William Wang

Education

• PhD, Carnegie Mellon University

Awards

• CVPR Best Student Paper Award, 2019
• IBM Faculty Award, 2019
• Facebook Research Award, 2019
• DARPA Young Faculty Award, 2018
• IBM Faculty Award, 2018
• Facebook Research Award, 2018
• Google Faculty Research Award 2018
• Adobe Research Award, 2018
• IBM Faculty Award, 2017
• Inaugural Notable Data Set Award, EMNLP 2015
• Best Reviewer Award, NAACL 2015
• Best Paper Honorable Mention Award, CIKM 2013
• Best Student Paper Award, ASRU 2013
• Richard King Mellon Presidential Fellowship, Carnegie Mellon University 2011-2012

Research Areas

• Machine Learning and Data Mining

Bio

William Wang is the Director of UC Santa Barbara's Natural Language Processing group and an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara. He is the founding Director of UCSB's Center for Responsible Machine Learning. He received his PhD from School of Computer Science, Carnegie Mellon University. He has broad interests in machine learning approaches to data science, including statistical relational learning, information extraction, computational social science, speech, and vision. He has published more than 80 papers at leading NLP/AI/ML conferences and journals, and received best paper awards (or nominations) at ASRU 2013, CIKM 2013, EMNLP 2015, and CVPR 2019, a DARPA Young Faculty Award (Class of 2018), three IBM Faculty Awards in 2017-2019, a Google Faculty Research Award in 2018, two Facebook Research Awards in 2018 and 2019, an Adobe Research Award in 2018, and the Richard King Mellon Presidential Fellowship in 2011. He often serves as an Area Chair for NAACL, ACL, EMNLP, and AAAI. He is an alumnus of Columbia University, and a former research scientist intern of Yahoo! Labs, Microsoft Research Redmond, and University of Southern California. In addition to research, William enjoys writing scientific articles that impact the broader online community: his microblog @王威廉 has 100,000+ followers and more than 2,000,000 views each month. His work and opinions appear at major tech media outlets such as Wired, VICE, Scientific American, Fast Company, and Mental Floss.

Research

I study the theoretical foundation and practical algorithms for Artificial Intelligence. To build intelligent machines that can tackle challenging reasoning problems under uncertainty, I have pursued answers via studies of Machine Learning, Natural Language Processing, and Interdisciplinary Data Science. More specifically, I am interested in designing scalable inference and learning algorithms to analyze massive datasets with complex structures. In particular, I advance methods in the following research areas: Statistical Relational Learning, Knowledge Representation and Reasoning, Natural Language Processing, Speech, and Computational Social Science. The central focus of my PhD dissertation research is to bring together all areas above and design scalable algorithms for large scale inference problems on knowledge graphs. Meanwhile, I enjoy collaborating with scientists and domain experts of different backgrounds for interdisciplinary research in data science. Currently, I am interested in advancing challenging problems in Artificial Intelligence, such as Natural Language Understanding, Information Extraction, and Learning to Reason.