I'm a PhD student in machine learning at Stanford University co-advised by Percy Liang and Tatsu Hashimoto and funded by a Knights-Hennessy Scholarship.

My interest lies in learning representations of data that provide guarantees on downstream machine learning. For example, representations that provably improve the robustness, fairness, privacy, generalization or efficiency of machine learning. I'm particularly interested in using statistical and information theoretic tools to do so.

Before starting my PhD I worked on similar research projects at the Vector Institute with Chris Maddison and also as part of my Facebook AI Residency. Before moving to research, I worked on more applied AI projects at different startups. For example, developing algorithms at Grab to analyze text in under-research languages (Thai, Khmer, Burmese, …); or predicting room occupancy at SBS to decrease the carbon footprint of large buildings in Vancouver.

Outside of machine learning I spend my time playing sports with friends (:ski:,:mountain:,:badminton:,:basketball:,:volleyball:,:runner:), traveling, or reading about geopolitics.