I research statistical and causal machine learning (ML) at Fero Labs, which I co-founded seven years ago. I also teach and advise students 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 ML and artificial intelligence (AI) interface with climate change. To this end, I developed a course about Machine Learning and Climate.
I currently focus on trustworthy ML methods — algorithms that carefully move beyond correlations, quantify future uncertainties, and identify causal relationships. I find these qualities to be a key component for a broad adoption of ML 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, where my dissertation won a best thesis award.