Eytan Bakshy

I'm a director and research scientist at Meta, where I lead the Adaptive Experimentation team. We develop methods for sample-efficient optimization and active learning with AI in order to solve generalizable product problems. Our team's 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.

Selected publications

Here are a few of my recent publications. For a more complete list of publications and activities, see my Google Scholar page and likely outdated CV.

Bayesian optimization, active learning, and reinforcement learning

Causal Inference and Experimentation

Field experiments

Observational studies

Open source

The Adaptive Experimentation team at Meta develops two open-source software packages:

I am also the creator of PlanOut. It's an open-source framework for designing and implementing complex behavioral science experiments. I find PlanOut to be helpful for thinking about and planning experiments, and hope that people can use it to run interesting experiments. Visit the PlanOut homepage and give it a try. For more on designing and deploying online field experiments with PlanOut, check out our paper on the subject.

Press

Coverage on papers and Data Science posts from the early days: Other press