I am an MSCS student at Stanford University working on AI for healthcare and AI for science. I work with Prof. Emily Alsentzer on EHR benchmarking and foundation model training, and with Prof. Jure Leskovec on generative models for protein design.
I completed my undergraduate studies in Computer Science and Mathematics at the University of Toronto, where I did research with Prof. Rahul G. Krishnan on healthcare AI, Prof. Sheila McIlraith on reinforcement learning, and Prof. Ashton Anderson on knowledge graphs. Before that, I spent a year at Cerebras Systems as a machine learning stack engineer working on ML compilers, and interned at Canaccord Genuity.
Publications
- Can we generate portable representations for clinical time series data using LLMs? ICLR 2026 · PDF
- PORTER: Portable Representations for Transferable Electronic Health Records NeurIPS 2026 Workshop on GenAI for Health
- AfriInstruct: Instruction Tuning of African Languages for Diverse Tasks Findings of EMNLP 2024 · PDF
* Equal contribution
About
My path to research has run through finance and ML systems. I started in financial mathematics at UofT and interned at Canaccord Genuity, then moved into machine learning systems at Cerebras, where I built ML compiler pipelines for large-scale model training, working on gradient accumulation, layout optimization, compiler cost models, and performance testing infrastructure. I have since transitioned into research, developing machine learning methods for healthcare and science, with work published at ICLR and EMNLP.