About Me
I am a PhD student in geography and research assistant at TransLAB (Transportation Research Lab) within the School of Earth, Environment & Society at McMaster University, working under the supervision of Dr. Darren Scott. My current research explores public transit and shared micromobility (e.g., bike share and shared electric scooters) across North America, with a special focus on Hamilton, Ontario. I apply data-driven and quantitative approaches to uncover patterns and improve transportation systems.
Previously, I earned an Honours Bachelor of Arts (HBA) Degree from the University of Toronto Scarborough (UTSC) in 2024, majoring in Economics for Management Studies, with minors in Geographic Information Science (GIS) and Applied Statistics, as well as a certificate in Computational Social Science. My works have twice been funded by the University of Toronto Excellence Award in 2023 and 2024. Additionally, I received the Governor General’s Silver Medal for achieving the highest academic standing among all HBA graduates at the University of Toronto in 2024. I have also been awarded the 2025–26 Ontario Graduate Scholarship for excellent academic performance, jointly funded by the Government of Ontario and McMaster University.
Before joining McMaster University, I was a research assistant in the Suburban Mobilities Cluster in the Department of Human Geography at UTSC. From 2022 to 2024, I participated in several projects under the supervision of Dr. Steven Farber, Dr. Andre Cire, and Dr. Ignacio Tiznado-Aitken, conducting survey and network data analysis to explore transportation accessibility, equity, and justice in suburban areas, particularly in Scarborough, Ontario.
My background in geography, economics and quantitative methods has increasingly guided me toward integrating machine learning (ML), artificial intelligence (AI), GeoAI, and large language models (LLMs) into transportation planning research. I use ML- and LLM‑based tools to enhance empirical analysis, streamline data processing, and support more scalable, reproducible approaches to studying mobility systems. I am particularly interested in how emerging AI and GeoAI methods can deepen our understanding of transit and micromobility usage, reveal spatial and behavioural patterns, and strengthen evidence‑based planning for more resilient, efficient, and equitable transportation networks. With extensive experience in Python, R, and GIS platforms such as ArcGIS Pro and QGIS, I aim to bridge traditional spatial‑economic analysis with modern computational techniques to address complex mobility challenges.
As a long‑time transit enthusiast, I remain motivated by the lived experience of mobility and the real‑world implications of transportation planning. Riding buses, trains, and shared micromobility systems continues to shape my curiosity about how people move and how planning decisions influence system performance. I am excited to leverage my interdisciplinary background, spanning geography, economics, data science, and transportation planning, to develop transparent, data‑driven, and AI‑supported approaches that improve mobility systems and enhance their resilience and efficiency.
Areas of Interest
Spatial Analysis
Econometrics
Machine Learning
Transportation Planning
Public Transit
Micromobility