EducationPostdoc, University of California, Berkeley, 2022
PhD, Peking University, 2016
BS, Beijing Institute of Technology, 2011
Develops techniques and systems for trustworthy natural language processing
Chenguang Wang has broad interests in natural language processing and machine learning. His work focuses on techniques and systems for making NLP trustworthy in real-world settings. His recent work focuses especially on the zero- or few-shot learning performance, security, interpretability, robustness, ethics, and blockchain applications of deep learning models. He has created several open-source deep learning systems, including AutoGluon and GluonNLP. His research has been deployed in real-world scenarios ranging from science to industry.
Chenguang Wang joined Washington University in St. Louis in 2022. Before that, he was a postdoc in Computer Science at UC Berkeley. He was previously a research scientist at Amazon AI and a research staff member at IBM Research-Almaden. He was nominated for the 2016 ACM China Doctoral Dissertation Award and received honorable mention (one of the two national finalists).