I am a computational scientist specializing in machine learning and statistical inference for biological data analysis, currently working in the lab of Dmitri Petrov at Stanford University and collaborating with Madhav Mani at Northwestern University. My research focuses on developing novel computational approaches that combine dynamical systems theory, biophysical modeling, and deep learning techniques to extract actionable insights from complex multi-scale biological datasets.

I am particularly passionate about Evolution and the Physics of Living Systems, believing that understanding evolution requires physics-like approaches, especially techniques borrowed from statistical physics and complex systems. My work aims to create robust, scalable workflows that bridge fundamental cellular principles with rigorous data-driven decision making—all implemented with strong software engineering practices to ensure reproducibility and deployment readiness.

I completed my Ph.D. with Rob Phillips at Caltech, where I explored how cells gather information from the environment, process it, and build responses to maintain homeostasis. You can learn more about this work in my thesis.