I research and develop statistical and causal machine learning solutions 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 serve as an action editor for JMLR and TMLR — top academic journals in machine learning and artificial intelligence.
I am motivated to explore how machine learning can mitigate 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 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.
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.