DeLiang Wang received the B.S. degree in 1983 and the M.S. degree in 1986 from Peking (Beijing) University, Beijing, China, and the Ph.D. degree in 1991 from the University of Southern California, Los Angeles, CA, all in computer science.
From July 1986 to December 1987 he was with the Institute of Computing Technology, Academia Sinica, Beijing. Since 1991, he has been with the Department of Computer Science and Engineering and the Center for Cognitive Science at The Ohio State University, Columbus, OH, where he is currently a Professor. From October 1998 to September 1999, he was a visiting scholar in the Department of Psychology at Harvard University, Cambridge, MA. From October 2006 to June 2007, he was a visiting scholar at Oticon A/S, Copenhagen, Denmark. From October 2014 to De 2007, he was a visiting scholar at Oticon A/S, Copenhagen, Denmark.
DeLiang Wang received the NSF Research Initiation Award in 1992 and the ONR Young Investigator Award in 1996. He received the OSU College of Engineering Lumley Research Award in 1996, 2000, 2005, and 2010. His 2005 paper, "The time dimension for scene analysis", received the 2007 Outstanding Paper Award from the IEEE Computational Intelligence Society. His 2014 paper with Yuxuan Wang and Arun Narayanan, "On training targets for supervised speech separation", received the 2019 Best Paper Award from the IEEE Signal Processing Society. He received the 2008 Helmholtz Award and the 2019 Ada Lovelace Service Award from the International Neural Network Society. In 2014, he was named a University Distinguished Scholar. He was an IEEE Distinguished Lecturer (2010-2012), and is an IEEE Fellow.
He is Co-Editor-In-Chief of Neural Networks, which is a premier journal published by Elsevier. In addition, he serves on the advisory board of Cognitive Computation. He also served as President of the International Neural Network Society in 2006, and currently serves on its governing board.
DeLiang Wang's general area of interest is machine perception. More specifically, he is interested in neural computation for auditory and visual information processing. To achieve this, his research program seeks to uncover computational principles for auditory and visual analysis, including segmentation, recognition and generation. This research is on the basis of psychological/neurobiological data from human and animal perception and computational considerations.
A fundamental aspect of perception is its ability to group elements of a perceived scene or sensory field into coherent clusters (objects), generally known as scene analysis and segmentation. The general problem of scene analysis remains unsolved in computational audition and vision. For a number of years, Wang's group focuses on understanding the dynamics of large networks of coupled neural oscillators and their applications to scene analysis (see Wang, 2005, for a view/review on this research effort). The results of this study have yielded a neurobiologically plausible approach to address the problem of scene analysis.
His recent work focuses on developing learning algorithms, in particular deep neural networks, for auditory scene analysis. In order to achieve the ultimate goal of constructing a computational system that possesses the human ability of cocktail-party processing, one must understand individual analyses, such as pitch, location, amplitude and frequency modulation, onset/offset, rhythm, and so on. One must also incorporate top-down mechanisms, such as recognition and attention. His Perception and Neurodynamics Lab (PNL) investigates a variety of topics under the general theme of computational audition where deep learning plays a major role.