I lead research, development, and deployment of artificial intelligence (AI) across Amazon’s global supply chain. I also teach at Columbia University and serve as an action editor for JMLR and TMLR — two of the field’s premier academic journals.
In my teaching, I explore how AI can mitigate climate change. I co-authored a report titled Artificial Intelligence for Climate Change Roadmap, which was unveiled at the United Nations COP29 conference in 2024.
My research focuses on trustworthy AI: building algorithms that move beyond correlations, quantify future uncertainties, and uncover causal relationships — qualities essential for widespread industry adoption.
Before focusing on supply chains, I co‑founded and led the research and development of causal AI at Fero Labs. Earlier, I worked on approximate Bayesian inference with David Blei and probabilistic programming with Andrew Gelman, designing and implementing Stan’s first variational inference algorithm. I earned my Ph.D. at Yale University, where my dissertation won a best thesis award.