and removes malware.
Mobile Payment Security : Enhance the security of mobile payment apps.
Secure Messaging App : As mentioned earlier, build a secure messaging app.
Mobile Device Tracker : Create a tool for tracking and recovering lost or stolen mobile devices.

Software Testing:

Automated Testing Framework : Develop a framework for automated software testing.
Load Testing Tool : Create a tool for simulating heavy user loads on web applications.
Code Coverage Analyzer : Build a tool to measure code coverage during testing.
Bug Reporting System : Design a system for efficient bug reporting and tracking.
Test Data Generation : Develop a tool for generating test data.
2D Game Engine : Create a game engine for developing 2D games.
Physics Engine : Build a physics engine for realistic game physics.
Game Level Design Tool : Develop a tool for designing game levels and environments.
Multiplayer Game Server : Create a server for hosting multiplayer games.
Game AI Framework : Design a framework for implementing game AI.
Serverless API : Build a serverless API for deploying and managing APIs.
Container Orchestration : Develop a system for orchestrating containers in the cloud.
Cloud Cost Management : Create tools for monitoring and managing cloud infrastructure costs.
Serverless Data Processing : Implement data processing workflows using serverless architecture.
Cloud-based IoT : Build an IoT platform that leverages cloud services.
IoT-Based Home Automation : Create a system to control home appliances and security using IoT.
Smart City Solutions : Develop IoT solutions for enhancing urban living.
IoT in Agriculture : Create IoT devices and systems for precision agriculture.
Industrial IoT : Build IoT solutions for monitoring and optimizing industrial processes.
IoT-Based Healthcare : Develop healthcare devices and systems using IoT.
Text Generation : Create a system that generates human-like text based on input data.
Language Translation : Work on improving machine translation systems.
Chatbots for Specific Domains : Develop chatbots tailored to specific industries or topics.
Speech-to-Text and Text-to-Speech : Build systems for converting spoken language to text and vice versa.
Emotion Recognition in Text : Create a system that can detect emotions in written text.

Artificial Intelligence (AI):

AI-Powered Personal Assistant : Develop a personal assistant like Siri or Alexa.
AI-Based Game Opponents : Create intelligent AI opponents for computer games.
AI in Healthcare : Build AI systems for diagnosing diseases or suggesting treatments.
AI in Education : Create AI-based educational tools and platforms.
AI in Finance : Work on AI applications in the financial industry.
Robotic Arm for Surgery : Develop a robotic system for assisting surgeons.
Autonomous Delivery Robot : Create a robot for delivering packages autonomously.
Robotic Pet Companion : Build a robot designed to provide companionship to users.
Robotic Exoskeleton : Develop an exoskeleton for assisting people with mobility challenges.
Autonomous Cleaning Robot : Create a robot for cleaning and maintaining spaces.
Blockchain-Based Supply Chain : Develop a blockchain solution for supply chain transparency.
Blockchain Identity Verification : Create a system for secure identity verification using blockchain.
Blockchain-Based Voting System : As mentioned earlier, work on a blockchain-based voting system.
Blockchain for Intellectual Property : Use blockchain for protecting intellectual property rights.
Blockchain in Education : Implement blockchain solutions for verifying educational credentials.

That’s quite a list of project ideas for computer science students! Remember, the key to a successful final year project is to choose something that genuinely interests you and aligns with your skills and career goals. So, take your time to explore these ideas, consult with your professors, and select a project that excites you. Good luck with your final year project, and may you succeed in your computer science journey!

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Research project ideas for Computer Science students

Sometimes Computer Science students struggle to find a final year research topic. In this post, you will find some ideas that can help you define a topic you can develop for your final year research project.

Once you choose your project, it is time to write your research proposal. I’ll leave some links at the end of this post that will guide you on how to do it.

1. mobile app for class notes, 2. graphic simulator of programming structures and basic algorithms., 3. augmented reality app to support the learning of oop concepts., 4. augmented reality app to translate uml to a code, 5. moodle reports dashboard.

This app won’t be just another note-taking app. In this case, you can build it with specific requirements to facilitate the students learning process.

Some of the requirements can be:

Instructors can load a course to the app. This will include the course outline and notes.
Students can subscribe to a certain course. Then, they will have access to all the information uploaded by the instructor.
Students can take their own notes and decide whether to share them or not.
The notes should be easily discovered and grouped by topic and/or unit.

Main advantage: Students will be able to use the accumulated experience (by instructors and other students) in a certain subject.

This topic will result in a progressive web application that can show, graphically, what exactly is happening while the computer executes the following:

Conditionals
Basic algorithms: counting, summing, maximum
Call to methods

It is well accepted that graphic representations help students to learn better. Also, there is one skill that is especially difficult for students named tracing.

Tracing is about finding out what will be the output of a given code. Understanding how the basic algorithms and programming structures work graphically will help students to grasp this skill.

This will be a cool app the students will just love.

The requirements will be the following:

– Once you point the camera to an object in the real world, the app first should identify the object.

– The app will give options to show attributes of the object (color, size, etc.)

– Show available actions to the object.

– Be able to execute some of the actions.

– Show a UML class diagram for the class, showing attributes and methods.

The requirements will be as follows:

– Point the camera to a UML diagram.

– Show options for different programming languages.

– Show the code of the class in a specific programming language.

– Output the code to a file.

– Give options to save the file: upload to an FTP server, save locally, upload to a git repository, cloud integration, etc.

Moodle is a well-known Learning Management System (LMS). It includes a list of useful reports, although some of them do not have the best presentation design.

This application can be developed as Moodle plugin. In this link, you can find a tutorial on how to develop a plugin for Moodle.

Some requirements that can be implemented are the following:

– Dashboard with user-defined Key Performance Indicators (KPI).

– Choose what type of graphs to show on the dashboard.

– Options to show any of the reports that Moodle already provides integrated on the dashboard. See the picture below.

– Options to track the performance of students with average marks within a certain range. This can help the lecturer to give special attention to students with difficulties.

– Additional reports of interest. To define which reports, the researcher should conduct interviews/questionnaires with lecturers and HoDs to find out what type of report will be useful for each of them. From these artefacts, the researcher will gain insights on how to better build the dashboard and include initial KPIs.

And that’s all for now.

I’ll keep updating this list regularly. So, if these ones don’t fit your preferences, come back again in a while. You are most welcome to leave a comment below.

How to write the background of the study for a research proposal?
How to write a problem statement for a research proposal?
How to write research objectives for a research proposal?
How to write the research methodology?
How to write a literature review?
How to write your research proposal?
How to write an abstract for your research paper, proposal, or dissertation?

Email address

2020-2021 SURE Research Projects in CSE

This page lists summer research opportunities in CSE that are available through the SURE Program. To learn more or apply, visit: https://sure.engin.umich.edu/ .

Please carefully consider each of the following projects, listed below, before applying to the SURE Program.
You must indicate your top three project choices on your SURE application, in order of preference, using the associated CSE project number.
Questions regarding specific projects can be directed to the listed faculty mentor.

Project descriptions

CSE Project #1: Natural Language Processing for Understanding Media Bias and Fake News Faculty Mentor: Lu Wang [wangluxy @ umich.edu] Prerequisites: EECS 445 (Machine Learning), probability and statistics, experience with natural language processing problems, proficient in Python. Description: News media play a vast role not just in supplying information, but in selecting, crafting, and biasing that information to achieve both nonpartisan and partisan goals. We aim to automate media bias detection from news articles, and quantify and further highlight biased content in order to promote the transparency of news production as well as enhance readers’ awareness of media bias. This project will explore and design natural language processing and machine learning algorithms to detect media bias. Specifically, we will work on developing information extraction systems, e.g., important entities and narrative structure will be extracted automatically from news articles. The developed tools will also be used for understanding fake news. Expected research delivery mode: Hybrid

CSE Project #2: Computational Strategic Reasoning Faculty Mentor: Michael Wellman [wellman @ umich.edu] Prerequisites: Programming ability; interest/background in finance, economics, game theory, and/or statistics (helpful though not required). Description: The Strategic Reasoning Group (strategicreasoning.org) develops computational tools to support reasoning about complex strategic environments. Recent applications include scenarios arising in finance and cyber-security. We employ techniques from agent-based modeling, game theory, and machine learning. Expected research delivery mode: Too soon to say

CSE Project #3: Taming the Performance Bottlenecks of Modern Web Applications Faculty Mentor: Baris Kasikci [barisk @ umich.edu] Prerequisites: EECS 482 Description: Modern data-center applications suffer significant slow-down due to large number instruction cache-misses. To reduce such cache-misses, recent studies have advocated the introduction of a new code prefetch instruction. While warehouse-scale processors do not support this feature yet, some mobile processors already support this code prefetch instruction. In this study, we will design a compiler backend to inject code prefetch instruction both statically and based on profile data in order to evaluate several data-center applications on mobile such processors. Expected research delivery mode: Too soon to say

CSE Project #4: Web automation using program synthesis Faculty Mentor: Xinyu Wang [xwangsd @ umich.edu] Prerequisites: EECS 485 or equivalent, and familiarity with HTML/DOM/JS Description: Many computer end-users often need to perform tasks that involve the web, such as filling online forms, extracting data, which are repetitive and tedious in nature. On the other hand, there are existing programming languages that can be used to automate these tasks. However, writing web automation scripts is far beyond the capability of end-users who have very little programming background. In this project, we aim to help users automate web-related programming tasks using program synthesis. Expected research delivery mode: Too soon to say

CSE Project #5: Interactive program synthesis Faculty Mentor: Xinyu Wang [xwangsd @ umich.edu] Prerequisites: Familiarity with one programming language. Description: Program synthesis aims to automatically generate programs from user intent expressed in some high-level format (such as input-output examples). It has found a lot of applications, for instance, in data science, software development, etc. While there has been a lot of algorithmic advancements in program synthesis techniques, it is still unclear what is the best way for synthesizers to interact with users. In this project, we will explore how to design interactive program synthesis algorithms as well as good user interfaces for these techniques. Expected research delivery mode: Too soon to say

CSE Project #6: Superoptimization using program synthesis Faculty Mentor: Xinyu Wang [xwangsd @ umich.edu] Prerequisites: Compilers, strong programming and engineering background. Description: The goal of superoptimization is to automatically derive compiler optimizations. It automatically searches among a space of optimizations and apply those that can be applied for the input program. The advantage of superoptimization is that it can dramatically reduce human effort and at the same time potentially generate better optimizations. In this project, we will look at how to use program synthesis and program analysis to automatically derive better optimizations more efficiently, compared to prior superoptimization techniques. Expected research delivery mode: Too soon to say

CSE Project #7: Censored Planet: A Global Observatory for Internet Censorship Faculty Mentor: Roya Ensafi [ensafi @ umich.edu] Prerequisites: EECS 388 and EECS 482 Description: The Internet Freedom community’s understanding of the current state and global scope of censorship remains limited: most work to-date has focused on the practices of particular networks and countries, or on the reachability of small sets of online services and from a small number of volunteers. Creating a global, data-driven view of censorship is a challenging proposition, since censorship practices are intentionally opaque, and there are a host of mechanisms and locations where disruptions can occur. Moreover, the behavior of the network can vary depending on who is requesting content from which location.

Fall 2018, Prof. Ensafi launched a pilot of Censored Planet, an online observatory for Internet censorship that applies all of next-generation measurement techniques in order to rapidly, continuously, and globally track online censorship. Data from the pilot has already been used by dozens of organizations, and it has helped provide insight into important events like Saudi Arabia’s reaction to the death of Jamal Khashoggi, the proliferation of DPI-based censorship products, and recent HTTPS interception attacks sponsored by the government of Kazakhstan.

We seek to extend and fully operationalize Censored Planet and make data from next-generation remote censorship measurements more useful to the entire Internet Freedom community. We plan to mature the project from a pilot to a production system with significant improvements in performance, stability, usability, and code quality; implement an API and new “rapid focus” capabilities to agily respond to world events; and develop aggregation and analysis tools to automatically extract useful insights from that data. We will also cultivate a community of civil society organizations and tool developers to ensure the data best serves real-world needs.

By helping create a more complete picture of global censorship than ever before, Censored Planet will allow researchers and policymakers to closely monitor for deployment of censorship technologies, track policy changes in censoring nations, and better understand the targets of interference. Making opaque censorship practices more transparent at a global scale will help counter the proliferation of these growing restrictions to online freedom. Expected research delivery mode: Remote

CSE Project #8: Supporting K-5 Children Learning While Using the Collabrify Roadmap Platform Faculty Mentor: Elliot Soloway [soloway @ umich.edu] Prerequisites: Competency in Javascript, databases, interfaces. Description: The Center for Digital Curricula in the College of Engineering provides deeply-digital curricula, standards-aligned to K-5 classrooms – free. During the fall 2020 semester, over 5,000 K-5 students are using the Center’s curricula on a daily basis. Students use the Collabrify Roadmap Platform to enact the digital curricula. Teachers and students request changes to the Platform; and researchers see opportunities to make the Platform still more effective. During the summer, then, the Center is seeking two ugrads to work on projects to implement the requested changes to the Platform. Join us in helping children to learn more effectively! Expected research delivery mode: Hybrid

CSE Project #9: Computer Vision for Physical and Functional Understanding Faculty Mentor: David Fouhey [fouhey @ umich.edu] Prerequisites: Good grades in EECS 442 OR EECS 445. Description: The lab is broadly focused on building 3D representations of the world and understanding human/object interaction. Potential projects include learning about: navigating environments, object articulations, commonsense physical properties of objects, and hand grasps. Please look at:http://web.eecs.umich.edu/~fouhey/ for a sense of what projects we’ve done in the past. We will find a specific project based on mutual interest and particular abilities (e.g., stronger systems programming abilities, experience with graphics, etc.). Students looking for a longer term project continuing during the school year are strongly encouraged to apply. Expected research delivery mode: Too soon to say

CSE Project #10: Does Wealth Matter? Learning Generative Models with Prediction Markets Faculty Mentor: Mithun Chakraborty and Sindhu Kutty [skutty @ umich.edu] Prerequisites: EECS 445 and STATS 412 (or equivalents) preferred. Description: As recent events have highlighted, polling can be messy, misleading and prone to misinterpretation. Markets have the advantage over polls in having built-in financial incentives and timely responses, and have been empirically observed to outperform alternative forecasting tools such as polls. However, when traders have varying degrees of wealth, are markets egalitarian? Moreover, how precise are they and what factors impact their precision? We will answer these questions in the context of Prediction Markets by tying market prices to learning a generative model of the outcome space. We will also explore other connections between convergence in Machine Learning algorithms (especially Bayesian processes) and equilibria in these markets.

Prediction markets (e.g. Iowa Electronic Markets, PredictIt, etc.) are a type of financial market the purpose of which is to elicit the personal beliefs of traders about a future uncertain event and aggregate these beliefs into the market price. In this project, students will implement and execute a set of experiments on the interaction of a new prediction market design with simulated trading agents having diverse risk attitudes and help address the above research questions in different environments in a systematic manner. An understanding of connections to Machine Learning algorithms would be illustrative for gauging the accuracy, and hence reliability, of Prediction Markets and can, in turn, inform innovations in their design. The learning outcome for students will be hands-on experience in interdisciplinary research with connections to Machine Learning and Computational Economics. Expected research delivery mode: Remote

CSE Project #11: Hazel Notebooks: Building a Better Jupyter Faculty Mentor: Cyrus Omar [comar @ umich.edu] Prerequisites: EECS 490 or equivalent is preferred, but not required. Description: The popular Jupyter lab notebook environment is powerful, but it has a problem: results stored in a notebook are not reproducible, because the user can execute cells out of order. In our group, we are developing a new live functional programming environment called Hazel (hazel.org). Right now, Hazel does not support multiple program cells. This project will turn Hazel into a next-generation version of Jupyter by adding support for notebooks with multiple cells, with dependencies between them. We will solve the reproducibility problem by developing a mechanism conjectured in a recent paper in our group: fill-and-resume. Expected research delivery mode: Too soon to say

CSE Project #12: Hazel: A Live Functional Programming Environment Faculty Mentor: Cyrus Omar [comar @ umich.edu] Prerequisites: EECS 490 or equivalent is preferred, but not required. Description: Hazel (hazel.org) is a live functional programming environment that is able to typecheck, transform and even execute incomplete programs, i.e. programs with holes. There are a number of projects available within the Hazel project for a student interested in research into programming languages. Expected research delivery mode: Too soon to say

CSE Project #13: Ubiquitous Health Sensing Faculty Mentor: Alanson Sample [apsample @ umich.edu] Prerequisites: Experience with embedded systems, computer vision, or machine learning Description: Effective means of unobtrusive and continuous monitoring of one’s health could transform how we detect and treat illnesses. This project aims to create a long-range health monitoring system that can passively measure an individual’s vital signs and daily activities from a distance of up to three meters. Building off of novel sensing techniques developed in the Interactive Sensing and Computing Lab, SURE students will work with faculty and graduate student mentors to create a fully working end-to-end system, utilizing embedded systems, computer vision, and machine learning. Expected research delivery mode: Hybrid

CSE Project #14: The Internet of Everything: Bringing everyday objects into the digital world with RFID tags Faculty Mentor: Alanson Sample [apsample @ umich.edu] Prerequisites: Strong programming skills. Description: RFID tags are battery-free, paper-thin stickers that can communicate with RFID readers from +8 meters of distance. These tags offer a minimalistic means of instrumenting everyday objects. By monitoring changes in the low-level communication channel parameters between the tag and reader, it is possible to turn an RFID tag into an ultra-low-cost, battery-free sensor. Applications include in-home activity inferencing, interactive physical objects, and health and wellness monitoring. Expected research delivery mode: Too soon to say

CSE Project #15: Computer Vision for Physical and Functional Understanding Faculty Mentor: Alanson Sample [apsample @ umich.edu] Prerequisites: Preferred EECS 311 or EECS 373. Description: This project encompasses a number of efforts at developing energy harvesting, battery free sensing systems that can be easily embedded into everyday objects and thus allowing for near perpetual operation. Topics include ambient energy harvesting techniques, platform architecture and power management, and debugging tools that deal with intermittent power. Expected research delivery mode: Too soon to say

CSE Project #16: Adversarial Human-AI Interactions in the On-Demand Economy Faculty Mentor: Nikola Banovic [nbanovic @ umich.edu] Prerequisites: Familiarity with programming (i.e., Python), interest in applied machine learning and human-computer interaction. Description: AI has started to transform the nature of work in many sectors of the economy. One of the most tangible transformations has been in the on-demand economy, for services such as grocery delivery, ride-hailing, and other last-mile services, where its advances have allowed a shift towards greater efficiency, through the use of AI-mediated platforms. On-demand work, with its promises of flexibility, independence and entrepreneurship is also an attractive option for individuals seeking a low-barrier entry into employment and economic opportunities. However, several recent debates around the employment status of workers with services such as Uber, Lyft and Instacart have shined a light on the adversarial relationships between workers and platforms, and the negative effects of opaque algorithms on workers’ well-being. In this project, we seek to design computational methods to audit these opaque platforms to uncover sources of adversarial human-AI interactions that may be potentially harmful to on-demand workers. Our goal is to understand the design of algorithmic platforms that enhance worker well-being and their access to economic opportunities. Expected research delivery mode: Remote

CSE Project #17: Novel Architectures to Compute with Graphs Faculty Mentor: Valeria Bertacco [valeria @ umich.edu] Prerequisites: EECS 281, EECS 370. Recommended: C++, scripting. Description: More and more applications rely on graphs as the underlying data structure: from social networks, to internet’s web connections, to geo maps, to ML algorithms and even consumers’ product preferences. The performance of these algorithms is often limited by the latency of accessing vertices in memory, whose access present poor spatial locality. The goal of this project is to boost the performance of graph-based algorithms by developing hardware and software solutions to this end: we plan to work on the data layout, on ad-hoc data structures and on designing dedicated hardware acceleration blocks. We hope to boost the performance of graph traversals by 3-5x. Expected research delivery mode: Too soon to say

CSE Project #18: From High-Level Language to Hardware — Without the Hardware Design Faculty Mentor: Valeria Bertacco [valeria @ umich.edu] Prerequisites: EECS 281. Recommended: C++, scripting. Description: This project explores a new hardware design flow, where the starting point is an application specified in a domain-specific language (more specialized than C) like Halide or GraphIt, and the endpoint is a hardware system equipped with specialized hardware accelerators, so to execute the application much faster than it would be possible in software. To reach the endpoint, we will work on the back-end of the compiler, so to target the primitives available in the hardware accelerators. Expected research delivery mode: Too soon to say

CSE Project #19: Computing on Encrypted Data Faculty Mentor: Valeria Bertacco [valeria @ umich.edu] Prerequisites: EECS 280, EECS 370. Recommended: C++, scripting. Description: In the age of big data, privacy is a key concern in sharing data. Unfortunately, the field of security is riddled with stories of security attacks…even to the most secure enclaves. The solution we want to investigate with this project uses encryption technology to encrypt data locally, transfer it to the cloud for any required computation, and receive encrypted results back. The enhanced cloud system performs the computation directly on the encrypted data without an access key — it never accesses the plaintext data nor can it decrypt the sensitive data. Only the end device, can decrypt the result and store it locally. Expected research delivery mode: Too soon to say

Upcoming Summer 2024 Application Deadline is May 12, 2024.

Click here to apply.

25+ Research Ideas in Computer Science for High School Students

As a high school student, you may be wondering how to take your interest in computer science to the next level. One way to do so is by pursuing a research project. By conducting research in computer science, you can deepen your understanding of this field, gain valuable skills, and make a contribution to the broader community. With more colleges going test-optional, a great research project will also help you stand out in an authentic way!

Research experience can help you develop critical thinking, problem-solving, and communication skills. These skills are valuable not only in computer science but also in many other fields. Moreover, research experience can be a valuable asset when applying to college or for scholarships, as it demonstrates your intellectual curiosity and commitment to learning.

Ambitious high school students who are selected for the Lumiere Research Scholar Programs work on a research area of their interest and receive 1-1 mentorship by top Ph.D. scholars. Below, we share some of the research ideas that have been proposed by our research mentors – we hope they inspire you!

Topic 1: Generative AI

Tools such as ChatGPT, Jasper.ai, StableDiffusion and NeuralText have taken the world by storm. But this is just one major application of what AI is capable of accomplishing. These are deep learning-based models , a field of computer science that is inspired by the structure of the human brain and tries to build systems that can learn! AI is a vast field with substantial overlaps with machine learning , with multiple intersections with disciplines such as medicine, art, and other STEM subjects. You could pick any of the following topics (as an example) on which to base your research.

1. Research on how to use AI systems to create tools that augment human skills. For example, how to use AI to create detailed templates for websites, apps, and all sorts of technical and non-technical documentation

2. Research on how to create multi-modal systems. For example, use AI to create a chatbot that can allow users Q&A capabilities on the contents of a podcast series, a television show, and a very diverse range of content.

3. Research on how to use AI to create tools that can do automated checks for quality and ease of understanding for student essays and other natural language tasks. This can help students quickly improve their writing skills by improving the feedback mechanism.

4. Develop a computer vision system to monitor wildlife populations in a specific region.

5. Investigate the use of computer vision in detecting and diagnosing medical conditions from medical images.

6. Extracting fashion trends (or insert any other observable here) from public street scene data (i.e. Google Street View, dash cam datasets, etc.)

Ideas by a Lumiere Mentor from Cornell University.

Topic 2: Data Science

As a budding computer scientist, you must have studied the importance of sound, accurate data that can be used by computer systems for multiple uses. A good example of data science used in education is tools that help calculate your chances of admission to a particular college. By collecting a small amount of data from you, and by comparing it with a much larger database that has been refined and updated regularly, these tools effectively use data science to calculate acceptance rates for students in a matter of seconds.

Another area is Natural Language Processing, or NLP, for short, aims to understand and improve machines' ability to understand and interpret human language. Be it the auto-moderation of content on Reddit, or developing more helpful, intuitive chatbots, you can pick any research idea that you're interested in.

You could pick one of the following, or related questions to study, that come under the umbrella of data science.

7. Develop a predictive model to forecast traffic congestion in your city.

8. Analyze the relationship between social media usage and mental health outcomes in a specific demographic.

9. Investigate the use of data analytics in reducing energy consumption in commercial buildings.

10. Develop a chatbot that can answer questions about a specific topic or domain, such as healthcare or sports.

11. Learn the different machine learning and natural language processing methods to categorize text (e.g. Amazon reviews) as positive or negative.

12. Investigate the use of natural language processing techniques in sentiment analysis of social media data.

Ideas by a Lumiere Mentor from the University of California, Irvine.

Topic 3: Robotics

A perfect research area if you're interested in both engineering and computer science , robotics is a vast field with multiple real-world applications. Robotics as a research area is a lot more hands-on than the other topics covered in this blog, so it's a good idea to make a note of all the possible tools, guides, time, and space that you may need for the following ideas. You can also pitch some of these ideas to your school if equipped with a robotics lab so that you can conduct your research in the safety of your school, and also receive guidance from your teachers!

13. Design and build a robot that can perform a specific task, such as picking up and stacking blocks.

14. Investigate the use of robots in medicine, such as high-precision surgical robots.

15. Develop algorithms to enable a robot to navigate and interact with an unfamiliar environment.

Ideas by a Lumiere Mentor from University College London.

Topic 4: Ethics in computer science

With the rapid development of technology, ethics has become a significant area of study. Ethical principles and moral values in computer science can relate to the design, development, use, and impact of computer systems and technology. It involves analyzing the potential ethical implications of new technologies and considering how they may affect individuals, society, and the environment. Some of the key ethical issues in computer science include privacy, security, fairness, accountability, transparency, and responsibility. If this sounds interesting, you could consider the following topics:

16. Investigate fairness in machine learning. There is growing concern about the potential for machine learning algorithms to perpetuate and amplify biases in data. Research in this area could explore ways to ensure that machine learning models are fair and do not discriminate against certain groups of people.

17. Study the energy consumption and carbon footprint of machine learning can have significant environmental impacts. Research in this area could explore ways to make machine learning more energy-efficient and environmentally sustainable.

18. Conduct Privacy Impact Assessments for a variety of tools for identifying and evaluating the privacy risks associated with a particular technology or system.

Topic 5: Game Development

According to statistics, the number of gamers worldwide is expected to hit 3.32 billion by 2024. This leaves an enormous demand for innovation and research in the field of game design, an exciting field of research. You could explore the field from multiple viewpoints, such as backend game development, analysis of various games, user targeting, as well as using AI to build and improve gaming models. If you're a gamer, or someone interested in game design, pursuing ideas like the one below can be a great starting point for your research -

19. Design and build a serious game that teaches users about a specific topic, such as renewable energy or financial literacy.

20. Analyze the impact of different game mechanics on player engagement and enjoyment.

21. Develop an AI-powered game that can adjust difficulty based on player skill level.

Topic 6: Cybersecurity

According to past research, there are over 2,200 attacks each day which breaks down to nearly 1 cyberattack every 39 seconds. In a world where digital privacy is of utmost importance, research in the field of cybersecurity deals with improving security in online platforms, spotting malware and potential attacks, and protecting databases and systems from malware and cybercrime is an excellent, relevant area of research. Here are a few ideas you could explore -

22. Investigate the use of blockchain technology in enhancing cybersecurity in a specific industry or application.

23. Apply ML to solve real-world security challenges, detect malware, and build solutions to safeguard critical infrastructure.

24. Analyze the effectiveness of different biometric authentication methods in enhancing cybersecurity.

Ideas by Lumiere Mentor from Columbia University

Topic 7: Human-Computer Interaction

Human-Computer Interaction, or HCI, is a growing field in the world of research. As a high school student, tapping into the various applications of HCI-based research can be a fruitful path for further research in college. You can delve into fields such as medicine, marketing, and even design using tools developed using concepts in HCI. Here are a few research ideas that you could pick -

25. Research the use of color in user interfaces and how it affects user experience.

26. Investigate the use of machine learning in predicting and improving user satisfaction with a specific software application.

27. Develop a system to allow individuals with mobility impairments to control computers and mobile devices using eye tracking.

28. Use tools like WAVE or WebAIM to evaluate the accessibility of different websites

Topic 8: Computer Networks

Computer networks refer to the communication channels that allow multiple computers and other devices to connect and communicate with each other. An advantage of conducting research in the field of computer networks is that these networks span from local, regional, and other small-scale networks to global networks. This gives you a great amount of flexibility while scoping out your research, enabling you to study a particular region that is accessible to you and is achievable in terms of time, resources, and complexity. Here are a few ideas -

29. Investigate the use of software-defined networking in enhancing network security and performance.

30. Develop a network traffic classification system to detect and block malicious traffic.

31. Analyze the effectiveness of different network topology designs in reducing network latency and congestion.

Topic 9: Cryptography

Cryptography is the practice of secure communication in the presence of third parties or adversaries. It uses mathematical algorithms and protocols to transform plain text into a form that is unintelligible to unauthorized users - the process known as encryption.

Cryptography has grown in uses - starting from securing communication over the internet, protecting sensitive information like passwords and financial transactions, and securing digital signatures and certificates.

32. Investigating side-channel attacks that exploit weaknesses in the physical implementation of cryptographic systems.

33. Research techniques that can enable secure and private machine learning using cryptographic methods.

Additional topics:

IoT: How can networked devices help us enrich human lives?

Computational Modeling: Using CS to model and study complex systems using math, physics, and computer science. Used for everything from weather forecasts, flight simulators, earthquake prediction, etc.

Parallel and distributed systems: Research into algorithms, operating systems and computer architectures built to operate in a highly parallelized manner and take advantage of large clusters of computing devices to perform highly specialized tasks. Used in data centers, supercomputers and by all major web-scale platforms like Amazon, Google, Facebook, etc.

UI/UX Design: Research into using design to improve all kinds of applications

Social Network Analysis: Exploring social structures through network and graph theory. Was used during COVID to make apps that can alert people about potential vectors of disease – be they places, events or people.

Optimization Techniques: optimization problems are common in all engineering disciplines, as well as AI and Machine Learning. Many of the common algorithms to solve them have been inspired by natural phenomena such as foraging behavior of ants or how birds naturally seem to be able to form large swarms that don’t crash into each other. This is a rich area of research that can help with innumerable problems across the disciplines.

Experimental Design: Research into the design and implementation of experimental procedures. Used in everything from Ai and Machine learning, to medicine, sociology, and most social and natural sciences.

Autonomous vehicle: Research into technical and non-technical aspects (user adoption, driver behavior) of self-driving cars

Augmented and Artificial Reality systems: Research into integrating AR to enhance and enrich everyday human experience. Augmenting gaming or augmented learning, for example.

Customized Hardware Research: Modern applications run on customized hardware. AI systems have their own architecture; crypto, its own. Modern systems have decoders built into your CPU, and this allows for highly compressed high quality video streams to play in real-time. Customized hardware is becoming increasingly critical for next-gen applications, from both a performance and an efficiency lens.

Database Systems: Research in the algorithms, systems, and architecture of database systems to enable effective storage, retrieval and usage of data of different types (text, image, sensor, streaming, etc) and sizes (small to petabytes)

Programming languages: Research into how computing languages translate human thought into machine code, and how the design of the language can significantly modify the kind of tools and applications that can be built in that language.

Bioinformatics and Computational Biology: Research into how computational methods can be applied to biological data such as cell populations, genetic sequences, to make predictions/discovery. Interdisciplinary field involving biology, modeling and simulation, and analytical methods.

If you're looking for a real-world internship that can help boost your resume while applying to college, we recommend Ladder Internships!

Ladder Internships is a selective program equipping students with virtual internship experiences at startups and nonprofits around the world!

The startups range across a variety of industries, and each student can select which field they would most love to deep dive into. This is also a great opportunity for students to explore areas they think they might be interested in, and better understand professional career opportunities in those areas.

The startups are based all across the world, with the majority being in the United States, Asia and then Europe and the UK.

The fields include technology, machine learning and AI, finance, environmental science and sustainability, business and marketing, healthcare and medicine, media and journalism and more.

You can explore all the options here on their application form . As part of their internship, each student will work on a real-world project that is of genuine need to the startup they are working with, and present their work at the end of their internship. In addition to working closely with their manager from the startup, each intern will also work with a Ladder Coach throughout their internship - the Ladder Coach serves as a second mentor and a sounding board, guiding you through the internship and helping you navigate the startup environment.

Cost : $1490 (Financial Aid Available)

Location: Remote! You can work from anywhere in the world.

Application deadline: April 16 and May 14

Program dates: 8 weeks, June to August

Eligibility: Students who can work for 10-20 hours/week, for 8-12 weeks. Open to high school students, undergraduates and gap year students!

Additionally, you can also work on independent research in AI, through Veritas AI's Fellowship Program!

Veritas AI focuses on providing high school students who are passionate about the field of AI a suitable environment to explore their interests. The programs include collaborative learning, project development, and 1-on-1 mentorship.

These programs are designed and run by Harvard graduate students and alumni and you can expect a great, fulfilling educational experience. Students are expected to have a basic understanding of Python or are recommended to complete the AI scholars program before pursuing the fellowship.

The AI Fellowship program will have students pursue their own independent AI research project. Students work on their own individual research projects over a period of 12-15 weeks and can opt to combine AI with any other field of interest. In the past, students have worked on research papers in the field of AI & medicine, AI & finance, AI & environmental science, AI & education, and more! You can find examples of previous projects here .

Location : Virtual

$1,790 for the 10-week AI Scholars program

$4,900 for the 12-15 week AI Fellowship

$4,700 for both

Need-based financial aid is available. You can apply here .

Application deadline : On a rolling basis. Applications for fall cohort have closed September 3, 2023.

Program dates : Various according to the cohort

Program selectivity : Moderately selective

Eligibility : Ambitious high school students located anywhere in the world. AI Fellowship applicants should either have completed the AI Scholars program or exhibit past experience with AI concepts or Python.

Application Requirements: Online application form, answers to a few questions pertaining to the students background & coding experience, math courses, and areas of interest.

Additionally, you can check out some summer programs that offer courses in computer science such as the Lumiere Scholars Program !

Stephen is one of the founders of Lumiere and a Harvard College graduate. He founded Lumiere as a PhD student at Harvard Business School. Lumiere is a selective research program where students work 1-1 with a research mentor to develop an independent research paper.

Image source: Stock image

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High School Computer Science Research: The Complete Guide from “Hello, World!” to the Real World

By Ross Greer

PhD Candidate in Electrical & Computer Engineering at the University of California, San Diego

8 minute read

These days, it seems like each month we see an amazingly cool computer technology emerge; in the past year, DALL-E gave us art, ChatGPT helped us write our essays, and autonomous robo-taxis have begun to transport us safely through big cities. There’s a lot to be excited about!

These are all incredible results of incredible efforts, and even though these things seem brand new, they are built on the foundations of decades of computer science research (in fact, Alan Turing’s seminal paper on AI was published back in 1950!).

In this article, I’d like to introduce you to ways that the fun-loving and fast-moving community of computer science researchers functions – that is, how do computer scientists create research projects and share their ideas with the world?

Create a research project tailored to your interests and your schedule

Polygence pairs you with an expert mentor in your area of passion. Together, you work to create a high quality research project that is uniquely your own. We also offer options to explore multiple topics, or to showcase your final product!

It’s important to keep a few big-picture ideas in mind when we talk about computer science research.

Research is an iterative process; we build on top of foundations (principles that act as building blocks or ways to think about problems). These are often mathematical in nature, and independent of our choice of programming language.

By doing this, we seek to push the boundaries of computer science, also known as advancing the state of the art .

When we are doing computer science, we usually demonstrate our ideas through computer programs; in other words, computer programming is a tool we use to realize (make real) computer science ideas. Using Python, Java, or HTML is not the computer science itself; these are the tools we use to do computer science and computer engineering.

In this blog post, I’d like to introduce three common stages of computer science research:

Scoping a Project

Working on a Project

Completing a Project

Before we dive into project scoping, let me briefly introduce myself: I’m Ross, a mentor with Polygence . I conduct research in artificial intelligence and computer vision on two teams - the Laboratory for Intelligent & Safe Automobiles (LISA) and the Center for Research in Entertainment & Learning (CREL)at UC San Diego. One of my favorite parts of my work is mentoring students in research as they investigate and explore their curiosities, and my hope is that this blog post will help you in designing your own research plan.

Scoping a Computer Science Research Project

Computer science research can be both exciting and overwhelming, and sometimes it’s hard to separate the exciting things we hear about in the news from the years of work and development that led to the results. When you’re embarking on a new project or working on an existing one, it’s important to be realistic about how much you can do on a small (or solo) team and any timeline you set for yourself. A lot of my job as a mentor is to help you find the best project scope for you , considering your current background, what skills you would like to develop, and what you would like to learn and explore. Because I work with autonomous vehicles, many students let me know in our first meeting that they would like to make a self-driving car – an awesome endeavor! At this point, I get to deliver some mixed news: the bad news is, there are university labs and companies of thousands of people with decades of experience who are struggling towards just a small piece of this puzzle, but the good news is, there are so many puzzle pieces that we can usually find one that can be an interesting and fun learning experience (and sometimes, even a novel research contribution)! In the rest of this section, I’ll refer back to this ambitious “self-driving car” example; this analogy applies well to most computer science research domains.

From my experience mentoring students, I have found that most projects typically evolve into one of three categories. I’ve included here a brief description of the categories, some pros and cons of completing a project in each category, and a recommendation that speaks to your experience as a student.

Projects to learn and explore foundations

In this style of project, you explore a problem by learning the fundamental approaches that past research has established. You will learn new ways to think about or frame the problem, and identify ongoing challenges and questions. Programming technology may come and go, and the state-of-the-art will always change, but the underlying principles will serve you throughout your college coursework and research.

Develop problem understanding and research maturity.

Learn a strong fundamental mindset for approaching later university work or joining a lab team.

Practice programming using established code libraries and your own algorithm implementations.

Typically will not lead to a significantly novel research contribution . This may limit possible publication opportunities (discussed later in the article). Projects in this category still have many publication opportunities as literature reviews, survey papers, high school journals, or student magazines.

Recommendation :

I recommend this type of project for students looking for a learning experience. You will have the chance to explore the intricacies and challenges of your chosen problem, and understand the progression taken by past researchers to understand and approach the problem. You will continue to develop programming skills, and there may even be opportunities for publication for dedicated students.

Your Project Your Schedule - Your Admissions Edge!

Register to get paired with one of our expert mentors and to get started on exploring your passions today! And give yourself the edge you need to move forward!

Projects to learn and apply programming tools

In this style of project, you will learn fundamentals of computer programming and will apply these tools to implement a solution to a computer science problem. The solution you implement may be an existing, known approach, or a simple preliminary approach – and this is great, because your focus is on developing your programming skills!

Improve programming skills (fundamentals which translate between programming languages, as well as language-specific skills).

Reinforce classroom learning from courses like APCS.

Excellent projects to create a blog or tutorial, or to work towards existing goals in high school robotics competitions or computer science clubs.

Because these projects focus on developing programming skills, they typically do not produce output suitable for research publications. Projects in this category still have many publication opportunities as blog posts or tutorials.

I recommend this type of project for first-time computer programmers looking to build their programming skills in the context of an interesting research problem.

Projects to push the state of the art

In this style of project, you will try to solve an open research problem in a way that is better than the existing solutions. Typically, this involves first implementing an existing solution, then exploring ideas to make some improvement to the existing approach. Sometimes, this means trying something brand new!

Strongest publication opportunities when projects succeed.

Build intense familiarity with open research problems and challenges.

Spend time studying and exploring cutting-edge computer science ideas.

When performing exploratory research, there is no guarantee of success of your experiments.

You may experience huge time sinks when implementing state of the art code, even when working from an existing repository. These codebases are often poorly documented, can sometimes work only on specific operating systems or with specific hardware, and may even contain bugs in their code. It’s a considerable and commendable effort just to get a state of the art programming running on a home computer!

Time with your mentor may be spent debugging frustrating issues (such as installing packages) instead of discussing interesting research questions. These debugging sessions may contain more patient waiting and hand-waving than teaching as you sift through thousands of lines of code – again, with the understanding that there is no guarantee of successful implementation.

There is a large time commitment expected outside of sessions to read, study, and understand the state of the art.

These projects are often very intense in setup, execution, and reading. Students who have been successful in these projects have strong prior programming experience, are driven to read and understand their research problem, and often have additional help (a parent or teacher) or are otherwise prepared to spend more than the typical 10 Polygence sessions to develop a substantial project. These projects can lead to awesome results and publications, but you should approach with a mindset that the learning experience will still be rewarding if your experiments do not yield optimal results (or if they fail partway).

You may have noticed that I did not mention prerequisites specific to each project category; prerequisite recommendations may vary from mentor-to-mentor and project-to-project, but in general (at least for students interested in AI), there is plenty of learning to be done at any level of programming experience. I usually recommend that my students have completed a basic Python primer (I recommend the free set of lessons at www.learnpython.org ) to build familiarity with loops, control, lists, dictionaries, and functions. With these building blocks, we have enough common ground to begin working together. Of course, if you bring additional experience from other courses or camps (at school or online), that’s great too! Remember that prerequisites are about more than just completing a course – what’s most important is the understanding you carry forward with you to your next project.

For students interested in computer science projects in a specific language or domain (web development, game development, networking, data mining, etc.), your mentor may have specific expectations or recommended background; my advice is to always be upfront with your mentor about your familiarity with a given language, and let them point you to resources to help you continue learning as you plan your project. Computer science research can be a lifelong learning activity – for these projects, come as you are, and let your mentor help find the right fit for your background.

Working on a Computer Science Research Project

How should you spend your valuable, limited time with your mentor?

Ideally, you can spend all of this time discussing the research elements of your project: your questions, your ideas, your experimental setup, your results, and your writing. But because we are working on computer science research (and using computer programming as our tool), there will inevitably be a point where you could use a little help debugging or troubleshooting. Computer science takes a lot of work, and sometimes it’s actually the exciting stuff that happens the easiest/fastest, after a LOT of boring setup work – things like setting up your coding environment, installing software, debugging a codebase, annotating data, etc.

While your mentor will do their best to be efficient in helping you solve your technical roadblock, there are some steps you can take to help your mentor help you:

Work on issues outside of your sessions, and come prepared to explain to your mentor what you tried and to show them any errors you hit. Do use your mentor’s time to help get unstuck, but make sure you’ve given it your best and hit a true roadblock first – otherwise, 10 hours will be gone before you know it!

You may think you are giving enough of a summary description of an error for your mentor to help, but mentors often have years of programming experience and have learned tricks and keywords in error messages that help them jump to a solution. For this reason, always be prepared to share the full error shown on your screen.

Don’t be afraid of using Google searches and outside resources like StackOverflow to ask your debugging questions. The computer science community is often a very supportive and encouraging online presence!

Make an account on GitHub, create a repository with your code, and share this with your mentor. This will save you countless sessions of “Please share your screen. Can you jump to line 839? Ok, now Ctrl+F for “time_sync”. Great, can you jump to line 526? …”. Plus, this is a great way to later share your code with your community!

Need some brief help getting “unstuck”, and now have a clear path forward? Ask if your mentor would consider stopping your session at half-an-hour so that you can continue working independently (and save another half-session of mentorship for your future questions)!

Finally, always be sure to complete all readings and assignments recommended by your mentor between sessions. Your mentor often has a large roadmap in mind for you to complete your project on your timeline, so it is really important to show up to sessions with an earnest effort given to assigned tasks. Don’t be afraid to communicate to your mentor if you need some help – we are here to make your project an awesome experience for you!

Completing a Computer Science Research Project

So, your code is working, and you’re seeing some really cool results that you would like to share with others. Fantastic! Two important questions to help you and your mentor decide how to share your work: who would you like to share your work with, and why would you like to share it?

Self Publication

If you would like to share your work with friends, family, or perhaps a club at your school, it may be easiest to choose a non-peer-reviewed publication option. For some, this could just be a nicely-formatted report you pass around, but other great options include blog posts on medium.com, towardsdatascience.com, Instructables, or a personal blog. If you would like to share your code in addition to your report, a public GitHub repository is another great choice (as long as you include a descriptive README file which tells visitors how to use your code!). For these purposes, you can be as creative as you like – you control the publication process.

Another self-publication hotbed for computer science research is arXiv, which is what is known as a preprint server . This is a place where researchers deposit their works during the long publication process (it can sometimes take over a year for a submitted work to appear in a journal!). The reason to use a preprint server is to make sure that you receive proper credit if you are “first” to have an idea, and to allow other researchers the opportunity to build off of and cite your work during the slow publication period.

However, I actually discourage my Polygence students from using arXiv, because the intention of arXiv is to allow exchange of ideas among the academic community. While some Polygence students do make significant research contributions, not all reports carry the impact that the research community would like to attend to. For this reason, arXiv actually requires all submitters to be endorsed by someone within the research community. On the plus side, there is actually a fantastic preprint server similar to arXiv designed and hosted by Polygence: RARS, the Research Archive of Rising Scholars ! This site welcomes your research, whether state-of-the-art, a well-crafted literature review, a repetition of prior work to try learning a foundational method, or a piece of creative work.

Remember, you are in control of how you share your work, so be as creative as you like! Sometimes, an excellent YouTube video or Twitter thread brings more attention to a project than a formal blog post. Have fun and share your awesome project proudly!

Peer Review

While these publication options are often engaging for their readers and easy to share, they are also missing the small flavor of peer review that catches the attention of folks in academia (in other words, people who may soon be reading over your college admissions, research position, or internship applications). By sending your work through a peer review process, you are opening your work to the judgment and advice of seniors in your project area. This is a valuable opportunity to receive feedback, but can also be disappointing in cases when your work is judged to be inadequate for the standards of the publication venue. (As an aside, I would encourage you to never take these judgments personally; there are plenty of reasons a work may not be selected that have nothing to do with the quality of the work. Sometimes, the venue is looking for works on a very particular, specific topic. Other times, the reviewer might just be having a bad day. Take their advice with a grain of salt, and move ahead to your next goal!).

In the category of peer review, there are several types of publication venues to choose from:

Local and Regional Science Fairs: these are a fantastic place to share your research with your local community (and even win some nifty awards). One small note—many science fairs are used to receiving scientific projects (picture your classic chemistry and biology experiments) and engineering projects (where the goal is to invent or design). The nature of computer science research sits somewhere in a middle ground between these, so it can often be difficult to figure out which category your project belongs to! Consult with your Polygence mentor, advisor, or science fair contact to identify the right fit for your particular project – you certainly belong, it’s just a matter of finding the best category.

High School Journals: these journals are intended to showcase research projects by high school students. Different journals have different reviewers and editors; some are managed only by high school students, others by college clubs, and others by professional researchers. A high school journal can be a great first journey into the publishing process. With the slightly lower barrier to entry, do be aware that some journals (particularly those run by high school students or college clubs) may cease operations without warning.

IEEE Potentials: this magazine publishes great monthly articles that thoroughly explain fundamental or cutting-edge computer science topics to a young reader base. This is a great fit for articles that seek to teach and are written in a thorough and casually-readable tone.

“Pay-to-Play” Conferences and Journals: sometimes nicknamed “predatory” conferences and journals, these publication venues have a fairly low barrier to accepting your research report. As a result, the true research impact of these venues is fairly low (and it can be expensive to have your project featured). However, these can be a good way for a beginning scholar to get their foot in the door of the publication world. These conferences often have vague and deceptive names (such as “American Journal of Computer Science Research, Inventions, and Technology”) that closely mirror their established conference counterparts. Be cautious when considering these choices, as you can often have the same publication impact from a free option such as RARS . When in doubt, always discuss with your Polygence mentor to figure out if this type of conference or journal is a reasonable step towards your academic goals.

Academic Conferences and Journals: our field is full of exciting (and often ultra-specific) conferences and journals! These are typically intended for professors, research professionals, and graduate students, but sometimes excellent work from younger researchers can be selected for publication. Your mentor can give you a good idea if your project meets the criteria, and can also point you towards the most relevant journals and conferences for your field. A popular option to consider is also the workshop track of conferences. Workshops are often very, very specific (for example, a large Computer Vision conference may have a workshop track dedicated to Computer Vision for Fashion, or Computer Vision for Sports). These workshops often have a slightly-less-intense peer review process, and may or may not formally publish their proceedings. If you’re interested in the field, it’s a great way to share your research with others and to learn what’s up-and-coming! Do keep in mind that if your work is accepted, it is usually expected that you will give a presentation or poster about your research, which may require some travel as conferences shift back to in-person gathering.

Most importantly, if you find an opportunity you are excited about, share it with your mentor! They will be excited to provide guidance and help you work towards your goal.

Getting started in computer science research can be exciting and overwhelming. Lean on your mentor to help you identify a reasonable, interesting, and fun scope for your project. Work hard both during and outside of your sessions to build an awesome project, and then share your project with your community in a way that also promotes growth toward your personal academic goals. Most importantly, enjoy your computer science journey!

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Computer Science > Computer Vision and Pattern Recognition

Title: learning to score sign language with two-stage method.

Abstract: Human action recognition and performance assessment have been hot research topics in recent years. Recognition problems have mature solutions in the field of sign language, but past research in performance analysis has focused on competitive sports and medical training, overlooking the scoring assessment ,which is an important part of sign language teaching digitalization. In this paper, we analyze the existing technologies for performance assessment and adopt methods that perform well in human pose reconstruction tasks combined with motion rotation embedded expressions, proposing a two-stage sign language performance evaluation pipeline. Our analysis shows that choosing reconstruction tasks in the first stage can provide more expressive features, and using smoothing methods can provide an effective reference for assessment. Experiments show that our method provides good score feedback mechanisms and high consistency with professional assessments compared to end-to-end evaluations.

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arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

MyU : For Students, Faculty, and Staff

Fall 2024 CSCI Special Topics Courses

Cloud computing.

Meeting Time: 09:45 AM‑11:00 AM TTh Instructor: Ali Anwar Course Description: Cloud computing serves many large-scale applications ranging from search engines like Google to social networking websites like Facebook to online stores like Amazon. More recently, cloud computing has emerged as an essential technology to enable emerging fields such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning. The exponential growth of data availability and demands for security and speed has made the cloud computing paradigm necessary for reliable, financially economical, and scalable computation. The dynamicity and flexibility of Cloud computing have opened up many new forms of deploying applications on infrastructure that cloud service providers offer, such as renting of computation resources and serverless computing. This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy. Registration Prerequisites: CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/6BvbUwEkBK41tPJ17 ).

CSCI 5980/8980

Machine learning for healthcare: concepts and applications.

Meeting Time: 11:15 AM‑12:30 PM TTh Instructor: Yogatheesan Varatharajah Course Description: Machine Learning is transforming healthcare. This course will introduce students to a range of healthcare problems that can be tackled using machine learning, different health data modalities, relevant machine learning paradigms, and the unique challenges presented by healthcare applications. Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.

Registration Prerequisites: CSCI 5521 or equivalent. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/z8X9pVZfCWMpQQ6o6 ).

Visualization with AI

Meeting Time: 04:00 PM‑05:15 PM TTh Instructor: Qianwen Wang Course Description: This course aims to investigate how visualization techniques and AI technologies work together to enhance understanding, insights, or outcomes.

This is a seminar style course consisting of lectures, paper presentation, and interactive discussion of the selected papers. Students will also work on a group project where they propose a research idea, survey related studies, and present initial results.

This course will cover the application of visualization to better understand AI models and data, and the use of AI to improve visualization processes. Readings for the course cover papers from the top venues of AI, Visualization, and HCI, topics including AI explainability, reliability, and Human-AI collaboration. This course is designed for PhD students, Masters students, and advanced undergraduates who want to dig into research.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/YTF5EZFUbQRJhHBYA ). Although the class is primarily intended for PhD students, motivated juniors/seniors and MS students who are interested in this topic are welcome to apply, ensuring they detail their qualifications for the course.

Visualizations for Intelligent AR Systems

Meeting Time: 04:00 PM‑05:15 PM MW Instructor: Zhu-Tian Chen Course Description: This course aims to explore the role of Data Visualization as a pivotal interface for enhancing human-data and human-AI interactions within Augmented Reality (AR) systems, thereby transforming a broad spectrum of activities in both professional and daily contexts. Structured as a seminar, the course consists of two main components: the theoretical and conceptual foundations delivered through lectures, paper readings, and discussions; and the hands-on experience gained through small assignments and group projects. This class is designed to be highly interactive, and AR devices will be provided to facilitate hands-on learning. Participants will have the opportunity to experience AR systems, develop cutting-edge AR interfaces, explore AI integration, and apply human-centric design principles. The course is designed to advance students' technical skills in AR and AI, as well as their understanding of how these technologies can be leveraged to enrich human experiences across various domains. Students will be encouraged to create innovative projects with the potential for submission to research conferences.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/Y81FGaJivoqMQYtq5 ). Students are expected to have a solid foundation in either data visualization, computer graphics, computer vision, or HCI. Having expertise in all would be perfect! However, a robust interest and eagerness to delve into these subjects can be equally valuable, even though it means you need to learn some basic concepts independently.

Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions: • How can we design and build sustainable computing systems? • How can we manage resources efficiently? • What system software and algorithms can reduce computational needs? Topics of interest would include: • Sustainable system design and architectures • Sustainability-aware systems software and management • Sustainability in large-scale distributed computing (clouds, data centers, HPC) • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

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Computer Science Research Topics (+ Free Webinar)
Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you've landed on this post, chances are you're looking for a computer science-related research topic, but aren't sure where to start.Here, we'll explore a variety of CompSci & IT-related research ideas and topic thought-starters ...
500+ Computer Science Research Topics
Computer Science Research Topics are as follows: Using machine learning to detect and prevent cyber attacks. Developing algorithms for optimized resource allocation in cloud computing. Investigating the use of blockchain technology for secure and decentralized data storage. Developing intelligent chatbots for customer service.
100+ Great Computer Science Research Topics Ideas for 2023
If you're searching for the best project topics for computer science students that will stand out in a journal, check below: Developments in human-computer interaction. Applications of computer science in medicine. Developments in artificial intelligence in image processing. Discuss cryptography and its applications.
Undergraduate Research Topics
Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work.
Top 30+ Computer Science Project Topics of 2024 [Source Code]
You will find projects for professionals, interns, freelancers, as well as final year projects for computer science. Top Computer Science Project Topics with Source Code. Source: crio.do. 1. Hospital Management System. Type: Application development, Database management, Programming. There is no shortage of computer science project topics out there.
Latest Computer Science Research Topics for 2024
It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one. 1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges.
Research projects
Text Analytics and Blog/Forum Analysis. Trustworthy Multi-source Learning (2025 entry onward) Verification Based Model Extraction Attack and Defence for Deep Neural Networks. Zero-Shot Learning and Applications. Search the postgraduate research projects currently available at The University of Manchester's Department of Computer Science.
Computer Science Research Topics
Computer science research topics can be divided into several categories, such as artificial intelligence, big data and data science, human-computer interaction, security and privacy, and software engineering. If you are a student or researcher looking for computer research paper topics. In that case, this article provides some suggestions on ...
25 of today's coolest network and computing research projects
Ghost-USB-Honeypot project. This effort, focused on nixing malware like Flame that spreads from computer to computer via USB storage drives, got its start based on research from Sebastian Poeplau ...
Computer Science Projects
To choose a major project for Computer Science Engineering (CSE), follow these steps: Identify your interests and strengths within CSE. Research current trends and emerging technologies in the field. Discuss project ideas with professors, peers, and industry professionals. Consider the project's feasibility, scope, and potential impact.
Research Projects
The CS department has resources available for research projects. The following is a quick start guide to research projects in Computer Science Department. This quick start guide does not cover all topics and it is recommended that you consult the CS Guide for more information. This guide is designed to help those beginning a research project by ...
Computer Science Research & Passion Project Ideas
Idea by computer science mentor Clayton. 5. Designing your own autocorrect algorithm. This is a project with two focal ideas - one in computer science and one in machine learning. The first idea is called dynamic programming and is one of the traditional ways in computer science to implement an autocorrect algorithm.
200+ Computer Science Research Project Ideas for College Students in
Interesting Computer Science Design Project Ideas for Finalists. Application of face detection technologies in crime deterrence. The role of an online auction system in preventing bribery. Application of computing technologies to improve academic performance. Shortcomings of the e-authentication systems.
Research Opportunities
Opportunities for undergraduates to conduct research in engineering, the applied sciences, and in related fields abound at Harvard. As part of your coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to take part in or participate in some extraordinary projects covering topics ...
150+ Final Year Project Ideas For Computer Science Students
Computer Vision: Facial Recognition System: Build a system that recognizes faces for security or authentication.; Object Detection: Create a system that can identify and locate objects within images or videos.; Traffic Sign Recognition: Develop a system that recognizes and interprets traffic signs.; Gesture Recognition: As mentioned earlier, work on gesture recognition for human-computer ...
Research project ideas for Computer Science students
2. Graphic simulator of programming structures and basic algorithms. This topic will result in a progressive web application that can show, graphically, what exactly is happening while the computer executes the following: Conditionals. Loops. Basic algorithms: counting, summing, maximum. Call to methods.
2020-2021 SURE Research Projects in CSE
The learning outcome for students will be hands-on experience in interdisciplinary research with connections to Machine Learning and Computational Economics. Expected research delivery mode: Remote. CSE Project #11: Hazel Notebooks: Building a Better Jupyter. Faculty Mentor: Cyrus Omar [comar @ umich.edu]
How to Look for Ideas in Computer Science Research
Else, I consider it a big deficiency and may hurt you significantly in the future after you graduate, e.g., you may have a hard time leading a research program independently as a professor in ...
25+ Research Ideas in Computer Science for High School Students
This can help students quickly improve their writing skills by improving the feedback mechanism. 4. Develop a computer vision system to monitor wildlife populations in a specific region. 5. Investigate the use of computer vision in detecting and diagnosing medical conditions from medical images. 6.
High School Computer Science Research: The Complete Guide ...
It's important to keep a few big-picture ideas in mind when we talk about computer science research. Research is an iterative process; we build on top of foundations (principles that act as building blocks or ways to think about problems). These are often mathematical in nature, and independent of our choice of programming language.
Computer Science Science Projects
Computer Science Science Projects. (55 results) From cell phones to social media, computer science is a part of your daily life. Everything from traffic lights to medical devices requires both computer hardware and software these days. Creative problem solvers are using computer science to tackle social problems, improve agriculture, make great ...
12 Interesting Computer Science Project Ideas & Topics For ...
8. Symbol recognition. This is one of the excellent computer science project ideas for beginners. The proposed project seeks to build a system that can recognize symbols inserted by the user. This symbol recognition system leverages an image recognition algorithm to process images and identify symbols.
Research Projects
April 11th, 2024 Marcel Dall'Agnol joins the department as teaching faculty, bringing expertise in theoretical computer science; April 10th, 2024 Grad alum Avi Wigderson wins Turing Award for groundbreaking insights in computer science; April 9th, 2024 Computer science faculty recognized at the annual SEAS teaching awards
Learning to Score Sign Language with Two-stage Method
Human action recognition and performance assessment have been hot research topics in recent years. Recognition problems have mature solutions in the field of sign language, but past research in performance analysis has focused on competitive sports and medical training, overlooking the scoring assessment ,which is an important part of sign language teaching digitalization. In this paper, we ...
Fall 2024 CSCI Special Topics Courses
Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.Registration Prerequisites: CSCI 5521 or equivalent.