Beyond loss: Efficient optimization of living machine learning systems
Director and research scientist, Meta
Date: Thursday, Sep 14, 2023, 9:00 - 10:00
I'm a director and research scientist at Meta, where I lead the Adaptive Experimentation team. We develop robust AI methods for sample-efficient optimization. We conduct applied and use-inspired basic research to solve real-world problems across the company, and scale these methods through the development of software frameworks. Our work is used broadly within Meta, with applications ranging from optimizing recommender system ranking policies and infrastructure, to AutoML, hardware design, and perception science. My research interests include Bayesian optimization, Bayesian machine learning, meta-learning, multi-armed bandits, and active learning. I am passionate about democratizing these methods through the development of open-source software, including BoTorch, a framework for Bayesian optimization research, and Ax, an end-user platform for Bayesian optimization and multi-armed bandits.