Dr. Willie Neiswanger

Biography

Willie Neiswanger is an Assistant Professor in the Department of Computer Science at the University of Southern California, Viterbi School of Engineering. His work is centered at the intersection of machine learning, decision making, and generative AI, with a strong focus on AI-for-science applications. Willie previously held a postdoctoral position at Stanford University, where he was affiliated with the StatsML Group and the Stanford AI Lab, as well as the SLAC National Accelerator Laboratory. He earned his PhD in Machine Learning from Carnegie Mellon University and his Bachelor's degree in Applied Mathematics and Computer Science from Columbia University. Willie's research aims to develop machine learning methods for efficient optimization and experimental design in costly real-world settings with limited resources. His expertise encompasses active learning, uncertainty quantification, Bayesian decision making, and reinforcement learning, applying these methods to solve problems in science and engineering. He has also worked on distributed algorithms for scalable machine learning and has been involved in developing and maintaining software libraries for multilevel optimization, uncertainty quantification, AutoML, and Bayesian optimization.

Research Interests