I research statistical and causal machine learning at Fero Labs, which I co-founded seven years ago. I also teach and advise students in the computer science department at Columbia University. I volunteer by serving as an action editor for JMLR and TMLR, and by leading entrepreneurship efforts at Climate Change AI.
I am motivated to explore how machine learning interfaces with climate change. To this end, I am teaching two courses on this topic. In addition, I co-authored a report titled Artificial Intelligence for Climate Change Roadmap, which was launched at the United Nations COP29 meeting in November 2024.
I focus my research on trustworthy machine learning — algorithms that carefully move beyond correlations, quantify future uncertainties, and identify causal relationships. These are key qualities for broad adoption of machine learning and artificial intelligence in industry and climate applications.
I previously worked on approximate Bayesian inference with David Blei and probabilistic programming with Andrew Gelman. I designed Stan's variational inference algorithm. I obtained my Ph.D. at Yale University, where my dissertation won a best thesis award.