Chi-Chun Lee

Associate Professor

National Tsing Hua University

Resarch Interests: Behavioral Signal Processing, Speech and Language, Affective Multimedia, Health Analytics

Chi-Chun Lee (Jeremy) is an Associate Professor at the Department of Electrical Engineering Department of the National Tsing Hua University (NTHU), Taiwan. He received his B.S. degree and Ph.D. degree both in Electrical Engineering under supervision of Prof Shri Narayanan from the University of Southern California, USA in 2007 and 2012. He was a data scientist at id:a lab at ID Analytics in 2013. His research interests are in the human-centered behavioral signal processing (BSP), affective multimedia computing, and intelligent clinical decision support. He is an associate editor for IEEE Transcation on Multimedia (2019-2020). He serves as an area chair for Interspeech 2016,2018,2019, senior program committee for ACII 2017, 2019, ACM ICMI 2018, publicity chair for ACM ICMI 2018, and a guest editor in Journal of Computer Speech and Language on special issue of Speech and Language Processing for Behavioral and Mental Health. He lead a team to the 1st place in Emotion Challenge in Interspeech 2009, and with his students reach 2nd place in Self-Affect subchallenge in Interspeech 2018. He is a coauthor on the best paper award/candidate in Interspeech 2010 (top 3), IEEE EMBC 2018 (top 15), Interspeech 2018 (top 12), and the most cited paper published in 2013 in Journal of Speech Communication on automatic modeling of couples' behaviors during therapeutic sessions. He received the MOST 2018 Futuretek Breakthrough Award, MediaTek Geniusforhome Special Prize Award (top 10 team), and the USC Annenberg Fellowship. His team is selected as one of the 40 Taiwan Tech Star to exhibit in 2019 CES Eureka Park. He is a member of Tau Beta Pi, Phi Kappa Phi and Eta Kappa Nu. He is also a member of IEEE and ISCA. He has been a reviewer for multiple internationally-renowned journals and program committee for international technical conferences. He is involved in multiple granted interdisciplinary research projects, including aspects on education, psychology, neuroscientific, and health-related applications, with a focus of modeling human multimodal behaviors using signal processing and machine learning.