Plenary Sessions

Shannon Lecture

Information: From Theory to Practice

Thursday, June 30, 8:30-9:30 (EEST, local Helsinki time)

   Raymond W. Yeung

Raymond W. Yeung is a Choh-Ming Li Professor of Information Engineering at The Chinese University of Hong Kong (CUHK). He received his PhD degree in electrical engineering from Cornell University in 1988. Before joining CUHK in 1991, he was a Member of Technical Staff at AT&T Bell Laboratories. He has been serving as Co-Director of the Institute of Network Coding at CUHK since 2010. His two textbooks on information theory have been adopted by over 100 institutions around the world. In 2014, he gave the first MOOC on information theory that has reached over 60,000 students to date.

He has received a number of awards for his research contributions. These include 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, the 2018 ACM SIGMOBILE Test-of-Time Paper Award, the 2021 IEEE Richard W. Hamming Medal, and the 2022 Claude E. Shannon 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.

 

Information theory is a mathematical theory with inherent beauty, but with coding as its executive arm, practice has always been a lighthouse for the subject. However, the connection between theory and practice is at times obscure and may come in unexpected ways. In this lecture, I will try to explore this connection by reviewing my own research in the past more than three decades.

whyeung

Raymond W. Yeung

Thursday, June 30th, 8:30-9:30 (EEST, local Helsinki time)

Raymond W. Yeung is a Choh-Ming Li Professor of Information Engineering at The Chinese University of Hong Kong (CUHK). He received his PhD degree in electrical engineering from Cornell University in 1988. Before joining CUHK in 1991, he was a Member of Technical Staff at AT&T Bell Laboratories. He has been serving as Co-Director of the Institute of Network Coding at CUHK since 2010. His two textbooks on information theory have been adopted by over 100 institutions around the world. In 2014, he gave the first MOOC on information theory that has reached over 60,000 students to date.

He has received a number of awards for his research contributions. These include 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, the 2018 ACM SIGMOBILE Test-of-Time Paper Award, the 2021 IEEE Richard W. Hamming Medal, and the 2022 Claude E. Shannon 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.

Plenary Speakers

Communication and Sensing: From Compressed Sampling to Model-based Deep Learning

Monday, June 27, 8:30-9:30 (EEST, local Helsinki time)

   Yonina Eldar

Yonina C. Eldar is a Professor in the Department of Math and Computer Science at the Weizmann Institute of Science, Rehovot, Israel, where she heads the center for Biomedical Engineering and Signal Processing. She is also a Visiting Professor at MIT and at the Broad Institute and an Adjunct Professor at Duke University, and was a Visiting Professor at Stanford University. She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award, the IEEE/AESS Fred Nathanson Memorial Radar Award, the IEEE Kiyo Tomiyasu Award, the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, and the Wolf Foundation Krill Prize for Excellence in Scientific Research. She is the Editor in Chief of Foundations and Trends in Signal Processing, and serves the IEEE on several technical and award committees. She heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel.

The famous Shannon-Nyquist theorem has become a landmark in analog to digital conversion and the development of digital signal processing algorithms. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Furthermore, the resulting high rate digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power.  In this talk we consider a general framework for communication and sensing including sub-Nyquist sampling, quantization and processing in space, time and frequency which allows to dramatically reduce the number of antennas, sampling rates, number of bits and band occupancy in a variety of applications.  Our framework relies on exploiting signal structure, quantization and the processing task in both standard processing and in deep learning networks leading to a new framework for model-based deep learning. It also allows for the development of efficient joint radar-communication systems and near-field processing. We consider applications of these ideas to a variety of problems in wireless communications, imaging, massive MIMO systems, automotive radar and ultrasound imaging and show several demos of real-time prototypes including a wireless ultrasound probe, sub-Nyquist automotive radar, cognitive radio and radar, dual radar-communication systems, analog precoding, sparse antenna arrays, and a deep Viterbi decoder. We end by discussing more generally how models can be used in deep learning methods with application to a variety of communication settings.

On Dynamics-Informed Blending of Machine Learning and Game Theory

Tuesday, June 28, 8:30-9:30 (EEST, local Helsinki time)

   Michael Jordan

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of EECS and the Department of Statistics at the University of California, Berkeley. His research interests include machine learning, optimization, and control theory. Prof. Jordan is a member of the National Academy of Sciences and the National Academy of Engineering, and is a Foreign Member of the Royal Society. He has given a Plenary Lecture at the International Congress of Mathematicians, he has received the IEEE John von Neumann Medal, the IJCAI Research Excellence Award, the AMS Ulf Grenander Prize in Stochastic Theory and Modeling, the David Rumelhart Prize, the ACM/AAAI Allen Newell Award, and he holds an Honorary Doctorate from Yale University.

Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Managing such sharing is one of the classical goals of microeconomics, and it is given new relevance in the modern setting of large, human-focused datasets, and in data-analytic contexts such as classifiers and recommendation systems. I’ll discuss several recent projects
that aim to explore the interface between machine learning and microeconomics, including leader/follower dynamics in strategic classification, the robust learning of optimal auctions, a Lyapunov theory for matching markets with transfers, and the use of contract theory as a way to design mechanisms for statistical inference.

How Digital Platforms are Radically Transforming Organizations

Wednesday, June 29, 8:30-9:30 (EEST, local Helsinki time)

   Bengt Holmström

Bengt Robert Holmström is the Paul A. Samuelson Professor of Economics, Emeritus, at Massachusetts Institute of Technology, where he was head of the Economics Department from 2003-2006. He held a joint appointment with MIT’s Sloan School of Management. Holmström received his doctoral degree from Stanford University in 1978. Before joining MIT in 1994, he was the Edwin J. Beinecke Professor of Management at Yale University’s School of Management (1983-94) and associate professor at the Kellogg Graduate School of Management at Northwestern University (1979-82). Holmström is a microeconomic theorist, best known for his research on the theory of contracting and incentives especially as applied to the theory of the firm, to corporate governance and to liquidity problems in financial crises. In 2011, he co-authored the book with Jean Tirole. He was awarded the 2016 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for his contributions to contract theory (together with Oliver Hart). He is a former board member of the Nokia Corporation (1999-2012) and Aalto University (2010-2017) and serves on several academic advisory boards, including Toulouse School of Economics and Luohan Academy.

One of the enduring questions in economics is the role played by institutions, contracts and organizations. Informational frictions are the central ingredient in understanding organizations and how they have evolved over time and across environments. Digitalization has changed informational frictions and organizations in many ways, but the biggest changes have come in recent years with the emergence of digital platforms. The talk will focus on digital platforms and the revolutionary impact they are having on all aspects of commerce including innovation, trade and banking. The emphasis is on the economic value that digital platforms create and the economic drivers and technological enablers behind it. The lecture illustrates how organizational innovations are essential for utilizing the fruits of technological innovations but also the role regulations must play to distribute the fruits in socially accepted ways.

Learning from humans how to improve lossy data compression

Friday, July 1, 8:30-9:30 (EEST, local Helsinki time)

   Tsachy Weissman

Tsachy Weissman has been on the faculty of the Electrical Engineering department at Stanford since 2003, researching and teaching the science of information, with applications spanning genomics, neuroscience, and technology.  He has been serving on editorial boards for scientific journals, technical advisory boards in industry, and as founding director of the Stanford Compression Forum. His recent projects include the STEM2SHTEM science and humanities summer internship program for high schoolers, and Stagecast, a low-latency video platform allowing actors and singers to perform together in real-time while geographically distributed due to the pandemic. An IEEE fellow, he has received multiple awards for his research and teaching, including best paper awards from the IEEE Information Theory and Communications societies, while his students received best student authored paper awards at the top conferences of their areas of scholarship. He has prototyped Guardant Health’s first algorithms for early detection of cancer from blood tests, and has more recently co-founded and sold Compressable to Amazon, reducing humanity’s cloud storage carbon footprint. His favorite gig to date was advising the HBO show “Silicon Valley”.

In the summer of 2018, inspired by a couple of high schoolers and Shannon’s work on estimating the entropy of printed English, we experimented with a framework for image compression comprising a human describing images using text instructions to another human who was tasked with reconstructing the original image. The reconstructions were rated by human scorers on Mechanical Turk and compared to reconstructions obtained by existing image compressors. The results suggested potential for substantial improvements over current approaches to multimedia data compression by harnessing the power of human language, knowledge, and perception. I’ll describe subsequent and ongoing work geared toward understanding and realizing this potential, as well as related challenges for the information theory community to consider.

Yonina Eldar

Monday, June 27th, 8:30-9:30 (EEST, local Helsinki time)

Yonina C. Eldar is a Professor in the Department of Math and Computer Science at the Weizmann Institute of Science, Rehovot, Israel, where she heads the center for Biomedical Engineering and Signal Processing. She is also a Visiting Professor at MIT and at the Broad Institute and an Adjunct Professor at Duke University, and was a Visiting Professor at Stanford University. She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical  Achievement Award, the IEEE/AESS Fred Nathanson Memorial Radar Award, the IEEE Kiyo Tomiyasu Award, the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, and the Wolf Foundation Krill Prize for Excellence in Scientific Research. She is the Editor in Chief of Foundations and Trends in Signal Processing, and serves the IEEE on several technical and award committees. She heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel.

Bengt Holmström 

Wednesday, June 29th, 8:30-9:30 (EEST, local Helsinki time)

Bengt Robert Holmström is the Paul A. Samuelson Professor of Economics, Emeritus, at Massachusetts Institute of Technology, where he was head of the Economics Department from 2003-2006. He held a joint appointment with MIT’s Sloan School of Management. Holmström received his doctoral degree from Stanford University in 1978. Before joining MIT in 1994, he was the Edwin J. Beinecke Professor of Management at Yale University’s School of Management (1983-94) and associate professor at the Kellogg Graduate School of Management at Northwestern University (1979-82). Holmström is a microeconomic theorist, best known for his research on the theory of contracting and incentives especially as applied to the theory of the firm, to corporate governance and to liquidity problems in financial crises. In 2011, he co-authored the book with Jean Tirole. He was awarded the 2016 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for his contributions to contract theory (together with Oliver Hart). He is a former board member of the Nokia Corporation (1999-2012) and Aalto University (2010-2017) and serves on several academic advisory boards, including Toulouse School of Economics and Luohan Academy.

Michael Jordan

Tuesday, June 28th, 8:30-9:30 (EEST, local Helsinki time)

Michael I. Jordan is the Pehong Chen Distinguished Professor in the
Department of EECS and the Department of Statistics at the University
of California, Berkeley.  His research interests include machine learning,
optimization, and control theory.  Prof. Jordan is a member of the National
Academy of Sciences and the National Academy of Engineering, and is a
Foreign Member of the Royal Society.  He has given a Plenary Lecture
at the International Congress of Mathematicians, he has received the
IEEE John von Neumann Medal, the IJCAI Research Excellence Award, the
AMS Ulf Grenander Prize in Stochastic Theory and Modeling, the David
Rumelhart Prize, the ACM/AAAI Allen Newell Award, and he holds an
Honorary Doctorate from Yale University.

Tsachy Weissman

Friday, July 1st, 8:30-9:30 (EEST, local Helsinki time)

Tsachy has been on the faculty of the Electrical Engineering department at Stanford since 2003, researching and teaching the science of information, with applications spanning genomics, neuroscience, and technology.  He has been serving on editorial boards for scientific journals, technical advisory boards in industry, and as founding director of the Stanford Compression Forum. His recent projects include the STEM2SHTEM science and humanities summer internship program for high schoolers, and Stagecast, a low-latency video platform allowing actors and singers to perform together in real-time while geographically distributed due to the pandemic. An IEEE fellow, he has received multiple awards for his research and teaching, including best paper awards from the IEEE Information Theory and Communications societies, while his students received best student authored paper awards at the top conferences of their areas of scholarship. He has prototyped Guardant Health’s first algorithms for early detection of cancer from blood tests, and has more recently co-founded and sold Compressable to Amazon, reducing humanity’s cloud storage carbon footprint. His favorite gig to date was advising the HBO show “Silicon Valley”.