Andrew Barron is the 2024 Shannon Lecturer.
The ISIT 2024 plenary speakers are (in alphabetical order):
Shannon Lecture
Andrew Barron (Yale University, USA)
Biography: Andrew R Barron, Professor of Statistics and Data Science at Yale University, has made outstanding contributions at the overlap of Information Theory with Probability and Statistics. Prior to joining Yale University in 1992, Barron was a faculty member in Statistics and Electrical and Computer Engineering at the University of Illinois at Urbana Champaign. Barron received his MS and PhD degrees from Stanford University in Electrical Engineering in 1985 under the direction of Tom Cover and a Bachelor's degree in the fields of Mathematical Science and Electrical Engineering from Rice University in 1981. Barron is a Fellow of the IEEE, a Medallion Prize winner of the Institute of Mathematical Statistics, and a winner along with Bertrand Clarke of the IEEE Thompson Prize. Andrew Barron has served as a Secretary of the Board of Governors of the IEEE Information Theory Society and several terms as an elected member of this Board. He has been an associate editor of the IEEE Transactions on Information Theory and the Annals of Statistics. Barron has served on and subsequently chaired the Thomas M. Cover Dissertation Prize Committee. At Yale University, Barron regularly teaches courses in Information Theory, Theory of Statistics, High-Dimensional Function Estimation and Artificial Neural Networks. Barron has served terms as department chair, director of graduate studies, director of undergraduate studies in Statistics, director of undergraduate studies in Applied Mathematics, and courtesy appointee as Professor of Electrical Engineering. Barron has proudly mentored 20 PhD students. Often working with these students and other colleagues, Barron is known for several specific research accomplishments: in particular, for generalizing the AEP to continuous-valued ergodic processes, for proving an information-theoretic Central Limit Theorem, for determining information-theoretic aspects of portfolio estimation, for formulating the index of resolvability and providing an associated characterization of performance of Minimum Description Length estimators, for determining the asymptotics of universal data compression in parametric families, for characterizing the concentration of Bayesian posteriors in the vicinity of parameters in the information support of the prior, for an information-theoretic determination of the minimax rates of function estimation, for providing information-theoretic characterization of statistical efficiency, for providing an early unifying view of statistical learning networks, for developing approximation and estimation bounds for artificial neural networks and recent extensions to deep learning, for advancing greedy algorithms for training neural networks, for information-theoretic aggregation of least squares regressions, and for formulating and proving capacity-achieving sparse regression codes for Gaussian noise communication channels. Barron maintains homes in New Haven, Connecticut and in Osijek, Croatia with his wife Lidija. Barron is also a distinguished FAI free flight model glider competitor in the F1A class, as a five time U.S. National Champion, a four time U.S. National Team Member at World Championships (most recently in 2023), as a two time America's Cup Champion, and as a co-manager and co-owner with family members of Barron Field, LLC.
Plenary Talks
Venkatesan Guruswami (University of California, Berkeley, USA)
Biography: Venkatesan Guruswami received his Bachelor's degree in Computer Science from the Indian Institute of Technology at Madras in 1997 and his Ph.D. in Computer Science from the Massachusetts Institute of Technology in 2001. He is currently a Chancellor’s Professor in the Electrical Engineering and Computer Science Department at the University of California, Berkeley, and a senior scientist at the Simons Institute for the Theory of Computing. He was a Miller Research Fellow at UC Berkeley and held faculty positions at the University of Washington and Carnegie Mellon University prior to his current position. His research interests span many topics such as coding and information theory, approximate optimization, computational complexity, pseudo-randomness, and related mathematics. Prof. Guruswami has served the theoretical computer science community in several leadership roles. He is the current Editor-in-Chief of the Journal of the ACM, and was previously Editor-in-Chief of the ACM Transactions on Computation Theory. He has served as the president of the Computational Complexity Foundation and on the editorial boards of JACM, the SIAM Journal on Computing and the IEEE Transactions on Information Theory. He has been program committee chair for the conferences CCC (2012), FOCS (2015), ISIT (2018, co-chair), FSTTCS (2022), and ITCS (2024). Prof. Guruswami is a recipient of a Guggenheim Fellowship, a Simons Investigator award, the Presburger Award, Packard and Sloan Fellowships, the ACM Doctoral Dissertation Award, an IEEE Information Theory Society Paper Award and a Distinguished Alumnus Award from IIT Madras. He was an invited speaker at the 2010 International Congress of Mathematicians. Prof. Guruswami is a fellow of the ACM, IEEE, and AMS.
Emina Soljanin (Rutgers University, USA)
Biography: Emina Soljanin is a Distinguished Professor of Electrical and Computer Engineering at Rutgers University. Before moving to Rutgers in January 2016, she was a (Distinguished) Member of Technical Staff for 21 years in Bell Labs Math Research. She received her Ph.D. and M.Sc. from Texas A&M University and her B.S. from the University of Sarajevo, all in Electrical Engineering. Prof. Soljanin’s research interests and expertise are broad. She has participated in numerous research and business projects. These projects include designing the first distance-enhancing codes implemented in commercial magnetic storage devices, the first forward error correction for Bell Labs optical transmission devices, color space quantization for image processing, link error prediction methods for Hybrid ARQ wireless standards, network and rateless coding, and network data security and user anonymity, Her most recent activities are in distributed computing systems and quantum information science. Prof. Soljanin has served as an Associate Editor for Coding Techniques for the IEEE Transactions on Information Theory and has had various roles in other journal editorial boards, special workshop organizing, and conference program committees. She is an IEEE Fellow, an outstanding alumnus of the Texas A&M School of Engineering, the 2011 Padovani Lecturer, a 2016/17 Distinguished Lecturer, and the 2019 IEEE Information Theory Society President. Prof. Soljanin’s favorite recognition is the 2023 Aaron D. Wyner Distinguished Service Award.
Rebecca Willett (University of Chicago, USA)
Biography: Rebecca Willett is a Professor of Statistics and Computer Science and the Director of AI in the Data Science Institute at the University of Chicago, and she holds a courtesy appointment at the Toyota Technological Institute at Chicago. Her research is focused on the mathematical foundations of machine learning, scientific machine learning, and signal processing. Prof. Willett is the Deputy Director for Research at the NSF-Simons Foundation National Institute for Theory and Mathematics in Biology and a member of the NSF Institute for the Foundations of Data Science Executive Committee. She is the Faculty Director of the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship at the University of Chicago and helps direct the Air Force Research Lab University Center of Excellence on Machine Learning. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group, received an Air Force Office of Scientific Research Young Investigator Program award in 2010, was named a Fellow of the Society of Industrial and Applied Mathematics in 2021, and was named a Fellow of the IEEE in 2022. Prof. Willett completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018. She serves on the advisory boards of the US National Science Foundation’s Institute for Mathematical and Statistical Innovation, the US National Science Foundation’s Institute for the Foundations of Machine Learning, and the MATH+ Berlin Mathematics Research Center, as well as National Academies of Science, Engineering and Medicine committees.
Gregory Wornell (Massachusetts Institute of Technology, USA)
Biography: Gregory W. Wornell received the B.A.Sc. degree (with honors) from the University of British Columbia, Canada, and the S.M. and Ph.D. degrees from the Massachusetts Institute of Technology, all in Electrical Engineering and Computer Science, in 1985, 1987 and 1991, respectively. Since 1991 he has been on the faculty at MIT, where he is the Sumitomo Professor of Engineering in the department of Electrical Engineering and Computer Science (EECS) and the Schwarzman College of Computing, and area co-chair of the EECS doctoral program. At MIT he leads the Signals, Information, and Algorithms Laboratory, and is affiliated with the Research Laboratory of Electronics (RLE), the Computer Science and Artificial Intelligence Laboratory (CSAIL), and the Institute for Data, Systems and Society (IDSS). Prof. Wornell has held visiting appointments at the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, CA, in 1999-2000, at Hewlett-Packard Laboratories, Palo Alto, CA, in 1999, and at AT&T Bell Laboratories, Murray Hill, NJ, in 1992-1993. His research interests and publications span the areas of signal processing, information theory, statistical inference, artificial intelligence, and information security, and include architectures for sensing, learning, computing, communication, and storage; systems for computational imaging, vision, and perception; aspects of computational biology and neuroscience; and the design of wireless networks. Prof. Wornell has been involved in the Information Theory and Signal Processing societies of the IEEE in a variety of capacities, and maintains a number of close industrial relationships and activities. He has won a number of awards for both his research and teaching, including the IEEE Leon K. Kirchmayer Graduate Teaching Award, and is a Fellow of the IEEE.