Speakers and Speeches Information
Bio: Venkat Anantharam is on the faculty of the EECS Department at the University of California, Berkeley. He received the Philips India Medal and the President of India Gold Medal from IIT Madras in 1980 and an NSF Presidential Young Investigator award in 1988. He is a corecipient of the 1998 Prize Paper Award of the IEEE Information Theory Society, and a corecipient of the 2000 Stephen O. Rice Prize Paper Award of the IEEE Communications Theory Society. He received the Distinguished Alumnus Award from IIT Madras in 2008. He is a Fellow of the IEEE.
Bio: Yuxin Chen is currently an assistant professor in the Department of Electrical Engineering at Princeton University. Prior to joining Princeton, he was a postdoctoral scholar in the Department of Statistics at Stanford University, and he completed his Ph.D. in Electrical Engineering at Stanford University. His research interests include high-dimensional data analysis, convex and nonconvex optimization, statistical learning, and information theory.
Bio: Dr. Yuejie Chi received the Ph.D. degree in Electrical Engineering from Princeton University in 2012, and the B.E. (Hon.) degree in Electrical Engineering from Tsinghua University, Beijing, China, in 2007. Since January 2018, she is Robert E. Doherty Career Development Professor and Associate Professor with the department of Electrical and Computer Engineering at Carnegie Mellon University, after spending 5 years at The Ohio State University. She is a recipient of IEEE Signal Processing Society Young Author Best Paper Award, NSF CAREER Award, AFOSR YIP Award, and ONR YIP Award. She is an Elected Member of the MLSP and SPTM Technical Committees of the IEEE Signal Processing Society since January 2016. Her research interests are in the mathematics of data representation that take advantage of structures and geometry to minimize complexity and improve performance. Specific topics include mathematical and statistical signal processing, machine learning, large-scale optimization, sampling and information theory, with applications in sensing, imaging and data science.
Bio: Antonio G. Marques received the telecommunications engineering degree and the Doctorate degree, both with highest honors, from the Carlos III University of Madrid, Madrid, Spain, in 2002 and 2007, respectively. In 2007, he became a faculty in the Department of Signal Theory and Communications, King Juan Carlos University, Madrid, Spain, where he currently develops his research and teaching activities as an Associate Professor. From 2005 to 2015, he held different visiting positions at the University of Minnesota, Minneapolis, MN, USA. In 2015 and 2016, he was a Visitor Scholar in the University of Pennsylvania, Philadelphia, PA, USA. His research interests lie in the areas of signal processing, networking and communications. His current research focuses on stochastic optimization of wireless and power networks, signal processing for graphs, and nonlinear network optimization. He has served the IEEE in a number of posts, collaborating on the organization of more than 20 IEEE conferences and workshops. Currently, he is an Associate Editor of the SIGNAL PROCESSING LETTERS, a member of the IEEE Signal Processing Theory and Methods Technical Committee and a member of the IEEE Signal Processing for Big Data Special Interest Group. Dr. Marques’ work has been awarded in several conferences and workshops, with recent best paper awards including Asilomar 2015, IEEE SSP 2016 and IEEE SAM 2016.
Bio: Georgios B. Giannakis (Fellow’97) received his Diploma in Electrical Engr. from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the University of Virginia from 1987 to 1998, and since 1999 he has been a professor with the Univ. of Minnesota, where he holds a Chair in Wireless Telecommunications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 400 journal papers, 700 conference papers, 25 book chapters, two edited books and two research monographs (h-index 130). Current research focuses on big data analytics, wireless cognitive radios, network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 32 patents issued, and the (co-) recipient of 9 best paper awards from the IEEE Signal Processing (SP) and Communications Societies, including the G. Marconi Prize Paper Award in Wireless Communications. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), a Young Faculty Teaching Award, the G. W. Taylor Award for Distinguished Research from the University of Minnesota, and the inaugural IEEE Fourier Technical Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in a number of posts including that of a Distinguished Lecturer for the IEEE-SP Society.
Bio: Dr. Longbo Huang is an assistant professor at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University, Beijing, China. He received his Ph.D. in EE from the University of Southern California, and then worked as a postdoctoral researcher in the EECS dept. at University of California at Berkeley before joining IIIS. Dr. Huang was a visiting scientist at the Simons Institute for the Theory of Computing at UC Berkeley in Fall 2016. Dr. Huang has held various visiting positions at the LIDS lab at MIT, the Chinese University of Hong Kong, Bell-labs France, and Microsoft Research Asia (MSRA). He was selected into China’s Youth 1000-talent program in 2013, and received the outstanding teaching award from Tsinghua university in 2014. Dr. Huang received the Google Research Award and the Microsoft Research Asia Collaborative Research Award in 2014, and was selected into the MSRA StarTrack Program in 2015. Dr. Huang has served as TPC members for top-tier IEEE and ACM conferences, and as the TPC vice-chair for submissions for IEEE WiOpt 2016, the Publicity Chair for ACM e-Energy 2017-2018, and the Workshop Chair for Sigmetrics 2018. Dr. Huang was the lead guest editor for the JSAC special issue on “Human-In-The-Loop Mobile Networks” in 2016. He currently serves as an editor for IEEE Transactions on Communications (TCOM), and an associate editor for ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ToMPECS). Dr. Huang’s current research interests are in the areas of stochastic modeling and analysis, optimization and control, machine learning, and sharing economy.
Tsinghua-Berkeley Shenzhen Institute
Bio: Shao-Lun Huang received the B.S. degree with honor in 2008 from the Department of Electronic Engineering, National Taiwan University, Taiwan, and the M.S. and Ph.D. degree in 2010 and 2013 from the Department of Electronic Engineering and Computer Sciences, Massachusetts Institute of Technology. From 2013 to 2016, he was working as a postdoctoral researcher jointly in the Department of Electrical Engineering at the National Taiwan University and the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. Since 2016, he has joined Tsinghua-Berkeley Shenzhen Institute, where he is currently an assistant professor. His research interests include information theory, communication theory, machine learning, and social networks.
Bio: Hamid Jafarkhani is a Chancellor's Professor at the Department of Electrical Engineering and Computer Science, University of California, Irvine, where he is also the Director of Center for Pervasive Communications and Computing and the Conexant-Broadcom Endowed Chair. Among his awards are the IEEE Marconi Prize Paper Award in Wireless Communications, the IEEE Communications Society Award for Advances in Communication, and the IEEE Eric E. Sumner Award. Dr. Jafarkhani is listed as a highly cited researcher in http://www.isihighlycited.com. According to the Thomson Scientific, he is one of the top 10 most-cited researchers in the field of "computer science" during 1997-2007. He is the 2017 Innovation Hall of Fame Inductee at the University of Maryland's School of Engineering, a Fellow of AAAS, an IEEE Fellow, and the author of the book "Space-Time Coding: Theory and Practice."
Bio: Young-Han Kim received his B.S. degree in Electrical Engineering from Seoul National University in 1996 and his Ph.D. degree in Electrical Engineering (M.S. degrees in Statistics and in Electrical Engineering) from Stanford University in 2006. Since then he has been a faculty member in the Department of Electrical and Computer Engineering at the University of California, San Diego, where he is currently a Professor. Professor Kim is a recipient of the NSF CAREER Award (2008), the US-Israel BSF Bergmann Memorial Award (2009), the IEEE Information Theory Paper Award (2012), and the first IEEE James L. Massey Research and Teaching Award (2015). He is an IEEE Fellow. His research interests include information theory, communication engineering, and data science. He has coauthored the book Network Information Theory (Cambridge University Press, 2011), which has been used widely as a textbook on the topic.
Bio: Gerhard Kramer is Alexander von Humboldt Professor and Chair of Communications Engineering at the Technical University of Munich (TUM). He received the B.Sc. and M.Sc. degrees in electrical engineering from the University of Manitoba, Canada, in 1991 and 1992, respectively, and the Dr. sc. techn. degree from the ETH Zurich, Switzerland, in 1998. From 1998 to 2000, he was with Endora Tech AG in Basel, Switzerland, and from 2000 to 2008 he was with the Math Center at Bell Labs in Murray Hill, NJ. He joined the University of Southern California (USC), Los Angeles, CA, as a Professor of Electrical Engineering in 2009. He joined TUM in 2010. Gerhard Kramer’s research interests are primarily in information theory and communications theory, with applications to wireless, copper, and optical fiber networks.
Bio: Geert Leus received the M.Sc. and Ph.D. degree in Electrical Engineering from the KU Leuven, Belgium, in June 1996 and May 2000, respectively. Geert Leus is now an "Antoni van Leeuwenhoek" Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands. His research interests are in the broad area of signal processing, with a specific focus on wireless communications, array processing, sensor networks, and graph signal processing. Geert Leus received a 2002 IEEE Signal Processing Society Young Author Best Paper Award and a 2005 IEEE Signal Processing Society Best Paper Award. He is a Fellow of the IEEE and a Fellow of EURASIP. Geert Leus was a Member-at-Large of the Board of Governors of the IEEE Signal Processing Society, the Chair of the IEEE Signal Processing for Communications and Networking Technical Committee, and the Editor in Chief of the EURASIP Journal on Advances in Signal Processing. He was also on the Editorial Boards of the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, the IEEE Signal Processing Letters, and the EURASIP Journal on Advances in Signal Processing. Currently, he is a Member of the IEEE Sensor Array and Multichannel Technical Committee, an Associate Editor of Foundations and Trends in Signal Processing, and the Editor in Chief of EURASIP Signal Processing.
Bio: Tie Liu received his B.S. (1998) and M.S. (2000) degrees, both in Electrical Engineering, from Tsinghua University, Beijing, China and a second M.S. degree in Mathematics (2004) and a Ph.D. degree in Electrical and Computer Engineering (2006) from the University of Illinois at Urbana-Champaign. Since August 2006 he has been with Texas A&M University, where he is currently a Professor in the Department of Electrical and Computer Engineering. His primary research interest is in the area of information theory and statistical information processing. Dr. Liu received an M. E. Van Valkenburg Graduate Research Award (2006) from the University of Illinois at Urbana-Champaign and a CAREER Award (2009) from the National Science Foundation. He was a Technical Program Committee Co-Chair for the 2008 IEEE GLOBECOM, a General Co-Chair for the 2011 IEEE North American School of Information Theory, and an Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory during 2014-2016.
Bio: Chandra Nair's research interests and contributions have been in developing ideas, tools, and techniques to tackle families of combinatorial and non-convex optimization problems arising primarily in the information sciences. His recent research focus has been on studying the optimality of certain inner and outer bounds to capacity regions for fundamental problems in multiuser information theory. Chandra Nair got his Bachelor's degree from IIT Madras (India) where he was the Philips (India) and Siemens (India) award winner for the best academic performance; and his master's and Ph.D. from Stanford, where he was a Stanford graduate fellow (00-04) and a Microsoft graduate fellow (04-05). He is a fellow of IEEE and is currently a distinguished lecturer of the IEEE Information theory society. He received the 2016 Information Theory Society paper award for developing a novel way to establish optimality of Gaussian distributions. Chandra Nair is an Associate Professor with the Information Engineering department at The Chinese University of Hong Kong, where he also serves as the Programme Director of the undergraduate program on Mathematics and Information Engineering and as the Director of the Institute of Theoretical Computer Science and Communications.
Bio: Dr. Zhi Tian has been a Professor in the Electrical and Computer Engineering Department of George Mason University since 2015. Previously she was on the faculty of Michigan Technological University, and served a three-year term as a Program Director at the US National Science Foundation. Her research interests lie in statistical signal processing, wireless communications, and decentralized network optimization. She is an IEEE Fellow. She is Chair of the IEEE Signal Processing Society Big Data Special Interest Group. She was General Co-Chair of the IEEE GlobalSIP Conference in 2016. She served as an IEEE Distinguished Lecturer, and Associate Editor for the IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing.
Bio: Raymond W. Yeung is a Choh-Ming Li Professor of Information Engineering at The Chinese University of Hong Kong (CUHK). He received his B.S., M.Eng., and PhD degrees from Cornell University in electrical engineering in 1984, 1985, and 1988, respectively. Before joining CUHK in 1991, he was a Member of Technical Staff at AT&T Bell Laboratories. A co-founder of the field of network coding, he has been serving as Co-Director of the Institute of Network Coding at CUHK since 2010. He is the author of the books A First Course in Information Theory (Kluwer Academic/Plenum Publishers, 2002) and Information Theory and Network Coding (Springer 2008), which have been adopted by over 100 institutions around the world. In spring 2014, he gave the first MOOC in the world on information theory that reached over 25,000 students. He was a recipient of the 2005 IEEE Information Theory Society Paper Award, the Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation in 2007, the 2016 IEEE Eric E. Sumner Award (Citation: “For pioneering contributions to the field of network coding”), and the 2018 ACM SIGMOBILE Test-of-Time Paper Award. In 2015, he was named an Outstanding Overseas Chinese Information Theorist by the China Information Theory Society. He is a Fellow of the IEEE, Hong Kong Academy of Engineering Sciences, and Hong Kong Institution of Engineers.
Bio: Dapeng Oliver Wu received Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA, in 2003. Since 2003, he has been on the faculty of Electrical and Computer Engineering Department at University of Florida, Gainesville, FL, where he is currently Professor. His research interests are in the areas of networking, communications, video coding, image processing, computer vision, signal processing, and machine learning. He received University of Florida Term Professorship Award in 2017, University of Florida Research Foundation Professorship Award in 2009, AFOSR Young Investigator Program (YIP) Award in 2009, ONR Young Investigator Program (YIP) Award in 2008, NSF CAREER award in 2007, the IEEE Circuits and Systems for Video Technology (CSVT) Transactions Best Paper Award for Year 2001, the Best Paper Award in GLOBECOM 2011, and the Best Paper Award in QShine 2006. Currently, he serves as Editor-in-Chief of IEEE Transactions on Network Science and Engineering, and Associate Editor of IEEE Transactions on Communications, IEEE Transactions on Signal and Information Processing over Networks, and IEEE Signal Processing Magazine. He was the founding Editor-in-Chief of Journal of Advances in Multimedia between 2006 and 2008, and an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Wireless Communications and IEEE Transactions on Vehicular Technology. He has served as Technical Program Committee (TPC) Chair for IEEE INFOCOM 2012. He was elected as a Distinguished Lecturer by IEEE Vehicular Technology Society in 2016. He is an IEEE Fellow.
Bio: Jiaming Xu is an assistant professor in the Krannert School of Management at Purdue University which he joined in Fall 2016. Before that, he was a research fellow with the Simons Institute for the Theory of Computing, UC Berkeley, and a postdoctoral fellow with the Statistics Department, The Wharton School, University of Pennsylvania. He received the Ph.D. degree from UIUC in 2014 under the supervision of Prof. Bruce Hajek, the M.S. degree from UT-Austin in 2011, and the B.E. degree from Tsinghua University, all in ECE. His research interests include high-dimensional statistical inference, information theory, convex and non-convex optimization, queueing theory, and game theory.
Bio: Wei Yu received the B.A.Sc. degree in Computer Engineering and Mathematics from the University of Waterloo, Waterloo, Ontario, Canada in 1997 and M.S. and Ph.D. degrees in Electrical Engineering from Stanford University, Stanford, CA, in 1998 and 2002, respectively. Since 2002, he has been with the Electrical and Computer Engineering Department at the University of Toronto, Canada, where he is now Professor and holds a Canada Research Chair (Tier 1) in Information Theory and Wireless Communications. Prof. Wei Yu currently serves on the IEEE Information Theory Society Board of Governors. He is currently the Chair of the Signal Processing for Communications and Networking Technical Committee of the IEEE Signal Processing Society. He is an Area Editor for the IEEE Transactions on Wireless Communications (2017-20). Prof. Wei Yu was an IEEE Communications Society Distinguished Lecturer (2015-16). He received the IEEE Communications Society Best Tutorial Paper Award in 2015, the IEEE Signal Processing Society Best Paper Award in 2008 and 2017, and the Journal of Communications and Networks Best Paper Award in 2017. Prof. Wei Yu is a Fellow of IEEE, a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada, and a Fellow of Canadian Academy of Engineering.
Bio: This talk approaches the overparameterization challenge from above and below: First, we investigate generalization bounds of popular deep neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and discuss their implications and insufficiencies; Second, we study simpler but nontrivial overparametrized models in machine learning, including matrix factorization, principal component analysis, deep linear networks, which allows us to make progress toward understanding and gaining more insights of overparameterization in deep neural networks.