Willie Neiswanger

Machine learning at Stanford Computer Science SAIL / StatsML

I am a postdoc in computer science at Stanford University, working with Stefano Ermon and affiliated with the StatsML Group, Stanford AI Lab, and SLAC.

Incoming Assistant Professor at USC Viterbi, Department of Computer Science, starting Jan 2024.

Prospective students: I am recruiting PhD students (current application cycle) and postdocs who wish to do work at the intersection of machine learning, decision making, generative AI, and AI-for-science! If you are interested, please feel free to reach out via email, and apply here by December 15.

Research: I develop machine learning methods to perform efficient optimization and experimental design in costly real-world settings, where resources are limited. My work spans topics in active learning, uncertainty quantification, Bayesian decision making, and reinforcement learning. I apply these methods downstream to solve problems in science and engineering, for example in the physical sciences and machine learning systems.

I have also worked on distributed algorithms for scalable machine learning, and I develop/maintain software libraries for multilevel optimization, uncertainty quantification, AutoML, and Bayesian optimization.

Education: I completed my PhD in Machine Learning at Carnegie Mellon University, where I was advised by Eric Xing and collaborated with Jeff Schneider and Barnabas Poczos.

Previously, I studied at Columbia University, where I worked with Chris Wiggins and Frank Wood.




A full list of my publications can be found here.