Assistant Professor of Computer Science
Education
- Other, Postdoc in Computer Science, Stanford University
- Doctoral Degree, Machine Learning, Carnegie-Mellon University
- Bachelor's Degree, Applied Mathematics, Computer Science, Columbia University
Biography
Willie Neiswanger is an Assistant Professor of Computer Science at USC, in the Viterbi School and the School of Advanced Computing. He works at the intersection of machine learning, decision making, generative AI, and AI-for-science. Previously, he was a postdoc in computer science at Stanford University, affiliated with the StatsML Group, Stanford AI Lab, and SLAC National Accelerator Laboratory. Before that, he received his PhD in Machine Learning from Carnegie Mellon University.
Research Summary
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.
