Speakers
Prof. Demetri Terzopoulos
ACM Fellow, IEEE Fellow, Fellow of the Royal Society of London, Fellow of the Royal Society of Canada
University of California, Los Angeles, USA
Bio-sketch: Demetri Terzopoulos is a Chancellor's Professor of Computer
Science at the University of California, Los Angeles, where he holds the
rank of Distinguished Professor and directs the UCLA Computer Graphics &
Vision Laboratory. He is also Co-Founder and Chief Scientist of
VoxelCloud, Inc., a multinational healthcare AI company. He graduated
from McGill University, received his PhD degree ('84) in Artificial
Intelligence from the Massachusetts Institute of Technology (MIT), and
remained a Research Scientist at the MIT Artificial Intelligence
Laboratory through 1985. He is or was a Guggenheim Fellow, a Fellow of
the ACM, a Life Fellow of the IEEE, a Distinguished Fellow of the IETI,
a Fellow of the Royal Society of London and the Royal Society of Canada,
a Member of the European Academy of Sciences and the New York Academy of
Sciences, and a Life Member of Sigma Xi. His many awards include an
Academy Award for Technical Achievement from the Academy of Motion
Picture Arts and Sciences for his pioneering work on physics-based
computer animation, as well as IEEE's Computer Pioneer Award, Helmholtz
Prize, and inaugural Computer Vision Distinguished Researcher Award for
his pioneering and sustained research on deformable models and their
applications. Deformable models, a term he coined, is listed in the IEEE
Taxonomy. With his more than 400 published research papers and several
volumes, primarily in computer graphics, computer vision, medical
imaging, computer-aided design, and artificial intelligence/life, the
ISI and other indexes have listed him among the most highly-cited
authors in engineering and computer science. He has given more than 500
invited talks worldwide about his research, including well over 100
distinguished lectures and keynote/plenary addresses. He joined UCLA in
2005 from New York University, where he held the Henry and Lucy Moses
Endowed Professorship in Science and was Professor of Computer Science
and Mathematics at NYU's Courant Institute of Mathematical Sciences.
Previously, he was Professor of Computer Science and Professor of
Electrical & Computer Engineering at the University of Toronto. Prior to
becoming an academic in 1989, he was a Program Leader at Schlumberger
corporate research centers in California and Texas.
Speech Title: To be added soon
Abstract: To be added soon
Prof. Ling Liu
IEEE fellow
Georgia Institute of Technology, USA
Bio-sketch: Ling Liu is a Professor in the School of Computer Science at
Georgia Institute of Technology. She directs the research programs in
the Distributed Data Intensive Systems Lab (DiSL), examining various
aspects of large scale big data-powered artificial intelligence (AI)
systems, and machine learning (ML) algorithms and analytics, including
performance, availability, privacy, security and trust. Prof. Liu is an
elected IEEE Fellow, a recipient of IEEE Computer Society Technical
Achievement Award (2012), and a recipient of the best paper award from
numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE
Cloud, IEEE ICWS. Prof. Liu served on editorial board of over a dozen
international journals, including the editor in chief of IEEE
Transactions on Service Computing (2013-2016) and currently, the editor
in chief of ACM Transactions on Internet Computing (TOIT). Prof. Liu is
a frequent keynote speaker in top-tier venues in Big Data, AI and ML
systems and applications, Cloud Computing, Services Computing, Privacy,
Security and Trust. Her current research is primarily supported by USA
National Science Foundation under CISE programs and IBM.
Speech Title: To be added soon
Abstract: To be added soon
Prof. Zhongsheng Hou (侯忠生教授)
IEEE fellow
Qingdao University, China
Bio-sketch: Dr. Zhongsheng Hou received Bachelor’s and Master’s degrees
from Jilin University of Technology, China, in 1983 and 1988,
respectively, and Ph.D. degree from Northeastern University, China, in
1994. He was a Postdoctoral Fellow with Harbin Institute of Technology,
China, from 1995 to 1997 and a Visiting Scholar with Yale University,
CT, from 2002 to 2003. In 1997, he joined the Beijing Jiaotong
University, China, where he is a distinguished professor and founding
director of advanced Control Systems Lab, and Head of Department of
Automatic Control. He is also the founding director of the Technical
Committee on Data Driven Control, Learning and Optimization (DDCLO),
Chinese Association of Automation. He is an IFAC Technical Committee
Member on both “Adaptive and Learning Systems” and “Transportation
Systems.” His research interests include the fields of data-driven
control, model-free adaptive control, learning control, and intelligent
transportation systems. Prof. Hou’s original work on Model Free Adaptive
Control has been recognized by over 150 different field applications
including wide-area power system, lateral control of autonomous vehicle,
temperature control of silicon rod, etc., and his pioneering
contributions in Data Driven Control & Learning Control have been
recognized by multiple projects supported by the National Natural
Science Foundation of China (NSFC), including two key projects of NSFC,
2009 and 2015 respectively, and a major international cooperation
project of NSFC, 2012, and by his leading role as a Guest Editor in two
Special Sections on the topic of data-driven control in the IEEE
TRANSACTIONS ON NEURAL NETWORKS, 2011, and the IEEE TRANSACTIONS ON
INDUSTRIAL ELECTRONICS, 2017. Up to now, he has over 160 peer-reviewed
journal papers published and over 140 papers in prestigious conference
proceedings. He is the author of two monographs Nonparametric Model and
its Adaptive Control Theory, Science Press, 1999, (in Chinese) and Model
Free Adaptive Control: Theory and Applications, CRC Press, 2013.
Speech Title: To be added soon
Abstract: To be added soon
Prof. Haizhou Li
IEEE fellow, ISCA Fellow
National University of Singapore, Singapore
Bio-sketch: Haizhou Li is currently a Professor at The Chinese
University of Hong Kong, Shenzhen, China, and an adjunct Professor at
the National University of Singapore (NUS). Prior to that, he was the
Principal Scientist and Department Head of Human Language Technology in
the Institute for Infocomm Research, Singapore (2003-2016). Prof. Li’s
research interests include speech information processing, natural
language processing, and human-machine interface. Prof. Li has served as
the Editor-in-Chief of IEEE/ACM Transactions on Audio, Speech and
Language Processing (2015-2018), a Member of the Editorial Board of
Computer Speech and Language (2012-2018), and a Member of IEEE Speech
and Language Processing Technical Committee (2013-2015). He was the
President of the International Speech Communication Association (ISCA,
2015-2017), the President of Asia Pacific Signal and Information
Processing Association (2015-2016), and the President of Asian
Federation of Natural Language Processing (2017-2018). He was the
General Chair of ACL 2012, INTERSPEECH 2014, IEEE ASRU 2019, and ICASSP
2022. Prof. Li is a Fellow of the IEEE, a Fellow of ISCA, and a Fellow
of Academy of Engineering Singapore. He was a recipient of the
President’s Technology Award 2013 in Singapore. He was named one of the
two Nokia Visiting Professors in 2009 by the Nokia Foundation, and U
Bremen Excellence Chair Professor in 2019 by Bremen University, Germany
Speech Title: Recent Advances in Selective Auditory Attention
Abstract: Humans have a remarkable ability to pay their auditory
attention only to a sound source of interest, that we call selective
auditory attention, in a multi-talker environment or a Cocktail Party.
However, signal processing approach to speech separation and/or speaker
extraction from multi-talker speech remains a challenge for machines. In
this talk, we study the deep learning solutions to monaural speech
separation and speaker extraction that enable selective auditory
attention. We also introduce their applications in speech recognition,
speaker recognition, and hearing aids. We discuss the computational
auditory models, technical challenges and the recent advances in the
field.