about

I recently defended my PhD in Electrical Engineering at Stanford, where I worked in the Lall Group, and will graduate in September 2026. I am now focusing on technical AI safety, supported by a grant from Coefficient Giving that is allowing me to spend time on self-study and independent research as I prepare for a career in the field.

My research background sits at the intersection of machine learning, optimization, and control theory, driven by a fascination with complex systems and a desire to understand their behavior well enough to enable safer and more efficient systems. I earned a B.A. and M.Eng. in Chemical Engineering from the University of Cambridge in 2019 and an M.S. in Electrical Engineering from Stanford in 2022. Drawing on this interdisciplinary background, I have applied these ideas to problems ranging from thermal stability and safe control, to constrained neural-network training, to simplifying LLM architectures while preserving training stability. Along the way I’ve worked on applied problems at TSMC, Applied Materials, KLA, and Inflection AI.