About

I received my Ph.D. from the University of Toronto in The Department of Electrical & Computer Engineering.

During this time, I was a graduate research student at the Vector Institute and a member of the Probabilistic and Statistical Inference Group supervised by Brendan Frey and co-supervised by Bo Wang. I also spent time as a research intern at Recursion Pharmaceuticals / Valence Labs, Deep Genomics, and Mila - Quebec AI Institute.

My research focuses on developing machine learning methods for understanding and modelling natural (physical) systems from data, with a particular focus on cellular and molecular biology. My interests include generative modelling (flows and diffusion), deep learning, causality, single-cell biology, and recently, proteins & molecules.

Research Interests

• Generative AI: Flow models, diffusion models, and generative flow networks
• Systems Biology: Single-cell analysis, gene regulatory networks, and cellular dynamics
• Causal Discovery: Understanding cause-and-effect relationships in biological systems
• Deep Learning: Neural network architectures for biological data
• Protein & Molecular Modeling: Computational approaches to understanding molecular structures and interactions

Education & Experience

• Postdoc at the Broad Institute of MIT-Harvard, Eric & Wendy Schmidt Center
• Ph.D. in Electrical & Computer Engineering, University of Toronto
• M.A.Sc. in Electrical & Computer Engineering, University of British Columbia
• B.A.Sc. in Electrical Engineering, University of British Columbia
• Machine Learning Research Intern at Recursion Pharmaceuticals / Valence Labs
• Machine Learning Intern at Deep Genomics
• Research Intern at Mila - Quebec AI Institute