Willie Neiswanger

Research: I work at the intersection of machine learning, decision making, generative AI, and AI-for-science. One focus of my research is on AI-driven decision making in costly, data-limited settings, using model-based optimization, experimental design, and uncertainty quantification. I also work on generative models, LLMs, and multimodal models, with applications in the physical sciences, biology, and engineering.

Additionally, I've worked on distributed algorithms for scalable training, and developed open-source libraries for multilevel optimization, uncertainty quantification, AutoML, and Bayesian optimization.

Background: Previously, I was a postdoc in CS at Stanford University, working with Stefano Ermon. Prior to that, I received my PhD in Machine Learning at Carnegie Mellon University, where I was advised by Eric Xing and also worked with Jeff Schneider and Barnabás Póczos.