I am an MMath student in Computer Science at the University of Waterloo. My interests lie in intelligent software systems, rigorous approaches to correctness, and the engineering of reliable distributed applications.
I am a Master of Mathematics (MMath) student in Computer Science at the University of Waterloo, where I work as a Graduate Research Assistant. My academic focus spans artificial intelligence, formal methods, and distributed systems.
My interests are motivated by a central question: how can we build increasingly intelligent and complex software while still having confidence in its behaviour? I am interested in both learning-based approaches and mathematically grounded techniques for understanding system reliability.
Before joining Waterloo, I completed a B.Sc. in Computer Science with a minor in Mathematics at the University of Manitoba. My undergraduate research explored provenance-based security analysis, including graph representations of system activity and machine learning for anomaly detection.
Alongside my academic research, I have gained industry experience in software engineering, working on the design, development, and optimization of real-world software systems. These experiences have strengthened my interest in scalable architectures, distributed systems, and building reliable, efficient software solutions.
Machine learning and language models offer new ways to interpret information, identify patterns, and assist with complex computational tasks. I am interested in their capabilities, limitations, and responsible use within software systems.
Formal methods provide mathematical techniques to specify and analyze system behaviour. I am interested in verification, automated reasoning, and methods for establishing whether software satisfies important correctness and safety properties.
Distributed applications must coordinate multiple components despite concurrency, communication delays, and failures. I am interested in the design, analysis, and reliability of systems that operate across interconnected services.
Converted system logs into provenance graphs to study security-relevant behaviour. Applied graph neural networks, Word2Vec, and supervised machine learning to detect anomalous and malicious activity.
Created an interactive visualization of FIFO, shortest-job-first, and round-robin scheduling. The tool displays execution timelines and waiting times to make operating-system scheduling behaviour easier to understand.
Built a transit-oriented mobile application integrating live bus schedules, trip planning, weather updates, QR-code trips, and nearby-stop discovery.
Improved a television advertisement-break detection system during the Taiv Hackathon, refining Python-based logic to handle difficult test cases more accurately.
Developed an interactive 3D visualization using Three.js and NASA APIs, combining particle simulation and data sonification to communicate space-weather phenomena.
Conducting graduate research at the intersection of distributed systems and artificial intelligence, including literature review, experimental design, and research prototyping.
Engineered reactive advertising-technology services using Java 21 and Eclipse Vert.x. Optimized backend API latency, expanded JUnit test coverage, and supported releases using Docker, Maven, and GitLab CI/CD.
Developed and improved APIs providing real-time travel inventory to global partners. Worked with PHP, Ruby, and Rust services, debugging and testing high-traffic systems and contributing to CI/CD releases.
Investigated security anomaly detection using system provenance graphs, graph neural networks, Word2Vec, and supervised machine learning; helped prepare research findings for publication.
Built features for a scientific-equipment sharing platform using Django, React, and PostgreSQL. Supported AWS deployment and automated testing with Pytest.
Developed machine-learning and natural-language-processing approaches for analyzing system logs in provenance-based security research.
Developed interactive mathematics learning games using HTML, CSS, and JavaScript, including randomized challenges and real-time feedback.
Academic focus: Distributed Systems and Artificial Intelligence.
NSERC Undergraduate Student Research Award (2025). Certifications: AWS Solutions Architect and Machine Learning Specialization.
Java 21, Python, C/C++, C#, JavaScript, PHP, Ruby, Rust, SQL, HTML/CSS
Eclipse Vert.x, React, Node.js, Django, TensorFlow, Pandas, NumPy, Matplotlib, Three.js
AWS, Azure DevOps, Docker, Maven, GitLab CI/CD, Git/GitHub, Postman
PostgreSQL, MySQL, SQL Server, SQLite, MongoDB, Neo4j, HSQLDB, JUnit 5, Pytest, Mockito
Interested in research collaboration, distributed software systems, or a technical conversation? I would be glad to connect.