Scholar
Willie Neiswanger
Google Scholar ID: QwKHApEAAAAJ
Assistant Professor of Computer Science, University of Southern California
Citations & Impact
Citations
4,530
H-index
31
i10-index
54
Publications
20
Co-authors
190
list available
Publications
Resume (English only)
Academic Achievements
- Published multiple papers on topics such as LiveBench, decision making under uncertainty with LLMs (DeLLMa), metagenomic foundation model METAGENE-1, uncertainty quantification for deep learning PDE surrogates, experimental design for determining safe tokamak rampdowns, algorithms and systems for scalable meta learning, offline model-based optimization through co-teaching, automatic differentiation for multilevel optimization, policy identification for active reinforcement learning, combining weak supervision and generative modeling, offline imitation learning with suboptimal demonstrations, uncertainty quantification with pre-trained language models, trajectory information planning for exploration in RL, and decision-theoretic entropies for generalizing Bayesian optimization. Co-organized several workshops and online reading groups.
Research Experience
- Currently an Assistant Professor of Computer Science at the University of Southern California (USC), in the Viterbi School and School of Advanced Computing. Recruiting PhD students and postdocs who wish to do work at the intersection of machine learning, decision making, generative AI, and AI-for-science.
Education
- Completed his PhD in Machine Learning at Carnegie Mellon University, advised by Eric Xing, and collaborated with Jeff Schneider and Barnabas Poczos. He then did a postdoc in computer science at Stanford University, working with Stefano Ermon. Previously, he studied at Columbia University, where he worked with Chris Wiggins and Frank Wood.
Background
- Research interests include machine learning, decision making, generative AI, and AI-for-science. He also develops methods for efficient optimization and experimental design in costly real-world settings where resources are limited, and works on uncertainty quantification in machine learning. He applies these to problems in the physical sciences, biology, and machine learning systems.
Miscellany
- Email: neiswang@usc.edu
