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 to me via email at neiswang@usc.edu.

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. I then did a postdoc in computer science at Stanford University, working with Stefano Ermon.

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.