Professor
David received the Ph.D. in Computer Science from Cornell University in 2008 and the M.S. and B.S. degrees in Computer Science and Engineering from Penn State in 2001. He was a Postdoctoral Associate at Cornell from 2008-2010, and as a Research Scientist at Eastman Kodak Company from 2001-2003. His main research interests are in computer vision, applied machine learning, probabilistic graphical models, and data mining.
Weslie Khoo
Postdoctoral Fellow
Weslie received his Ph.D. in Food Science from Penn State University in 2019, and the B.S. in Chemistry and Biological Chemistry from Nanyang Technological University, Singapore in 2013. His research interests are in consumer behavior and perception of packaged goods and how AI can aid in healthy eating choices.
Sarah Carroll
Project Finance Manager
Starting at the Luddy School in 2015, Sarah has managed research programs, project finances, research centers, international research collaborations, and AI symposia. She received her BS from the University of Wisconsin-Madison, and is due to receive her MS in Accountancy from the Kelley School of Business in 2023. Her primary interest is forensic accounting.
Soon-Heung Jung
Visiting Scholar
Soon-heung received Ph.D. and M.S. degrees in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST), Korea, and the BS degree in Electronics from Pusan National University, Korea. Since 2005, he has been a principal researcher in Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea. His research interests include immersive media, computer vision, video coding, and realistic broadcasting system.
Visiting Scholar
Hank was a research assistant at National Yang Ming Chioa Tung University in Taiwan from 2021-2024. Prior to this, he received his M.S. degree from National Tsing Hua University in 2020 and B.S. from National Taipei University in 2017. He was a research intern at IBM T.J. Watson Research Center in 2020. His research interests include video representation learning, compositional representation learning, and robot learning.