Carrson C. Fung received his B.S. degree from Carnegie Mellon University, Pittsburgh, PA, USA, in 1994, M.S. degree from Columbia University, New York City, NY, USA, in 1996 and Ph.D. degree at The Hong Kong University of Science and Technology in 2005, all in electrical engineering.  He is currently an active member of the IEEE Future Network AIML and massive MIMO working group, focusing his work on advancing HMIMO/UL-MIMO communications using AIML methods.  He was the recipient of the prestigious Sir Edward Youde Ph.D. Fellowship in 2001-2002.  He was the recipient of the National Chiao Tung University College of Electrical & Computer Engineering Excellent Teaching Award in 2011, 2017, and 2018.  He received the National Chiao Tung University College of Electrical & Computer Engineering Outstanding Teaching Award in 2020.  He also received the National Chiao Tung University Excellent Teaching Award in 2019.  He became a fellow of the Advance Higher Education Academy in 2020.  He and his student have won honorable mention in the 2024 student's Master thesis award from the Chinese Institute of Electrical Engineers.  He was a Member of Technical Staff at AT&T and Lucent Technologies Bell Laboratories, Holmdel, NJ, USA, from 1994-1999, where he worked on video and audio coding.  He was also a Researcher at the Hong Kong Applied Science and Technology Research Institute (ASTRI) in 2005, where he worked on MIMO-OFDM systems and a Senior DSP Engineer at Sennheiser Research Lab in Palo Alto, CA, USA, in 2006, where he worked on microphone and microphone array technologies.  He is currently an Associate Professor at the National Yang Ming Chiao Tung University in Hsinchu, Taiwan.  His research interests include network and data science, machine learning for signal processing and communications, and optimization.
 

The Signal Processing and AI Group (SIPAI Group) designs numerical optimization algorithms to solve different machine/deep learning problems and apply the solutions to different applications, such as data association for automotive radar, channel estimation and precoder design for wireless communications, data analysis using distributed and federated learning, and graph signal processing and graph learning for communications and biomedical engineering.  We leverage our knowledge in a signal processing (SP) and numerical optimization during the design process which allows us to extend conventional model-based SP approaches to model/data-driven or pure data-driven algorithms.