San Diego, California, United States
I'm a PhD scientist working on various aspects of machine learning, from fundamental research to deploying models that serve millions of customers. Prior to this role, I worked as a theoretical physicist studying new particles and forces beyond the Standard Model. I thrive on pioneering new solutions to challenging technical problems.
Currently working on building proprietary genAI foundation models for understanding customer behavior across Amazon. My role focuses both on fundamental research and developing models for production.
Pioneered global modeling techniques for Amazon’s Buyer Risk Prevention team. Trained and deployed models at scale for both retail and Amazon Pay applications. Also worked on fundamental AI research, publishing a first-author paper in NeurIPS 2023 on deep learning methods tailored to tabular data.
Leveraged state-of-the-art data analytics and machine learning techniques to detect and prevent fraudulent e-commerce transactions in the 3D-Secure space as part of Arcot (Broadcom’s Payment Security division). Worked on all aspects of model development, from data preprocessing, to training and evaluation.
Performed independent research on theoretical particle physics and cosmology.
Performed independent research on theoretical particle physics and cosmology.
Performed independent research on theoretical particle physics and cosmology.