UNIVERSITY OF TORONTO · COMPUTER SCIENCE & MATHEMATICS
Welcome to my Website, I'm Hussein Khalil a Fourth year Computer Science student driven by curiosity for AI, data intensive systems, and polished product experiences. I thrive on breaking down complex problems into approachable builds that serve people fairly and reliably.
Research and applied work that blends responsible AI with rigorous engineering practice.
Investigated algorithmic bias in the COMPAS risk assessment tool, analyzing 10k+ case records with Python to evaluate fairness and predictive accuracy. Built reproducible Jupyter experiments, fairness-aware pipelines, and visual reports that improved model accuracy to 77.2% while reducing disparity (SPD –0.012, DI 0.981).
Hands-on builds that translate theoretical concepts into tangible software.
Engineered modular scikit-learn + TensorFlow pipelines to surface algorithmic bias mitigation strategies. Documented experiment design, automated statistical testing, and delivered stakeholder-ready dashboards that guided ethical decision making.
Implemented recursive quadrant partitioning to compress large grayscale datasets by up to 60% while maintaining fidelity. Added batch tooling, benchmarking scripts, and visualization layers to evaluate reconstruction quality.
Languages, frameworks, and platforms I use to turn concepts into production-ready solutions.
University of Toronto coursework that grounds my engineering practice in theory.
Expected Graduation · Nov 2026
Deepened analytical rigor through coursework in Statistics, Numerical Methods, Probability, Combinatorics, Software Engineering, Computational Modeling, and Cybersecurity.
Open to internships, research collaborations, and software projects aimed at solving real world problems.
Activily Looking for a teammate who enjoy creating change, helping the communinty and are inspired to be part of something big, something meaningful