Rüdiger L. Urbanke obtained his Dipl. Ing. degree from TU of Vienna in 1990 and the M.Sc. and PhD degrees in EE from Washington University in St. Louis, MO, in 1992
and 1995, respectively.
He held a position at the Mathematics of Communications Department at Bell Labs from 1995 till 1999 before becoming a faculty member in I&C at EPFL. He is a member of the Information Processing Group.
For an extended period he was principally interested in the analysis and design of iterative coding schemes, which allow reliable transmission close to theoretical limits at low complexities. Such schemes are part of most modern communications standards, including wireless transmission, optical communication and hard disk storage. His current research interests have shifted to the theory of ML and problems in quantum communication and computation.
(Technion – Israel Institute of Technology, Israel)
My Little Hammers and Screwdrivers for Analyzing Code Ensemble Performance
8:30-9:30 June 26 / ROOM 101
In this talk, I will give an overview of several new analytical tools, that I have gradually developed during the last decade, for assessing the performance of code ensembles in a variety of scenarios of coded communication configurations. By bypassing the traditional use certain well-known inequalities, such as Jensen's inequality and others, these analytical tools often enable exponentially tight evaluations of the average error probability, the excess distortion probability, and other types of large deviations probabilities. These tools were inspired by models and methods of statistical physics. In particular, the first tool that I will describe, which I will refer to as "type-class enumeration method", was inspired by the so-called random energy model (REM) of spin glasses in statistical mechanics, as there is a remarkable degree of similarity and parallelism between the REM and the paradigm of random coding. The second tool is a continuous-alphabet analogue of the method of types, without recourse to quantization arguments that convert the problem back to the realm of finite alphabets. In this context, one of the powerful techniques is saddle-point integration. The results obtained from these tools will be demonstrated in a variety of examples. Finally, time permits, I will also discuss additional techniques, like integral representations of the logarithmic function and non-integer power functions, with applications to information theory, as well as methods for reversing Jensen's inequality and manipulating it in various ways.
Neri Merhav has received the B.Sc., M.Sc., and D.Sc. degrees from the Technion, Israel Institute of Technology, in 1982, 1985, and 1988, respectively, all in electrical engineering. From 1988 to 1990 he was with AT&T Bell Laboratories, Murray Hill, NJ, USA. Since 1990 he has been with the Electrical Engineering Department (now, the Viterbi ECE Department) of the Technion, where he is the Irving Shepard Professor. During 1994-2000 he was also serving as a consultant to the Hewlett--Packard Laboratories - Israel (HPL-I). His research interests include information theory, statistical communications, and statistical signal processing. He is especially interested in the areas of lossless/lossy source coding and prediction/filtering, relationships between information theory and statistics, detection, estimation, as well as in the area of Shannon Theory, including topics in joint source--channel coding, source/channel simulation, and coding with side information with applications to information hiding and watermarking systems. Another recent research interest concerns the relationships between Information Theory and statistical physics. Dr. Merhav was a co-recipient of the 1993 Paper Award of the IEEE Information Theory Society and he is a Fellow of the IEEE since 1999 and a Life Fellow since 2023. He also received the 1994 American Technion Society Award for Academic Excellence and the 2002 Technion Henry Taub Prize for Excellence in Research. More recently, he was a co-recipient of the Best Paper Award of the 2015 IEEE Workshop on Information Forensics and Security (WIFS 2015).During 1996-1999 he served as an Associate Editor for Source Coding to the IEEE Transactions on Information Theory, and during 2017-2020 - as an Associate Editor for Shannon Theory in the same journal. He also served as a co-chairperson of the Program Committee of the 2001 IEEE International Symposium on Information Theory. He is currently on the Editorial Board of Foundations and Trends in Communications and Information Theory.
What is the densest lattice sphere packing in the d-dimensional Euclidean space? In this talk we will investigate this question as dimension goes to infinity and we will focus on the lower bounds for the best packing density, or in other words on the existence results. I will give a historical overview of the existence of dence lattice packings by H. Minkowski, E. Hlawka, C. L. Siegel, C. A. Rogers, and more recently by S. Vance and A. Venkatesh. At the final part of the lecture, I will present a recent work done in collaboration with V. Serban and N. Gargava on the moments of the number of lattice points in a bounded set for random lattices constructed from a number field.
Maryna Viazovska did her bachelor studies at the Kyiv National Taras Shevchenko University and completed her MSc at the Technical University Kaiserslautern. She obtained her PhD in 2013 in Bonn. She was a postdoctoral researcher at the Institut des Hautes Etudes Scientifiques and at the Humboldt University of Berlin, and in 2017 was a Minerva Distinguished Visitor at Princeton University. She joined EPFL in 2017 as Tenure-Track Assistant Professor and was promoted Full Professor in 2018.
Random graphs, statistical physics and information
8:30-9:30 June 28 / ROOM 101
Statistical physics models for disordered materials provide precise predictions about the efficiency of communication codes and the typical complexity of certain combinatorial optimization problems. The underlying common structure is that of many discrete variables, whose interaction is represented by a random sparse graph. I will discuss recent progress in proving some of these predictions. In particular, on the emerging theory of nonlinear large deviations, yielding mean field approximation for certain Gibbs measures and an information theoretic criterion for representing such measures as mixtures of not too many product measures.
Amir Dembo obtained his Ph.D. in Electrical Engineering from Technion, Israel. Since 1990, he has been on the faculty of Stanford University and since 2012 as the Marjorie Mhoon Fair Professor in Quantitative Science. His areas of specialization are probability theory and stochastic processes, information theory, large deviations, and their applications in communication, control, and biomolecular sequence analysis. Together with Ofer Zeitouni, he has authored a book on the theory of large deviations which is now a classical reference in the field. He has served as editor of Probability Theory and Related Fields and of the Annals of Probability. He is a fellow of the Institute of Mathematical Statistics, a member of the National Academy of Sciences and in 2023 was elected to the American Academy of Arts and Sciences.
(UNINA (University of Naples Federico II) & NYU (New York University))
A Service-Driven Network Evolution: from Communication, to Content Distribution, to Ubiquitous Computation
8:30-9:30 June 30 / ROOM 101
In this talk, we will explore recent advances in optimizing next-generation services over increasingly integrated computation-communication networks. We will discuss the evolution of digital services, from communication to content distribution - with a particular focus on the critical role of the wireless edge - and finally delve into real-time computation overviewing increasingly relevant resource-intensive and latency-sensitive services and applications. The objective is to provide valuable insights on future network design and end-to-end service optimization for real-time performance and enhanced customer experience.
Antonia M. Tulino (Fellow, IEEE) is currently a Full Professor with the Università degli Studi di Napoli Federico II. She held a research positions at the Center for Wireless Communications in Oulu, Princeton University, the Università degli Studi del Sannio, Italy, and Bell Laboratories, NJ, USA. Since 2019, she has been a Research Professor with the Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, and the Director of the 5G Academy jointly organized by the Università degli Studi di Napoli, Federico II, and several leading ICT companies. Her research interests lay in the area of communication networks approached with the complementary tools provided by signal processing, information theory, and random matrix theory. From 2011 to 2013, she was a member of the Editorial Board of the IEEE Transactions on Information Theory. In 2013, she was elevated to IEEE Fellow. She has received several paper awards, including the 2009 Stephen O. Rice Prize in the field of communications theory. She was a recipient of the UC3M-Santander Chair of Excellence from 2018 to 2019 and selected by the National Academy of Engineering for the Frontiers of Engineering Program in 2013. From 2019 to 2021, she was the Chair of the Information Theory society Fellows Committee.