The central question that I look at is whether technology that looks "green" on paper actually delivers environmental and public-health benefits in the real world. I combine large air-pollution data-sets, epidemiological statistics, and interpretable ML models to quantify outcomes such as childhood-asthma incidence when electric-vehicle sales rise, or energy "rebound" in smart buildings after efficiency upgrades. Through science communication and by making the models transparent, I aims to give regulators a defensible basis for zero-emission-vehicle mandates, building-codes, and data-centre standards.

At the University of Toronto, I am supervised by Dr. Steve Easterbrook. I am a doctoral fellow at the University of Toronto's Climate Positive Energy (CPE) and Data Science Institute (DSI), where I contribute to research on sustainable energy solutions and data-driven environmental policy. I'm a member of President's Advisory Committee for Environment, Climate Change, and Sustainability where I co-chair the Student Leadership Subcommittee. I'm also a member of Toronto Climate Observatory where we build decision-support tools so cities can transition to clean energy without inadvertently shifting emissions or health burdens onto other communities.

"Where there is much desire to learn, thereof necessity will be much arguing, much writing, many opinions; for opinion in good men (good person) is but knowledge in the making." — John Milton.

Please find my updated CV here.