msc computer science thesis

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Computer Science

Master of Science (MSc)

Thesis-based program

Program overview.

​The Computer Science program provides the bedrock for exciting careers at the forefront of innovation in private industry or entrepreneurship. It helps students build skills and novel ideas for designing and implementing software, as well as developing effective algorithms to solve computing problems and plan and manage organizational technology infrastructures. Cutting-edge companies such as Google, Apple, Amazon, Facebook, Autodesk, and Microsoft frequently hire graduates. Alumni are also actively engaged in entrepreneurship, innovation, and creating start-ups.

Completing this program

• Core Course: Research Methodology in Computer Science.
• Seminar: Students are required to give a departmental seminar on the results of their research.
• Software Engineering Specialization: Four additional courses from a list approved by the Department of Computer Science.
• Additional Courses: May include Artificial Intelligence, Databases, Computer Graphics, Scientific Computing, HCI and Visualization and others.
• Thesis: Students will complete a thesis based on original research.

Specializations

• Master of Science (MSc) Thesis-based in Computer Science, Software Engineering Specialization . The specialization is offered jointly through the Department of Computer Science and the Department of Electrical and Software Engineering.
• Wearable Technology Interdisciplinary Specialization
• Computational Neuroscience Interdisciplinary Specialization

Technology sector, business start-ups, computer science research, IT, software development.

A master’s degree in computer science will give you the pre-requisite for a PhD.

Students are required to prepare a thesis and successfully defend in an open oral defense.

One core course and four electives

Learn more about program requirements in the Academic Calendar

Classroom delivery

Time commitment.

Two years full-time

A supervisor is required, but is not required prior to the start of the program

See the Graduate Calendar for information on  fees and fee regulations,  and for information on  awards and financial assistance .

Virtual Tour

Explore the University of Calgary (UCalgary) from anywhere. Experience all that UCalgary has to offer for your graduate student journey without physically being on campus. Discover the buildings, student services and available programs all from your preferred device.

Supervisors

Learn about faculty available to supervise this degree. Please note: additional supervisors may be available. Contact the program for more information.

John Aycock

Mario Costa Sousa

Philip Fong

Dr. Marina Gavrilova

Majid Ghaderi

Helen Ai He

Peter Høyer

Christian Jacob

Michael Jacobson, Jr.

Admission requirements

A minimum of 3.3 GPA on a 4.0 point system, over the past two years of full-time study (a minimum of 10 full-course equivalents or 60 units) of the undergraduate degree. Post-degree CS courses may be considered when calculating GPA. Exceptions to GPA requirement may be considered for those with either:

• demonstrated research excellence, or
• GRE General scores of at least 600 verbal and 750 quantitative and either 720 analytical (old test format) or 5.5 (new test format)

Minimum education

Four year degree in computer science or another field with 3rd or 4th year courses in the following areas: Theory of Computation; Software Engineering; Systems (OS, Compilers, Distributed Systems, Networking); Application (AI, Graphics, Databases, etc.).

Work samples

Reference letters.

Two letters of reference dated within twelve months of the application.

Test scores

Optional: Special consideration will be given to those with GRE scores of at least 600 verbal, 750 quantitative, and 720 analytical (5.5 in the new format). Applicants from outside Canada are expected to apply with GRE scores.

English language proficiency

An applicant whose primary language is not English may fulfill the English language proficiency requirement in one of the following ways:

• Test of English as a Foreign Language (TOEFL ibt)  score of 97 (Internet-based, with no section less than 20).
• International English Language Testing System (IELTS)  score of 7.0 (minimum of 6.0 in each section).
• Pearson Test of English (PTE)   score of 68, or higher (Academic version).
• Canadian Academic English Language test (CAEL)  score of 70 (70 in some sections – up to the program, 60 in all other).  
• Academic Communication Certificate (ACC)  score of A- in one or two courses (up to the program), “B+” on all other courses.  
• Cambridge C1 Advanced or Cambridge C2 Proficiency  minimum score of 191.

*Please contact your program of interest if you have any questions about ELP requirements

WINTER (For admission on January 1)

• Final Application Deadline – July 1 (Final Documentation Submission Deadline – July 15 )
• Final Application Deadline – September 1 (Final Documentation Submission Deadline – October 1 )

--------------

FALL (For admission on September 1)

• Early Applications (complete application review) -  January 15
• Final Application Deadline –  March 1  (Final Documentation Submission Deadline –  March 15 )
• Final Application Deadline – May 1 (Final Documentation Submission Deadline – June 1 )

If you're not a Canadian or permanent resident, or if you have international credentials, make sure to learn about international requirements

Are you ready to apply?

Learn more about this program, department of computer science.

602 ICT Building 856 Campus Place NW Calgary, ABT2N 1N4 403.220.3528

Contact the Graduate Program Administrator

Visit the departmental website

University of Calgary 2500 University Drive NW Calgary, AB, T2N 1N4

Visit the Faculty of Science's website

Learn more about UCalgary by taking a virtual tour

Related programs

If you're interested in this program, you might want to explore other UCalgary programs.

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Course-based MEng

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Thesis-based MSc

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Home > CICS > CS > CS_DISS

Computer Science Department Dissertations Collection

Dissertations from 2024 2024.

Enabling Privacy and Trust in Edge AI Systems , Akanksha Atrey, Computer Science

Generative Language Models for Personalized Information Understanding , Pengshan Cai, Computer Science

Towards Automatic and Robust Variational Inference , Tomas Geffner, Computer Science

Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping , Samer Nashed, Computer Science

Policy Gradient Methods: Analysis, Misconceptions, and Improvements , Christopher P. Nota, Computer Science

Data to science with AI and human-in-the-loop , Gustavo Perez Sarabia, Computer Science

Question Answering By Case-Based Reasoning With Textual Evidence , Dung N. Thai, Computer Science

Dissertations from 2023 2023

An Introspective Approach for Competence-Aware Autonomy , Connor Basich, Computer Science

Foundations of Node Representation Learning , Sudhanshu Chanpuriya, Computer Science

Learning to See with Minimal Human Supervision , Zezhou Cheng, Computer Science

IMPROVING USER EXPERIENCE BY OPTIMIZING CLOUD SERVICES , Ishita Dasgupta, Computer Science

Automating the Formal Verification of Software , Emily First, Computer Science

Learning from Sequential User Data: Models and Sample-efficient Algorithms , Aritra Ghosh, Computer Science

Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data , Mahmood Jasim, Computer Science

Rigorous Experimentation For Reinforcement Learning , Scott M. Jordan, Computer Science

Towards Robust Long-form Text Generation Systems , Kalpesh Krishna, Computer Science

Emerging Trustworthiness Issues in Distributed Learning Systems , Hamid Mozaffari, Computer Science

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP , Milad nasresfahani, Computer Science

Evidence Assisted Learning for Clinical Decision Support Systems , Bhanu Pratap Singh Rawat, Computer Science

DESIGN AND ANALYSIS OF CONTENT CACHING SYSTEMS , Anirudh Sabnis, Computer Science

Quantifying and Enhancing the Security of Federated Learning , Virat Vishnu Shejwalkar, Computer Science

Effective and Efficient Transfer Learning in the Era of Large Language Models , Tu Vu, Computer Science

Data-driven Modeling and Analytics for Greening the Energy Ecosystem , John Wamburu, Computer Science

Bayesian Structural Causal Inference with Probabilistic Programming , Sam A. Witty, Computer Science

LEARNING TO RIG CHARACTERS , Zhan Xu, Computer Science

GRAPH REPRESENTATION LEARNING WITH BOX EMBEDDINGS , Dongxu Zhang, Computer Science

Dissertations from 2022 2022

COMBINATORIAL ALGORITHMS FOR GRAPH DISCOVERY AND EXPERIMENTAL DESIGN , Raghavendra K. Addanki, Computer Science

MEASURING NETWORK INTERFERENCE AND MITIGATING IT WITH DNS ENCRYPTION , Seyed Arian Akhavan Niaki, Computer Science

Few-Shot Natural Language Processing by Meta-Learning Without Labeled Data , Trapit Bansal, Computer Science

Communicative Information Visualizations: How to make data more understandable by the general public , Alyxander Burns, Computer Science

REINFORCEMENT LEARNING FOR NON-STATIONARY PROBLEMS , Yash Chandak, Computer Science

Modeling the Multi-mode Distribution in Self-Supervised Language Models , Haw-Shiuan Chang, Computer Science

Nonparametric Contextual Reasoning for Question Answering over Large Knowledge Bases , Rajarshi Das, Computer Science

Languages and Compilers for Writing Efficient High-Performance Computing Applications , Abhinav Jangda, Computer Science

Controllable Neural Synthesis for Natural Images and Vector Art , Difan Liu, Computer Science

Probabilistic Commonsense Knowledge , Xiang Li, Computer Science

DISTRIBUTED LEARNING ALGORITHMS: COMMUNICATION EFFICIENCY AND ERROR RESILIENCE , Raj Kumar Maity, Computer Science

Practical Methods for High-Dimensional Data Publication with Differential Privacy , Ryan H. McKenna, Computer Science

Incremental Non-Greedy Clustering at Scale , Nicholas Monath, Computer Science

High-Quality Automatic Program Repair , Manish Motwani, Computer Science

Unobtrusive Assessment of Upper-Limb Motor Impairment Using Wearable Inertial Sensors , Brandon R. Oubre, Computer Science

Mixture Models in Machine Learning , Soumyabrata Pal, Computer Science

Decision Making with Limited Data , Kieu My Phan, Computer Science

Neural Approaches for Language-Agnostic Search and Recommendation , Hamed Rezanejad Asl Bonab, Computer Science

Low Resource Language Understanding in Voice Assistants , Subendhu Rongali, Computer Science

Enabling Daily Tracking of Individual’s Cognitive State With Eyewear , Soha Rostaminia, Computer Science

LABELED MODULES IN PROGRAMS THAT EVOLVE , Anil K. Saini, Computer Science

Reliable Decision-Making with Imprecise Models , Sandhya Saisubramanian, Computer Science

Data Scarcity in Event Analysis and Abusive Language Detection , Sheikh Muhammad Sarwar, Computer Science

Representation Learning for Shape Decomposition, By Shape Decomposition , Gopal Sharma, Computer Science

Metareasoning for Planning and Execution in Autonomous Systems , Justin Svegliato, Computer Science

Approximate Bayesian Deep Learning for Resource-Constrained Environments , Meet Prakash Vadera, Computer Science

ANSWER SIMILARITY GROUPING AND DIVERSIFICATION IN QUESTION ANSWERING SYSTEMS , Lakshmi Nair Vikraman, Computer Science

Dissertations from 2021 2021

Neural Approaches to Feedback in Information Retrieval , Keping Bi, Computer Science

Sociolinguistically Driven Approaches for Just Natural Language Processing , Su Lin Blodgett, Computer Science

Enabling Declarative and Scalable Prescriptive Analytics in Relational Data , Matteo Brucato, Computer Science

Neural Methods for Answer Passage Retrieval over Sparse Collections , Daniel Cohen, Computer Science

Utilizing Graph Structure for Machine Learning , Stefan Dernbach, Computer Science

Enhancing Usability and Explainability of Data Systems , Anna Fariha, Computer Science

Algorithms to Exploit Data Sparsity , Larkin H. Flodin, Computer Science

3D Shape Understanding and Generation , Matheus Gadelha, Computer Science

Robust Algorithms for Clustering with Applications to Data Integration , Sainyam Galhotra, Computer Science

Improving Evaluation Methods for Causal Modeling , Amanda Gentzel, Computer Science

SAFE AND PRACTICAL MACHINE LEARNING , Stephen J. Giguere, Computer Science

COMPACT REPRESENTATIONS OF UNCERTAINTY IN CLUSTERING , Craig Stuart Greenberg, Computer Science

Natural Language Processing for Lexical Corpus Analysis , Abram Kaufman Handler, Computer Science

Social Measurement and Causal Inference with Text , Katherine A. Keith, Computer Science

Concentration Inequalities in the Wild: Case Studies in Blockchain & Reinforcement Learning , A. Pinar Ozisik, Computer Science

Resource Allocation in Distributed Service Networks , Nitish Kumar Panigrahy, Computer Science

History Modeling for Conversational Information Retrieval , Chen Qu, Computer Science

Design and Implementation of Algorithms for Traffic Classification , Fatemeh Rezaei, Computer Science

SCALING DOWN THE ENERGY COST OF CONNECTING EVERYDAY OBJECTS TO THE INTERNET , Mohammad Rostami, Computer Science

Deep Learning Models for Irregularly Sampled and Incomplete Time Series , Satya Narayan Shukla, Computer Science

Traffic engineering in planet-scale cloud networks , Rachee Singh, Computer Science

Video Adaptation for High-Quality Content Delivery , Kevin Spiteri, Computer Science

Learning from Limited Labeled Data for Visual Recognition , Jong-Chyi Su, Computer Science

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications , Amee Trivedi, Computer Science

Geometric Representation Learning , Luke Vilnis, Computer Science

Understanding of Visual Domains via the Lens of Natural Language , Chenyun Wu, Computer Science

Towards Practical Differentially Private Mechanism Design and Deployment , Dan Zhang, Computer Science

Audio-driven Character Animation , Yang Zhou, Computer Science

Dissertations from 2020 2020

Noise-Aware Inference for Differential Privacy , Garrett Bernstein, Computer Science

Motion Segmentation - Segmentation of Independently Moving Objects in Video , Pia Katalin Bideau, Computer Science

An Empirical Assessment of the Effectiveness of Deception for Cyber Defense , Kimberly J. Ferguson-Walter, Computer Science

Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems , Richard Freedman, Computer Science

Improving Reinforcement Learning Techniques by Leveraging Prior Experience , Francisco M. Garcia, Computer Science

Optimization and Training of Generational Garbage Collectors , Nicholas Jacek, Computer Science

Understanding the Dynamic Visual World: From Motion to Semantics , Huaizu Jiang, Computer Science

Improving Face Clustering in Videos , SouYoung Jin, Computer Science

Reasoning About User Feedback Under Identity Uncertainty in Knowledge Base Construction , Ariel Kobren, Computer Science

Learning Latent Characteristics of Data and Models using Item Response Theory , John P. Lalor, Computer Science

Higher-Order Representations for Visual Recognition , Tsung-Yu Lin, Computer Science

Learning from Irregularly-Sampled Time Series , Steven Cheng-Xian Li, Computer Science

Dynamic Composition of Functions for Modular Learning , Clemens GB Rosenbaum, Computer Science

Improving Visual Recognition With Unlabeled Data , Aruni Roy Chowdhury, Computer Science

Deep Neural Networks for 3D Processing and High-Dimensional Filtering , Hang Su, Computer Science

Towards Optimized Traffic Provisioning and Adaptive Cache Management for Content Delivery , Aditya Sundarrajan, Computer Science

The Limits of Location Privacy in Mobile Devices , Keen Yuun Sung, Computer Science

ALGORITHMS FOR MASSIVE, EXPENSIVE, OR OTHERWISE INCONVENIENT GRAPHS , David Tench, Computer Science

System Design for Digital Experimentation and Explanation Generation , Emma Tosch, Computer Science

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This collection of MIT Theses in DSpace contains selected theses and dissertations from all MIT departments. Please note that this is NOT a complete collection of MIT theses. To search all MIT theses, use MIT Libraries' catalog .

MIT's DSpace contains more than 58,000 theses completed at MIT dating as far back as the mid 1800's. Theses in this collection have been scanned by the MIT Libraries or submitted in electronic format by thesis authors. Since 2004 all new Masters and Ph.D. theses are scanned and added to this collection after degrees are awarded.

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All of our academic staff are research active, working with a team of post-graduate and post-doctoral researchers and a lively population of research students. Our research focuses on core themes of theoretical and practical computer science: artificial intelligence and symbolic computation, networked and distributed systems, systems engineering, and human computer interaction.

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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

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

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

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Home > Computer Science > CompSci TDs > Masters Theses

Computer Science Masters Theses

Theses from 2024 2024.

Enabling smart healthcare applications through visible light communication networks , Jack Manhardt

Time series anomaly detection using generative adversarial networks , Shyam Sundar Saravanan

Theses from 2023 2023

DYNAMIC DISCOUNTED SATISFICING BASED DRIVER DECISION PREDICTION IN SEQUENTIAL TAXI REQUESTS , Sree Pooja Akula

MAT: Genetic Algorithms Based Multi-Objective Adversarial Attack on Multi-Task Deep Neural Networks , Nikola Andric

COMPUTER VISION IN ADVERSE CONDITIONS: SMALL OBJECTS, LOW-RESOLUTION IMAGES, AND EDGE DEPLOYMENT , Raja Sunkara

Theses from 2022 2022

Maximising social welfare in selfish multi-modal routing using strategic information design for quantal response travelers , Sainath Sanga

Man-in-the-Middle Attacks on MQTT based IoT networks , Henry C. Wong

Theses from 2021 2021

Biochemical assay invariant attestation for the security of cyber-physical digital microfluidic biochips , Fredrick Eugene Love II

Theses from 2020 2020

On predicting stopping time of human sequential decision-making using discounted satisficing heuristic , Mounica Devaguptapu

Theses from 2019 2019

Advanced techniques for improving canonical genetic programming , Adam Tyler Harter

Evolved parameterized selection for evolutionary algorithms , Samuel Nathan Richter

Design and implementation of applications over delay tolerant networks for disaster and battlefield environment , Karthikeyan Sachidanandam

Theses from 2018 2018

Mixed-criticality real-time task scheduling with graceful degradation , Samsil Arefin

CARD: Concealed and remote discovery of IoT devices in victims' home networks , Sammie Lee Bush

Multiple security domain non deducibility in the FREEDM smart grid infrastructure , Manish Jaisinghani

Reputation and credit based incentive mechanism for data-centric message delivery in delay tolerant networks , Himanshu Jethawa

Solidification rate detection through solid-liquid interface tracking , Wei Luo

Cloud transactions and caching for improved performance in clouds and DTNs , Dileep Mardham

Cyber-physical security of an electric microgrid , Prashanth Palaniswamy

An approach for formal analysis of the security of a water treatment testbed , Sai Sidharth Patlolla

Analyzing large scale trajectory data to identify users with similar behavior , Tyler Clark Percy

Precise energy efficient scheduling of mixed-criticality tasks & sustainable mixed-criticality scheduling , Sai Sruti

A network tomography approach for traffic monitoring in smart cities , Ruoxi Zhang

Improved CRPD analysis and a secure scheduler against information leakage in real-time systems , Ying Zhang

Theses from 2017 2017

Cyber-physical security of a chemical plant , Prakash Rao Dunaka

UFace: Your universal password no one can see , Nicholas Steven Hilbert

Multi stage recovery from large scale failure in interdependent networks , Maria Angelin John Bosco

Multiple security domain model of a vehicle in an automated vehicle system , Uday Ganesh Kanteti

Personalizing education with algorithmic course selection , Tyler Morrow

Decodable network coding in wireless network , Junwei Su

Multiple security domain nondeducibility air traffic surveillance systems , Anusha Thudimilla

Theses from 2016 2016

Automated design of boolean satisfiability solvers employing evolutionary computation , Alex Raymond Bertels

Care-Chair: Opportunistic health assessment with smart sensing on chair backrest , Rakesh Kumar

Theses from 2015 2015

Dependability analysis and recovery support for smart grids , Isam Abdulmunem Alobaidi

Sensor authentication in collaborating sensor networks , Jake Uriah Bielefeldt

Argumentation based collaborative software architecture design and intelligent analysis of software architecture rationale , NagaPrashanth Chanda

A Gaussian mixture model for automated vesicle fusion detection and classification , Haohan Li

Hyper-heuristics for the automated design of black-box search algorithms , Matthew Allen Martin

Aerial vehicle trajectory design for spatio-temporal task satisfaction and aggregation based on utility metric , Amarender Reddy Mekala

Design and implementation of a broker for cloud additive manufacturing services , Venkata Prashant Modekurthy

Cyber security research frameworks for coevolutionary network defense , George Daniel Rush

Energy disaggregation in NIALM using hidden Markov models , Anusha Sankara

Theses from 2014 2014

Crime pattern detection using online social media , Raja Ashok Bolla

Energy efficient scheduling and allocation of tasks in sensor cloud , Rashmi Dalvi

A cloud brokerage architecture for efficient cloud service selection , Venkata Nagarjuna Dondapati

Access control delegation in the clouds , Pavani Gorantla

Evolving decision trees for the categorization of software , Jasenko Hosic

M-Grid : A distributed framework for multidimensional indexing and querying of location based big data , Shashank Kumar

Privacy preservation using spherical chord , Doyal Tapan Mukherjee

Top-K with diversity-M data retrieval in wireless sensor networks , Kiran Kumar Puram

On temporal and frequency responses of smartphone accelerometers for explosives detection , Srinivas Chakravarthi Thandu

Efficient data access in mobile cloud computing , Siva Naga Venkata Chaitanya Vemulapalli

An empirical study on symptoms of heavier internet usage among young adults , SaiPreethi Vishwanathan

Theses from 2013 2013

Sybil detection in vehicular networks , Muhammad Ibrahim Almutaz

Argumentation placement recommendation and relevancy assessment in an intelligent argumentation system , Nian Liu

Security analysis of a cyber physical system : a car example , Jason Madden

Efficient integrity verification of replicated data in cloud , Raghul Mukundan

Search-based model summarization , Lokesh Krishna Ravichandran

Hybridizing and applying computational intelligence techniques , Jeffery Scott Shelburg

Secure design defects detection and correction , Wenquan Wang

Theses from 2012 2012

Robust evolutionary algorithms , Brian Wesley Goldman

Semantic preserving text tepresentation and its applications in text clustering , Michael Howard

Vehicle path verification using wireless sensor networks , Gerry W. Howser

Distributed and collaborative watermarking in relational data , Prakash Kumar

Theses from 2011 2011

A social network of service providers for trust and identity management in the Cloud , Makarand Bhonsle

Adaptive rule-based malware detection employing learning classifier systems , Jonathan Joseph Blount

A low-cost motion tracking system for virtual reality applications , Abhinav Chadda

Optimization of textual affect entity relation models , Ajith Cherukad Jose

MELOC - memory and location optimized caching for mobile Ad hoc networks , Lekshmi Manian Chidambaram

A framework for transparent depression classification in college settings via mining internet usage patterns , Raghavendra Kotikalapudi

An incentive based approach to detect selfish nodes in Mobile P2P network , Hemanth Meka

Location privacy policy management system , Arej Awodha Muhammed

Exploring join caching in programming codes to reduce runtime execution , Swetha Surapaneni

Theses from 2010 2010

Event detection from click-through data via query clustering , Prabhu Kumar Angajala

Population control in evolutionary algorithms , Jason Edward Cook

Dynamic ant colony optimization for globally optimizing consumer preferences , Pavitra Dhruvanarayana

EtherAnnotate: a transparent malware analysis tool for integrating dynamic and static examination , Joshua Michael Eads

Representation and validation of domain and range restrictions in a relational database driven ontology maintenance system , Patrick Garrett. Edgett

Cloud security requirements analysis and security policy development using a high-order object-oriented modeling technique , Kenneth Kofi Fletcher

Multi axis slicing for rapid prototyping , Divya Kanakanala

Content based image retrieval for bio-medical images , Vikas Nahar

2-D path planning for direct laser deposition process , Swathi Routhu

Contribution-based priority assessment in a web-based intelligent argumentation network for collaborative software development , Maithili Satyavolu

An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems , Shivakar Vulli

Intelligent computational argumentation for evaluating performance scores in multi-criteria decision making , Rubal Wanchoo

Minimize end-to-end delay through cross-layer optimization in multi-hop wireless sensor networks , Yibo Xu

Theses from 2009 2009

Information flow properties for cyber-physical systems , Rav Akella

Exploring the use of a commercial game engine for the development of educational software , Hussain Alafaireet

Automated offspring sizing in evolutionary algorithms , André Chidi Nwamba

Theses from 2008 2008

Image analysis techniques for vertebra anomaly detection in X-ray images , Mohammed Das

Cross-layer design through joint routing and link allocation in wireless sensor networks , Xuan Gong

A time series classifier , Christopher Mark Gore

An economic incentive based routing protocol incorporating quality of service for mobile peer-to-peer networks , Anil Jade

Incorporation of evidences in an intelligent argumentation network for collaborative engineering design , Ekta Khudkhudia

PrESerD - Privacy ensured service discovery in mobile peer-to-peer environment , Santhosh Muthyapu

Co-optimization: a generalization of coevolution , Travis Service

Critical infrastructure protection and the Domain Name Service (DNS) system , Mark Edward Snyder

Co-evolutionary automated software correction: a proof of concept , Joshua Lee Wilkerson

Theses from 2007 2007

A light-weight middleware framework for fault-tolerant and secure distributed applications , Ian Jacob Baird

Symbolic time series analysis using hidden Markov models , Nikhil Bhardwaj

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Thesis Examples

Latex Example (shortened M.Sc. with urthesis.sty)  (ZIP)

Latex Example (complete M.Sc. with no .sty)  (ZIP)

How to Write a M.Sc. Thesis

The following guide to writing an M.Sc. thesis was prepared by Howard Hamilton and Brien Maguire, based on previous guides by Alan Mackworth (University of British Columbia) and Nick Cercone (Simon Fraser University), with their permission.

Quick Guide to the M.Sc. Thesis

An acceptable M.Sc. thesis in Computer Science should attempt to satisfy one or more of the following criteria:

• Original research results are explained clearly and concisely.
• The thesis explains a novel exploratory implementation or a novel empirical study whose results will be of interest to the Computer Science community in general and to a portion of the Computer Science community in particular, e.g., Artificial Intelligence, Computational Complexity, etc.
• Novel implementation techniques are outlined, generalized, and explained.
• Theoretical results are obtained, explained, proven, and (worst, best, average) case analysis is performed where applicable.
• The implementation of a practical piece of nontrivial software whose availability could have some impact on the Computer Science community. Examples are a distributed file system for a mobile computing environment and a program featuring the application of artificial intelligence knowledge representation and planning techniques to intelligent computer assisted learning software.

Writing an acceptable thesis can be a painful and arduous task, especially if you have not written much before. A good methodology to follow, immediately upon completion of the required courses, is to keep a paper or electronic research notebook and commit to writing research oriented notes in it every day. From time to time, organize or reorganize your notes under headings that capture important categories of your thoughts. This journal of your research activities can serve as a very rough draft of your thesis by the time you complete your research. From these notes to a first M.Sc. thesis draft is a much less painful experience than to start a draft from scratch many months after your initial investigations. To help structure an M.Sc. thesis, the following guide may help.

One Formula for an M.Sc. Thesis for Computer Science

Chapter 1 Introduction: This chapter contains a discussion of the general area of research which you plan to explore in the thesis. It should contain a summary of the work you propose to carry out and the motivations you can cite for performing this work. Describe the general problem that you are working towards solving and the specific problem that you attempt to solve in the thesis. For example, the general problem may be finding an algorithm to help an artificial agent discover a path in a novel environment, and the specific problem may be evaluating the relative effectiveness and efficiency of five particular named approaches to finding the shortest path in a graph where each node is connected to at most four neighbours, with no knowledge of the graph except that obtained by exploration. This chapter should also explain the motivations for solving each of the general problem and your specific problem. The chapter should end with a guide to the reader on the composition and contents of the rest of the thesis, chapter by chapter. If there are various paths through the thesis, these should also be explained in Chapter 1.

Chapter 2 Limited Overview of the Field: This chapter contains a specialized overview of that part of a particular field in which you are doing M.Sc. thesis research, for example, paramodulation techniques for automated theorem proving or bubble figure modelling strategies for animation systems. The survey should not be an exhaustive survey but rather should impose some structure on your field of research endeavour and carve out your niche within the structure you impose. You should make generous use of illustrative examples and citations to current research.

Chapter 3 My Theory/Solution/Algorithm/Program: This chapter outlines your proposed solution to the specific problem described in Chapter 1. The solution may be an extension to, an improvement of, or even a disproof of someone else's theory / solution / method / ...).

Chapter 4 Description of Implementation or Formalism: This chapter describes your implementation or formalism. Depending on its length, it may be combined with Chapter 3. Not every thesis requires an implementation. Prototypical implementations are common and quite often acceptable although the guiding criterion is that the research problem must be clearer when you've completed your task than it was when you started!

Chapter 5 Results and Evaluation: This chapter should present the results of your thesis. You should choose criteria by which to judge your results, for example, the adequacy, coverage, efficiency, productiveness, effectiveness, elegance, user friendliness, etc., and then clearly, honestly and fairly adjudicate your results according to fair measures and report those results. You should repeat, whenever possible, these tests against competing or previous approaches (if you are clever you will win hands down in such comparisons or such comparisons will be obviated by system differences). The competing or previous approaches you compare against must have been introduced in Chapter 2 (in fact that may be the only reason they actively appear in Chapter 2) and you should include pointers back to Chapter 2. Be honest in your evaluations. If you give other approaches the benefit of the doubt every time, and develop a superior technique, your results will be all the more impressive.

Chapter 6 Conclusions: This chapter should summarize the achievements of your thesis and discuss their impact on the research questions you raised in Chapter 1. Use the distinctive phrasing "An original contribution of this thesis is" to identify your original contributions to research. If you solved the specific problem described in Chapter 1, you should explicitly say so here. If you did not, you should also make this clear. You should indicate open issues and directions for further or future work in this area with your estimates of relevance to the field, importance and amount of work required.

References Complete references for all cited works. This should not be a bibliography of everything you have read in your area.

Appendices include technical material (program listings, output, graphical plots of data, detailed tables of experimental results, detailed proofs, etc.) which would disrupt the flow of the thesis but should be made available to help explain or provide details to the curious reader.

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Degree Requirements

Master of science.

The Master of Science (MSc) in Computing Science is a research-intensive program that has a primary emphasis on the MSc thesis. The program provides an environment for education in theoretical and applied Computer Science. Through training in formal coursework and hands-on research in areas such as artificial intelligence, computer systems and networks, computer graphics, and data mining, graduates will be capable of working with integrity to design, improve, and apply cutting-edge computational techniques to support a career in academia, industry or the public sector.

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Computer Science - M.Sc. program

M.sc. in computer science.

The thesis-based Master of Science in Computer Science program provides students with skills and knowledge necessary to pursue doctoral studies or a professional career in the rapidly changing computing and technology fields and in the rapidly advancing field of artificial intelligence, considering the MSc in CS – Artificial Intelligence stream outlined below. This is done through course work in specialized areas of computing technology, applications, and theory, as well as participation in research-oriented seminars, and culminates in an original research thesis.

Students pursuing the thesis-based Master of Science in Computer Science program may optionally elect to seek employment through Co-operative education.

M.Sc. in Computer Science - Artificial Intelligence Stream

The thesis-based Master of Science in Computer Science program in Artificial Intelligence stream is endorsed by the Vector Institute in Artificial Intelligence. This program provides students with skills and knowledge necessary to pursue doctoral studies or a professional career in the rapidly changing computing, technology and artificial intelligence fields. This is done through course work in specialized areas of artificial intelligence, computing technology, applications, and theory, as well as participation in research-oriented seminars, and culminating in an original research thesis with an artificial intelligence topic.

Note: Initial admission to the Master of Science in Computer Science program is required, i.e., both Computer Science (MSc) (with and without Co-op). Once an offer of admission to the MSc in CS program is accepted, students must complete an internal application to the MSc in AI stream.

Co-operative Education & Workplace Partnerships Information

• Students in the MSc in Computer Science and MSc in Computer Science-AI stream may undertake a four-month or eight-month work placement and are anticipated to gain practical knowledge of the state-of-the-art technologies of software development and/or artificial intelligence by working in software companies in Canada and abroad.
• Some examples of companies hiring our MSc Co-op students include IBM, Blackberry, among other top high-tech companies.

Refer to the admission eligibility and graduation requirements in the Graduate Calendar link below (under “Learn More” ) .

Program Specific Admission Requirements

Deadline for Fall: 

April 15 

Deadline for Winter:

August 15 

Deadline for Summer:

December 15

Minimum TOEFL: IBT-92 (a minimum of 23 in each band)

Minimum IELTS: 6.5, with no individual band score below 6.0 and no more than one band score of 6.0.

Minimum Pearson: 65

Additional Requirements

Applicants must submit the School of Computer Science  MSc Study Plan/Research Statement Form  in their application.  (*Failure to submit the required template will result in delays in processing of your application)  500 words or less. Indicate the name of potential supervisor(s), and area(s) of research interest.

Resume or Curriculum Vitae.

Two academic referee assessments.

General admission requirements for all Graduate Programs

Scholarships and Funding

Request More Information  Graduate Academic Calendar Program and Course Descriptions   Visit Computer Science Apply to this Program

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Master's Project Reports

  Chen, Catherine

  Chen, Yiwen and Ren, Jiahao

  Chernosky, Brynn

  Christou, Neophytes

  Dekle, Max

  Demetci, Pinar

  Fu, Changcheng

  Fu, Haotian

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  Golovanevsky, Michal

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  Luo, Calvin

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  Peng, Kathy

  Ramesh, Dev

  Ryjikov, Benjamin

  Scherick, James

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  Zhou, Peisen

  Zhou, Tongyu

  Zhuo, Wang

Computer Science at Brown University Providence, Rhode Island 02912 USA Phone: 401-863-7600 Map & Directions / Contact Us

College of Arts & Sciences

M.S. Program

Admission to the m.s. program.

The M.S. program accepts applications annually to begin study in either the Fall or Spring semester. Information about the application process may be found below or by visiting our FAQ for Prospective Students . Applications are accepted online beginning in mid-July. Visit the Graduate School application page to begin an application.

• Applications for Spring program start : The final submission deadline is October 1 . The admitted students are notified in early November.
• Applications for Fall program start : Priority submission deadline is January 15 , and the final submission deadline: April 1 . The admissions committee begins reviewing applications after the priority deadline. Generally, applications received before March 1 will receive an admission decision by April 1.

Admission Requirements

Successful M.S. applications will hold a B.S. in computer science or a closely-related field or will have significant coursework or professional experience in computer science. A background in computer science topics including data structures and algorithms, hardware and architecture, and computer programming; as well as mathematics including discrete mathematics, probability and statistics, linear algebra, and calculus are expected.

All applicants must meet the requirements specified by the Graduate School in their  application FAQ . Additionally, our program requires a TOEFL score of at least 90 or an IELTS score of at least 7.0 to apply.

M.S. Program Requirements and Milestones

The Computer Science M.S. program provides students with two options to deepen their understanding of computer science topics: the coursework option and the thesis option. All M.S. students are initially admitted under the coursework option but may elect the thesis option by selecting an M.S. thesis adviser.

The information below is an overview of these requirements. The official requirements, procedures, and policies are kept in the  Graduate Student Handbook .

M.S. Coursework Option

Students electing the M.S. coursework option must complete 30 credits (10 courses) at the graduate level. The coursework includes taking a graduate-level algorithms course as well as a course chosen from the “systems” set detailed on this page.

Students typically complete the M.S. coursework option in two years; the timeline below is based on a typical two-year course of study. Note: Georgetown considers 9 credits (3 courses) to be a full-time course load for enrollment and visa purposes.

M.S. Thesis Option

Students electing the M.S. thesis option must complete 24 credits (8 courses) at the graduate level. The coursework includes a graduate-level algorithms course as well as a course chosen from the “systems” set detailed on this page. In addition, M.S. thesis students must write and defend a master’s thesis containing novel research.

Students typically complete the M.S. thesis option in two years; the timeline below is based on a typical two-year course of study.

Elective Coursework

M.S. students may select as their elective coursework any CS course taught at the graduate level. The department regularly offers courses across multiple disciplines of computer science theory, computer and network security, and data management and processing.

Students may also elect to take up to two external electives as part of their coursework, subject to the approval of the department graduate committee. These external electives may be chosen from graduate-level courses in other departments at Georgetown or may be taken at other D.C. area universities as part of the Consortium of Universities of the Washington Metropolitan Area .

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Master of Science (M.Sc.) in Computer Science

[Note: The M.Sc. programs have undergone a revision starting Fall 2020. The main change is a reduction in the course credit requirements and an increase in the research credit requirements. Students who began the M.Sc. program prior to Fall 2020 may follow the requirements of the new program if they wish.]

We offer two M.Sc. programs - the Thesis and Non-Thesis. The Non-Thesis program will be sometimes referred to as the Project option since it substitutes a project (and additional courses) for a thesis. Both programs are designed to take between 1.5 and 2 years. The maximum allowable is 3 years. Students begin in the Thesis program, and may switch to the Project option any time after their second semester.

Students intending to pursue a Ph.D. after the M.Sc. should follow the Thesis program rather than the Non-Thesis program. Alternatively, students may apply to be fast-tracked to the Ph.D. program without completing the M.Sc.. Such applicants must have completed a minimum of two and a maximum of four full-time semesters, according to GPS rules. For more information, see the bottom of this web page.

Students in either M.Sc. program have a minimum residence requirement of three full-time semesters. Students may register for the Summer semester if they wish to complete their residence requirements. For further details on student status, see here .

Students should take a minimum of two Complementary courses in their first semester and should complete all four Complementary courses by the end of their second semester. In addition, students in their first two semesters should take the Seminar courses COMP 602 (Fall) and 603 (Winter).

Here is a brief summary of the requirements of the two M.Sc. programs. Both programs require:

• three full-time terms of residence
• two seminar courses COMP 602 and 603
• a total of at least 45 credits

In addition, the Thesis program requires:

• at least 14 credits of COMP (or approved) Complementary coursesat the 500 level or higher, which satisfy a Breadth Requirement (see below)
• a thesis with significant scholarly content

and the Non-Thesis program requires:

• at least 28 credits of COMP (or approved) Complementary courses at the 500 level or higher, which satisfy a Breadth Requirement (see below);
• a research project (see guidelines )

Further details on the two programs including the course Breadth Requirement, the Letter of Understanding agreement between student and supervisor, and the Progress Report are given below.

M.Sc. Computer Science (Thesis) (45 credits)

Thesis courses (29 credits).

At least 29 credits selected from:

• COMP 691 Thesis Research 1 (3 credits)
• COMP 696 Thesis Research 2 (3 credits)
• COMP 697 Thesis Research 3 (4 credits)
• COMP 698 Thesis Research 4 (10 credits)
• COMP 699 Thesis Research 5 (12 credits)

Required Courses (2 credits)

• COMP 602 Computer Science Seminar 1 (1 credit)
• COMP 603 Computer Science Seminar 2 (1 credit)

Complementary Courses (14 credits)

At least 14 credits of COMP (or approved by MSc Graduate Program Director) courses at the 500-, 600-, or 700-level. Complementary courses must satisfy a Computer Science Breadth Requirement, with at least one course in two of the Theory, Systems, and Application areas.

Course Breadth Requirement

Courses must be taken from at least two of the three categories below (Theory, Systems, and Applications). The category of any course not listed below such as a new course or a 500 level Topics courses follows the general pattern of the existing courses. In cases of doubt, students should contact the Computer Science Graduate (M.Sc.) Program Director.

Category A: Theory

COMP 523 Language-based Security (3 credits) COMP 524 Theoretical Foundations of Programming Languages (3 credits) COMP 525 Formal Verification (3 credits) COMP 527 Logic and Computation COMP 531 Advanced Theory of Computation (3 credits) COMP 540 Matrix Computations (4 credits) COMP 547 Cryptography and Data Security (4 credits) COMP 552 Combinatorial Optimization (4 credits) COMP 553 Algorithmic Game Theory (4 credits) COMP 554 Approximation Algorithms (4 credits) COMP 560 Graph Algorithms and Applications (3 credits) COMP 566 Discrete Optimization 1 (3 credits) COMP 567 Discrete Optimization 2 (3 credits) COMP 610 Information Structures 1 (4 credits) COMP 627 Theoretical Programming Languages (4 credits) COMP 642 Numerical Estimation Methods (4 credits) COMP 647 Advanced Cryptography (4 credits) COMP 649 Quantum Cryptography (4 credits) COMP 690 Probabilistic Analysis of Algorithms (4 credits) COMP 760 Advanced Topics Theory 1 (4 credits) COMP 761 Advanced Topics Theory 2 (4 credits)

Category B: Systems

COMP 512 Distributed Systems (4 credits) COMP 520 Compiler Design (4 credits) COMP 529 Software Architecture (4 credits) COMP 533 Model-Driven Software Development (3 credits) COMP 535 Computer Networks 1 (4 credits) COMP 575 Fundamentals of Distributed Algorithms (3 credits) COMP 612 Database Programming Principles (4 credits) COMP 614 Distributed Data Management (4 credits) COMP 621 Program Analysis and Transformations (4 credits) COMP 655 Distributed Simulation (4 credits) COMP 667 Software Fault Tolerance (4 credits) COMP 762 Advanced Topics Programming 1 (4 credits) COMP 763 Advanced Topics Programming 2 (4 credits) COMP 764 Advanced Topics Systems 1 (4 credits) COMP 765 Advanced Topics Systems 2 (4 credits)

Category C: Applications

COMP 521 Modern Computer Games (4 credits) COMP 522 Modellin and Simulation (4 credits) COMP 526 Probabilistic Reasoning and AI (3 credits) COMP 546 Computational Perception (4 credits) COMP 550 Natural Language Processing (3 credits) COMP 551 Applied Machine Learning (4 credits) COMP 557 Fundamentals of Computer Graphics (4 credits) COMP 558 Fundamentals of Computer Vision (4 credits) COMP 559 Fundamentals of Computer Animation (4 credits) COMP 561 Computational Biology Methods and Research (4 credits) COMP 564 Advanced Computational Biology Methods and Research (3 credits) COMP 579 Reinforcement Learning (4 credits) COMP 618 Bioinformatics: Functional Genomics (3 credits) COMP 680 Mining Biological Sequences (4 credits) COMP 652 Machine Learning (4 credits) COMP 766 Advanced Topics Applications 1 (4 credits) COMP 767 Advanced Topics: Applications 2 (4 credits)

M.Sc. Computer Science (Non-Thesis) (45 credits)

Research project courses (15 credits).

• COMP 693 Research Project 1 (3 credits)
• COMP 694 Research Project 2 (6 credits)
• COMP 695 Research Project 3 (6 credits)

Students who have taken any Thesis Research (1-5) courses prior to switching to the Non-Thesis program and who wish to use these credits (instead of Research Project course credits) toward their M.Sc. Non-Thesis program should contact the M.Sc. Graduate Program Director.

Complementary Courses (28 credits)

At least 28 credits of COMP (or approved by MSc Graduate Program Director) courses including at least three 4-credit courses at the 500, 600, or 700 level. The courses must meet the same Breadth Requirement as in the Thesis program (see above), namely courses must be from at least two of the three areas of Theory, Systems, and Applications.

Letter of Understanding

The letter of understanding must be filled by the student and the supervisor(s) at the initial meeting and signed by both. This letter of understanding must be uploaded by the student into MyProgress. If there are significant changes in the understanding, a new letter can be created and uploaded.

Annual Progress Report

Each student must meet annually with his/her supervisor or co-supervisors to assess the progress made during the previous year, and describe plans for the coming year. The progress form below must be filled by the student, discussed with the supervisor, and signed by both. A progress form must be filled each year (except the first year) before September 30th, and submitted to Ann Jack.

Annual Progress Form (PDF document)

Fast-tracking from the M.Sc. Thesis to the Ph.D. program

Excellent M.Sc. students who would like to pursue doctoral studies can apply to be "fast-tracked" to the Ph.D. program, after having completed a minimum of two and maximum of four full time semesters of the MSc Thesis program. Each fast-tracking application will be evaluated by the Ph.D. committee, in concert with the proposed Ph.D. supervisor, on a case-by-case basis. Evaluation criteria will include excellence of the academic record and achievements in research. M.Sc. students interested in fast-tracking to the Ph.D. program should discuss this option with their supervisor.

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© McGill University 2024 Credits

The M.S. Thesis Track

The MS Thesis track is for students who want to concentrate on research in some sub-field of Computer Science.  You are required to arrange for a Computer Science Faculty member who agrees to advise the thesis and the rest of your course selection prior to selecting the track.

SUMMARY OF REQUIREMENTS

• Complete a total of  30 points  (Courses must be at the 4000 level or above)
• Maintain at least a  2.7  overall GPA. (No more than 1 D is permitted).
• Complete the  Columbia Engineering Professional Development & Leadership (PDL)  requirement
• Satisfy  breadth requirements
• Take at least  6 points  of technical courses at the 6000 level
• At most, up to 3 points  of your degree can be Non-CS/Non-track If they are deemed relevant to your track and sufficiently technical in nature. Submit the  Non-CS/NonTrack form  and the course syllabus to your CS Faculty Advisor for review

1. BREADTH REQUIREMENT

Visit the breadth requirement page for more information.

2. REQUIRED TRACK COURSES (9 credits)

Students must take 9 credits of COMS E6902 Thesis. The points are typically spread over multiple semesters, e.g., 3 points each for 3 semesters or 4.5 points each for 2 semesters. No more than 9 points of E6902 may be taken. Sign up for the section number of E6902 associated with your thesis advisor.

3. ELECTIVE TRACK COURSES

Students are required to complete 9 elective credits of graduate courses (4000-level or above) selected from Computer Science and/or related areas together with your faculty thesis advisor. These would normally be strongly related to your thesis topic.

Up to 3 of these points may be in COMS E6901 Projects in Computer Science.

Please note:

The  degree progress checklist should be used to keep track of your requirements. if you have questions for your track advisor or cs advising, you should have an updated checklist prepared, due to a significant overlap in course material, ms students not in the machine learning track can only take 1 of the following courses – coms 4771, coms 4721, elen 4903, ieor 4525, stat 4240, stat 4400/4241/5241 – as part of their degree requirements, the elective track courses cannot be imported from another institution., 4. general electives.

Students must complete the remaining credits of General Elective Courses at the 4000 level or above. At least three of these points must be chosen from either the Track Electives listed above or from the CS department at the 4000 level or higher.

Students may also request to use at most 3 points of Non-CS/Non-Track coursework if approved by the process listed below.

5. THESIS DEFENSE

A thesis proposal is presented to your thesis committee at least three months before your defense. Your thesis committee should have three members. Two of them must be internal, but one can be an outsider. Please bring the thesis defense form to your defense. Once completed, please submit the form to CS Advising via email: [email protected].

The thesis cannot be imported from another institution.

A publication-quality thesis document is also published as a CS department technical report. Once completed, please upload your thesis into MICE.

PROGRAM PLANNING

Please visit  the Directory of Classes  to get the updated course listings. Please also note that not all courses are offered every semester or even every year. A few courses are offered only once every two or three years or even less frequently.

Updated: 3/26/2024

Find open faculty positions here .

Computer Science at Columbia University

Upcoming events, in the news, press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

Mary C. Boyce Dean of Engineering Morris A. and Alma Schapiro Professor

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Computer Science - MMath at Waterloo

Program information.

Watch the How to apply to Waterloo graduate studies video

What does it take to get in?

Minimum admission requirements.

• An Honours Bachelor degree in Computer Science or Engineering (or equivalent degree) with at least a 78% standing.

Supervisors

• Review the finding a supervisor resources
• Applicants do not need to have a confirmed supervisor before applying. If offered admission, a supervisor  will be assigned at that time 

Application materials

• The SIF contains questions specific to your program, typically about why you want to enrol and your experience in that field. Review the  application documents web page for more information about this requirement
• If a statement or letter is required by your program, review the  writing your personal statement resources  for helpful tips and tricks on completion

Transcript(s)

• Three  references are required; at least two academic
• TOEFL 93 (writing 22, speaking 22), IELTS 6.5 (writing 6.0, speaking 6.5)

How much will it cost?

• Use the student budget calculator to estimate your cost and resources
• Visit the  graduate program tuition page  on the Finance website to determine the tuition and incidental fees per term for your program
• Review the  study and living costs
• Review the funding graduate school resources for graduate students

What can you expect at Waterloo?

• Review the degree requirements in the Graduate Studies Academic Calendar, including the courses that you can anticipate taking as part of completing the degree
• Students have the option to apply to transfer to a co-op option after beginning this program
• Check out profiles of current graduate students to learn about their experience at Waterloo
• Check out Waterloo's institutional thesis repository - UWspace to see recent submissions from the David R. Cheriton School of Computer Science graduate students
• Check out the Waterloo campus and city tours
• Review the  David R. Cheriton School of Computer Science  website to see information about supervisors, research areas, news, and events

This program page is effective September 2023; it will be updated annually. Any changes to the program page following this date will be indicated with a notation. 

We strive to provide you with the necessary information on each of our program pages.  Was there something you found helpful?  Was there anything missing?  Share your thoughts .

Program Contact

Computer Science Graduate Office

Graduate Studies and Postdoctoral Affairs (GSPA)

Needles Hall, second floor, room 2201

Graduate Studies Academic Calendar

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The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land granted to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is co-ordinated within the Office of Indigenous Relations .

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Computer Science

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Program Type

• Master of Science in Computer Science (MSc)
• Master of Science in Computer Science (MSc) - Co-operative Option

The application is closed to International applicants. 

You will need to provide your unofficial documents: final transcript, proof of graduation, CV, Statement of Purpose, proof of English language, and the Computer Science Background Form.

Computer Science combines the intellectual challenge of a young discipline with the excitement of an innovative and rapidly expanding technology.  

Computer science has been an active area at Lakehead University for over 25 years. The department resides in the University's Advanced Technology and Academic Centre ( ATAC ), a striking building that houses many of the University’s computing activities. Faculty, staff, and students are equipped with state-of-the-art computing facilities including smart lecture rooms, labs, and a variety of computing technologies (e.g. Virtual Reality, Parallel Computing).

The Master's program in computer science provides an opportunity to explore the breadth and depth of advanced knowledge in the discipline. Students benefit from a selection of advanced courses and a chance to pursue research that aligns with their interests and aspirations.

Three program options are available to students:

• Master’s by Course (1 year), which involves courses aligned with the IT industry requirements
• Master's by Project (2 years), which involves courses aligned with the IT industry needs and also includes a limited research project
• Master's by Thesis (2 years), which requires fewer courses and involves a more substantial research project

The Course-based master's program is not suitable for pursuing higher graduate programs (e.g. PhD). The Project and Thesis master's programs prepare students for higher graduate programs (e.g. PhD) and may provide them with further industrial experience through available Co-op placement opportunities.

Specialization in Artificial Intelligence

The Department of Computer Science is offering a two-year, thesis-based Master of Science in Computer Science (MSc) program with a specialization in Artificial Intelligence (AI).

Students will develop the skills and knowledge to conduct research in the field of Computer Science with a focus on core AI techniques. Upon completion, students will be able to apply and select appropriate AI algorithms and techniques in a variety of industrial sectors and further advance the AI-related research. Topics will include deep learning, natural language processing, machine learning, image processing, pattern recognition, and other emerging technologies. Finally, students will develop research and application-based ethical awareness.

Please note: The AI Specialization is not eligible for the Co-operative Option.

MSc Computer Science Co-operative Program Option - For Project and Thesis based students, only:

A graduate student will normally be admitted to the co-operative program option after completion of two terms, for a starting date in May. Students are expected to obtain an aggregate of 80% or more and must have taken at least 4 half credit courses (excluding non-credit courses, project and thesis).

Co-op employment for 8 months (two terms) must be successfully completed to  satisfy the co-op requirement for the degree. 

Students interested in a co-op placement should inform the Department's Co-op Advisor at least four months in advance of the proposed date of the placement (e.g., by late December for placements beginning in May). The Department's decision as to the suitability of each candidate will be based primarily on academic performance. Successful candidates will work with the Student Success Centre and the university Co-op Coordinator in their search for suitable employment.

Upon completion of the co-op placement, the student will either complete a thesis (Thesis Program option) or complete the Project Program option requirements including the required project course.

NOTE : Students in this program must complete all requirements within six terms (2 years) of continuous registration.   For co-op students, the duration of the co-op placement will be added to the program time limit.

Admission Requirements for Masters

Applicants for admission must be graduates of a recognized university, college, or institute as well as show evidence of scholarly achievement. Except where otherwise stated in the Admission Requirements of a particular program, degree students must have a four year bachelor's degree or its equivalent with at least second class standing (B) based on their last 20 half courses or equivalent.

For applicants from countries that follow a British-patterned grading system, please note:

• Overall standings are normally reported in lieu of an average. The CGPA can be determined on the basis of annual or overall standings as reported on the transcript or degree certificate.
• A minimum grade of B, or Second Class, Upper Division, is required.
• The National Diploma and Higher National Diploma are not recognized for admission to a graduate-level program.
• Be sure to include a copy of your institution grading scale when uploading your transcripts.
• If in doubt, you may wish to order a WES International Credential Advantage (iCap) report from wes.org

An applicant holding a degree other than one in the discipline area to which admission is sought will be considered on the basis of courses taken and academic standing. A Qualifying Year at the undergraduate level may be required to meet the admission standards. Courses taken as part of a Qualifying Year can not be used as credit towards a graduate degree.

Meeting the minimum requirements does not necessarily guarantee admission. No candidate will be admitted unless the academic unit recommends admission. All applicants will be advised by the Office of Graduate Studies in writing of their admission status.

• View English Language Proficiency Requirements
• View Lakehead University Calendar Disclaimer

Program Specific Requirements

In addition to the general admission requirements for Master programs, the following minimum requirements also apply :

• A student entering the Master's program is expected to have at least a "B" average in an Honours Computer Science program or equivalent from an accredited university and the necessary undergraduate prerequisites for the graduate courses to be completed.
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Academic Fees and Important Payment Information

• General Information about University Fees
• Graduate Tuition Fees
• Convocation Fees & Applying to Graduate
• Other University and Program Fees
• Student Fee and Payment Information
• Refund Schedule

Graduate Funding

At Lakehead University, we realize the importance of financial support for graduate students.

Therefore, financial assistance opportunities are available in several forms and are generally awarded to students by individual programs on the basis of academic promise and financial need.

The different funding options available include:

• Graduate Scholarships, Bursaries, and Awards
• Graduate Assistantships
• Faculty Research Scholarships

For your convenience, a searchable database of graduate scholarships, bursaries, and awards is provided below . Award eligibility, criteria, and application procedures for graduate funding is indicated for each award. Please use the general search tool to find available funding by program. Alternatively, you may also click the advanced search link to specify available funding by program level, award category and/or award amount.

Although financial support cannot be guaranteed to all graduate students in all programs, we encourage you to inquire about financial assistance with your Graduate Coordinator in your program of study . You may also contact the Graduate Funding Officer in the Faculty of Graduate Studies to learn more about your graduate funding options.

Conditions of Graduate Awards

Graduate scholarships are based on academic merit. Graduate bursaries are based on financial need, although there may be a merit component to the bursary. Where the award designates that an application is required, only those students who have submitted the specified application by the deadline will be considered for those awards. Late and/or incomplete applications will not be considered. Only successful applicants will be notified.

Recipients of scholarships, awards and bursaries must be registered in order to receive funding. Graduate awards are applied to any outstanding balance on the student's account. Students are entitled to their awards only after their fees are paid in full. Only students with credit account balances will be refunded the balance of the overpayment. Overpayment refunds of these awards will be issued at the end of September, January and May each year.

The University reserves the right to make changes without prior notice to the terms, conditions and award values listed in this section and in the University Calendar.

The most up-to-date internal awards and applications are on our new award system MyAwards

Graduate Studies Funding Database

Required application documents.

Applicants for admission must be graduates of an accredited university, college, or institute as well as show evidence of scholarly achievement. Except where otherwise stated in the admission requirements of a particular program, domestic degree students must have a four year bachelor's degree or its equivalent with at least (B) based on their last 20 half courses or equivalent. We recommend that International applicants have an overall standing of Second Class - Upper Division or higher.

Meeting the minimum application requirements does not guarantee admission. The Faculty of Graduate Studies will advise all applicants in writing of admission decisions once they are received from the program. Applicants are encouraged to regularly monitor their Lakehead University email and application portal for the most current information.

The first step in the application process is to complete the online graduate studies application form.

After you have submitted the online form along with the required $125 CAD application fee, you will be provided with an online account where you can complete the remaining steps of the application process which include uploading the required supporting documents and monitoring the status of your application.

Click here to Apply to Graduate Studies

After you have applied 

After you have submitted the online application form, you can access your account here . Any change in your application status will be reflected in this portal.

• An electronic reference form will be automatically sent by email to the references you identify on the graduate studies application form
• This form is requested in support of the applicant's ability to undertake advanced study and research
• Click here for information about transcript requirements
• Click here for information about proof of degree requirements
• For a list of program specific documents, please see this program's Additional Application Information section (if required, see above)
• For information about English test results, please see our Academic Calendar

Additional Application Information

Please do not send in your official transcript(s) or proof of degree as these items will be discarded. If you are recommended for admission we will request the documents at that time.

You will need to provide your unofficial documents: final transcript, proof of graduation, CV, Statement of Purpose, proof of English language, three references, and the Computer Science Background Form.

Use the following form to provide a background of your Computer Science experience. Please ensure that you read and follow the instructions on how to fill out the Computer Science Background Form, below. You must submit the Computer Science Background Form with your application, in order to be considered for admission. 

• Instructions on how to complete Computer Science Background Form
• Computer Science Background Form (xlsx)

Registration Procedures

Check to make sure all of your course selections are currently being offered by referring to the University Course Calendar and the University Course Time Tables .

• View Registration Regulations
• View Graduate Course Time Tables
• View How & Where to Register for Courses
• Check Your Eligibility to Register  You should register as soon as you are eligible
• Review the Academic Schedule of Dates for registration deadlines & important dates

University Graduate Studies & General Regulations & Policies

• View University and Graduate Study Regulations, Policies and Guidelines

Application Availability

Fall 2024 (September)

International Application Deadline - March 1st

Domestic Applications are still being accepted.

ScholarWorks

Home > Engineering > Computer Science > Computer Science Graduate Projects

Computer Science Graduate Projects and Theses

Theses/dissertations from 2023 2023.

High-Performance Domain-Specific Library for Hydrologic Data Processing , Kalyan Bhetwal

Verifying Data Provenance During Workflow Execution for Scientific Reproducibility , Rizbanul Hasan

Remote Sensing to Advance Understanding of Snow-Vegetation Relationships and Quantify Snow Depth and Snow Water Equivalent , Ahmad Hojatimalekshah

Exploring the Capability of a Self-Supervised Conditional Image Generator for Image-to-Image Translation without Labeled Data: A Case Study in Mobile User Interface Design , Hailee Kiesecker

Fake News Detection Using Narrative Content and Discourse , Hongmin Kim

Anomaly Detection Using Graph Neural Network , Bishal Lakha

Sparse Format Conversion and Code Synthesis , Tobi Goodness Popoola

Portable Sparse Polyhedral Framework Code Generation Using Multi Level Intermediate Representation , Aaron St. George

Severity Measures for Assessing Error in Automatic Speech Recognition , Ryan Whetten

Theses/Dissertations from 2022 2022

Improved Computational Prediction of Function and Structural Representation of Self-Cleaving Ribozymes with Enhanced Parameter Selection and Library Design , James D. Beck

Meshfree Methods for PDEs on Surfaces , Andrew Michael Jones

Deep Learning of Microstructures , Amir Abbas Kazemzadeh Farizhandi

Long-Term Trends in Extreme Environmental Events with Changepoint Detection , Mintaek Lee

Structure Aware Smart Encoding and Decoding of Information in DNA , Shoshanna Llewellyn

Towards Making Transformer-Based Language Models Learn How Children Learn , Yousra Mahdy

Ontology-Based Formal Approach for Safety and Security Verification of Industrial Control Systems , Ramesh Neupane

Improving Children's Authentication Practices with Respect to Graphical Authentication Mechanism , Dhanush Kumar Ratakonda

Hate Speech Detection Using Textual and User Features , Rohan Raut

Automated Detection of Sockpuppet Accounts in Wikipedia , Mostofa Najmus Sakib

Characterization and Mitigation of False Information on the Web , Anu Shrestha

Sinusoidal Projection for 360° Image Compression and Triangular Discrete Cosine Transform Impact in the JPEG Pipeline , Iker Vazquez Lopez

Theses/Dissertations from 2021 2021

Training Wheels for Web Search: Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom , Garrett Allen

Fair and Efficient Consensus Protocols for Secure Blockchain Applications , Golam Dastoger Bashar

Why Don't You Act Your Age?: Recognizing the Stereotypical 8-12 Year Old Searcher by Their Search Behavior , Michael Green

Ensuring Consistency and Efficiency of the Incremental Unit Network in a Distributed Architecture , Mir Tahsin Imtiaz

Modeling Real and Fake News Sharing in Social Networks , Abishai Joy

Modeling and Analyzing Users' Privacy Disclosure Behavior to Generate Personalized Privacy Policies , A.K.M. Nuhil Mehdy

Into the Unknown: Exploration of Search Engines' Responses to Users with Depression and Anxiety , Ashlee Milton

Generating Test Inputs from String Constraints with an Automata-Based Solver , Marlin Roberts

A Case Study in Representing Scientific Applications ( GeoAc ) Using the Sparse Polyhedral Framework , Ravi Shankar

Actors for the Internet of Things , Arjun Shukla

Theses/Dissertations from 2020 2020

Towards Unifying Grounded and Distributional Semantics Using the Words-as-Classifiers Model of Lexical Semantics , Stacy Black

Improving Scientist Productivity, Architecture Portability, and Performance in ParFlow , Michael Burke

Polyhedral+Dataflow Graphs , Eddie C. Davis

Improving Spellchecking for Children: Correction and Design , Brody Downs

A Collection of Fast Algorithms for Scalar and Vector-Valued Data on Irregular Domains: Spherical Harmonic Analysis, Divergence-Free/Curl-Free Radial Basis Functions, and Implicit Surface Reconstruction , Kathryn Primrose Drake

Privacy-Preserving Protocol for Atomic Swap Between Blockchains , Kiran Gurung

Unsupervised Structural Graph Node Representation Learning , Mikel Joaristi

Detecting Undisclosed Paid Editing in Wikipedia , Nikesh Joshi

Do You Feel Me?: Learning Language from Humans with Robot Emotional Displays , David McNeill

Obtaining Real-World Benchmark Programs from Open-Source Repositories Through Abstract-Semantics Preserving Transformations , Maria Anne Rachel Paquin

Content Based Image Retrieval (CBIR) for Brand Logos , Enjal Parajuli

A Resilience Metric for Modern Power Distribution Systems , Tyler Bennett Phillips

Theses/Dissertations from 2019 2019

Edge-Assisted Workload-Aware Image Processing System , Anil Acharya

MINOS: Unsupervised Netflow-Based Detection of Infected and Attacked Hosts, and Attack Time in Large Networks , Mousume Bhowmick

Deviant: A Mutation Testing Tool for Solidity Smart Contracts , Patrick Chapman

Querying Over Encrypted Databases in a Cloud Environment , Jake Douglas

A Hybrid Model to Detect Fake News , Indhumathi Gurunathan

Suitability of Finite State Automata to Model String Constraints in Probablistic Symbolic Execution , Andrew Harris

UNICORN Framework: A User-Centric Approach Toward Formal Verification of Privacy Norms , Rezvan Joshaghani

Detection and Countermeasure of Saturation Attacks in Software-Defined Networks , Samer Yousef Khamaiseh

Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy , Manish Kumar

Application-Specific Memory Subsystem Benchmarking , Mahesh Lakshminarasimhan

Multilingual Information Retrieval: A Representation Building Perspective , Ion Madrazo

Improved Study of Side-Channel Attacks Using Recurrent Neural Networks , Muhammad Abu Naser Rony Chowdhury

Investigating the Effects of Social and Temporal Dynamics in Fitness Games on Children's Physical Activity , Ankita Samariya

BullyNet: Unmasking Cyberbullies on Social Networks , Aparna Sankaran

FALCON: Framework for Anomaly Detection In Industrial Control Systems , Subin Sapkota

Investigating Semantic Properties of Images Generated from Natural Language Using Neural Networks , Samuel Ward Schrader

Incremental Processing for Improving Conversational Grounding in a Chatbot , Aprajita Shukla

Estimating Error and Bias of Offline Recommender System Evaluation Results , Mucun Tian

Theses/Dissertations from 2018 2018

Leveraging Tiled Display for Big Data Visualization Using D3.js , Ujjwal Acharya

Fostering the Retrieval of Suitable Web Resources in Response to Children's Educational Search Tasks , Oghenemaro Deborah Anuyah

Privacy-Preserving Genomic Data Publishing via Differential Privacy , Tanya Khatri

Injecting Control Commands Through Sensory Channel: Attack and Defense , Farhad Rasapour

Strong Mutation-Based Test Generation of XACML Policies , Roshan Shrestha

Performance, Scalability, and Robustness in Distributed File Tree Copy , Christopher Robert Sutton

Using DNA For Data Storage: Encoding and Decoding Algorithm Development , Kelsey Suyehira

Detecting Saliency by Combining Speech and Object Detection in Indoor Environments , Kiran Thapa

Theses/Dissertations from 2017 2017

Identifying Restaurants Proposing Novel Kinds of Cuisines: Using Yelp Reviews , Haritha Akella

Editing Behavior Analysis and Prediction of Active/Inactive Users in Wikipedia , Harish Arelli

CloudSkulk: Design of a Nested Virtual Machine Based Rootkit-in-the-Middle Attack , Joseph Anthony Connelly

Predicting Friendship Strength in Facebook , Nitish Dhakal

Privacy-Preserving Trajectory Data Publishing via Differential Privacy , Ishita Dwivedi

Cultivating Community Interactions in Citizen Science: Connecting People to Each Other and the Environment , Bret Allen Finley

Uncovering New Links Through Interaction Duration , Laxmi Amulya Gundala

Variance: Secure Two-Party Protocol for Solving Yao's Millionaires' Problem in Bitcoin , Joshua Holmes

A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases , Akshay Kansal

Integrity Coded Databases: Ensuring Correctness and Freshness of Outsourced Databases , Ujwal Karki

Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display , Yuan Li

The Effects of Pair-Programming in a High School Introductory Computer Science Class , Ken Manship

Towards Automatic Repair of XACML Policies , Shuai Peng

Identification of Unknown Landscape Types Using CNN Transfer Learning , Ashish Sharma

Hand Gesture Recognition for Sign Language Transcription , Iker Vazquez Lopez

Learning to Code Music : Development of a Supplemental Unit for High School Computer Science , Kelsey Wright

Theses/Dissertations from 2016 2016

Identification of Small Endogenous Viral Elements within Host Genomes , Edward C. Davis Jr.

When the System Becomes Your Personal Docent: Curated Book Recommendations , Nevena Dragovic

Security Testing with Misuse Case Modeling , Samer Yousef Khamaiseh

Estimating Length Statistics of Aggregate Fried Potato Product via Electromagnetic Radiation Attenuation , Jesse Lovitt

Towards Multipurpose Readability Assessment , Ion Madrazo

Evaluation of Topic Models for Content-Based Popularity Prediction on Social Microblogs , Axel Magnuson

CEST: City Event Summarization using Twitter , Deepa Mallela

Developing an ABAC-Based Grant Proposal Workflow Management System , Milson Munakami

Phoenix and Hive as Alternatives to RDBMS , Diana Ornelas

Massively Parallel Algorithm for Solving the Eikonal Equation on Multiple Accelerator Platforms , Anup Shrestha

A Certificateless One-Way Group Key Agreement Protocol for Point-to-Point Email Encryption , Srisarguru Sridhar

Dynamic Machine Level Resource Allocation to Improve Tasking Performance Across Multiple Processes , Richard Walter Thatcher

Theses/Dissertations from 2015 2015

Developing an Application for Evolutionary Search for Computational Models of Cellular Development , Nicolas Scott Cornia

Accelerated Radar Signal Processing in Large Geophysical Datasets , Ravi Preesha Geetha

Integrity Coded Databases (ICDB) – Protecting Integrity for Outsourced Databases , Archana Nanjundarao

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Master of computer science : [140] Collection home page

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The majority of graduate programs are NOT impacted by recent government announcements about tuition increases. PhD students from the rest of Canada will continue to pay Quebec fees. International PhD fees will see the same 3% increase as Quebec fees.

Computer Science (M.Sc.)

Program description.

The Master of Science (M.Sc.) in Computer Science (Non-Thesis) offered by the School of Computer Science in the Faculty of Science is a course-based program that emphasizes practical and rigorous learning opportunities. The program's objective is to equip students with skills in forward-thinking, data analysis, and information literacy to pursue professional opportunities.

Unique Program Features

• The program is designed for students who want to obtain broad knowledge of advanced topics in computer science without the requirement of completing a thesis;
• The School’s Faculty members conduct research in various areas including artificial intelligence, robotics, machine learning and vision, bioinformatics, systems research, computer science education, software engineering, programming languages, and foundations of computer science;
• The School is one of the leading teaching and research centres for computer science in Canada;
• The program offers an excellent preparation for the job market, though it is not recommended for students interested in eventually pursuing a Ph.D.

University-Level Admission Requirements

• An eligible Bachelor's degree with a minimum 3.0 GPA out of a possible 4.0 GPA
• English-language proficiency

Each program has specific admission requirements including required application documents. Please visit the program website for more details.

Visit our Educational credentials and grade equivalencies and English language proficiency webpages for additional information.

Program Website

MSc in Computer Science website

Department Contact

Graduate Program grad.cs [at] mcgill.ca (subject: MSc%20in%20Computer%20Science%20(Non-Thesis)) (email)

Available Intakes

Application deadlines.

Note : Application deadlines are subject to change without notice. Please check the application portal for the most up-to-date information.

Application Resources

• Application Steps webpage
• Submit Your Application webpage

Application Workshops

Consult our full list of our virtual application-focused workshops on the Events webpage .

Department and University Information

Graduate and postdoctoral studies.