I'm interested in solving transportation problems at the intersection of engineering, analytics, and technology.
At the City of Toronto, I've worked on projects ranging from FIFA World Cup congestion monitoring and city-wide bicycle demand estimation to accessibility modeling, machine learning, and Automated Speed Enforcement evaluation. This work has supported operational planning, informed City Council reporting, and contributed to public transportation policy discussions.
What I enjoy most are problems where the answer isn't obvious from the data alone: understanding congestion, improving transit reliability, estimating travel demand, or evaluating policy. I like combining engineering judgment with analytical tools to help cities make better decisions.
Outside of engineering, I'm usually building side projects, exploring new tech, or playing sports.
Engineering solutions for transportation, infrastructure, and healthcare.
Placed 10th out of 328 participants (100+ teams) at the NSRI Summer Research Hackathon. Tested AI's likely effect on hospital costs against two historical analogies, slow post-electrification productivity gains vs. the dot-com boom-and-bust, using Medicare Cost Report data across ~6,000 hospitals (2011–2024). Found administrative cost share has climbed steadily since EHR saturation with no reversal, forecasted to keep rising through 2034, while clinical AI funding already shows classic bubble markers.
Read the report ↗Designed and deployed an automated system evaluating the impacts of ASE camera removals across Toronto. Built scalable analytics pipelines on city-wide traffic data to monitor roadway performance and support policy evaluation. The analyses informed City Council reporting and were discussed publicly by the Mayor.
Developed automated congestion monitoring workflows supporting the City of Toronto's transportation planning for the FIFA World Cup. Built repeatable Python and SQL pipelines to monitor network performance, identify congestion trends, and generate operational insights.
Built ML models on over ten years of TTC streetcar delay data. Random Forest models reached R² up to 0.98 and MAE as low as 0.73 min, showing how historical transit data can improve operational planning.
Read the write-up ↗A city-scale pipeline for Annual Average Daily Bicycle Traffic (AADT), combining temporal normalization, seasonal adjustment, and confidence interval estimation to support cycling infrastructure planning.
Reproducible geospatial workflows computing gravity-based and cumulative accessibility measures across transportation networks, with GIS visualizations supporting planning and accessibility analysis.
Winner of the AI × Bio Hackathon. An AI-powered patient-monitoring platform that learns individualized physiological baselines from wearables to detect early deterioration. Currently being developed as a startup.
CGPA 3.50 · Dean's List ×3