Willie

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.

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.

News

Projects

Publications

A full list of my publications can be found here.