Is Continual Learning Easy with a Foundation Model?

Bing Liu is a distinguished professor of Computer Science at the University of Illinois at Chicago (UIC). He received his Ph.D. in Artificial Intelligence (AI) from the University of Edinburgh. Before joining UIC, he was a faculty member at the School of Computing, National University of Singapore (NUS). His research interests include lifelong and continual learning, sentiment analysis, chatbots, open-world AI/learning, natural language processing (NLP), and data mining and machine learning. He has published extensively in top conferences and journals. He also authored four books: two on sentiment analysis, one on lifelong learning, and one on Web mining. Three of his papers received Test-of-Time awards: two from SIGKDD (ACM Special Interest Group on Knowledge Discovery and Data Mining) and one from WSDM (ACM International Conference on Web Search and Data Mining). Another of his papers received Test-of-Time award - honorable mention also from WSDM. Some of his work has also been widely reported in the international press, including a front-page article in the New York Times. On professional services, he has served as the Chair of ACM SIGKDD from 2013-2017, as program chair of many leading data mining conferences, including KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editor of leading journals such as TKDE, TWEB, DMKD and TKDD, and as area chair or senior PC member of numerous NLP, AI, Web, and data mining conferences. He is a recipient of ACM SIGKDD Innovation Award (the most prestigious technical award from SIGKDD), and a Fellow of the ACM, AAAI, and IEEE.