I lead the Modeling & Optimization team within the Adaptive Experimentation group on Meta’s Core Data Science team. We focus on developing methods and tools for probabilistic modeling and sample-efficient optimization, and apply them to a broad range of applications across the company, including infrastructure optimization, AutoML, online A/B tests, ranking systems, and AR/VR. I also lead the development of BoTorch, an open-source library for Bayesian Optimization in PyTorch.
In the past, I have also worked on the intersection of Machine Learning and Econometrics, in particular on how to utilize Machine Learning algorithms to perform causal inference in experimental and non-experimental settings.
I hold an MA in Mathematics and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley.
Interests
Bayesian Optimization, Gaussian processes, probabilistic modeling, causal inference and machine Learning