IZS 2026 Plenaries

Plenary Speakers

Helmut Bölcskei, ETH Zurich
Jonas Peters, ETH Zurich
Pascal Vontobel, The Chinese University of Hong Kong

Plenary on Wednesday:
An Introduction to Causality and some Applications

Causal models can help us with the following two tasks:

1. they can, unlike statistical models, predict how a real-world system reacts under an active perturbation;
2. they suggest ways to robustly predict a response variable under a distribution shift, that is, in a scenario, where training and test distributions differ.

We introduce the concept of causal models and discuss principles allowing us to learn causal quantities from data. We highlight a few connections to information theory and, if time allows, mention some recent results on causal methodology and applications. No prior knowledge on causality is required.

Jonas is interested in using different types of data to predict the effect of interventions and to build statistical methods that are robust with respect to distributional shifts. He seeks to combine theory and methodology and tries to let real world applications guide his research. His work relates to areas such as causal inference, distribution generalization, dynamical systems, policy learning, graphical models, and independence testing. Since 2023, Jonas is professor in statistics at ETH Zurich. Previously, he has been a professor at the Department of Mathematical Sciences at the University of Copenhagen and a group leader at the Max-Planck-Institute for Intelligent Systems in Tuebingen. He studied Mathematics at the University of Heidelberg and the University of Cambridge and obtained his PhD jointly from MPI and ETH.

Plenary on Thursday:
On Typical Permutations

Permutations play an important role in the design and analysis of many engineering systems and algorithms. This presentation will show that typical permutations play a key role when analyzing loopy-belief-propagation-based methods for approximating the permanent of a matrix consisting of non-negative-real-valued entries or complex-valued entries, in a similar way as typical sequences play an important role in information theory for analyzing data compression and reliable communication systems. Finally, the relevance of these permanent results for classical and quantum information processing systems will be discussed.

(Based on joint work with my MPhil/PhD students Yuwen Huang, Kit Shing Ng, Binghong Wu, and Junda Zhou.)

Pascal O. Vontobel received the Diploma degree in electrical engineering in 1997, the Post-Diploma degree in information techniques in 2002, and the Ph.D. degree in electrical engineering in 2003, all from ETH Zurich, Switzerland.

From 1997 to 2002 he was a research and teaching assistant at the Signal and Information Processing Laboratory at ETH Zurich, from 2006 to 2013 he was a research scientist with the Information Theory Research Group at Hewlett-Packard Laboratories in Palo Alto, CA, USA, and since 2014 he has been with the Department of Information Engineering at the Chinese University of Hong Kong, where, since 2023, he has been a (full) professor, department chairman, and graduate division head. Besides this, he was a postdoctoral research associate at the University of Illinois at Urbana-Champaign (2002-2004), a visiting assistant professor at the University of Wisconsin-Madison (2004-2005), a postdoctoral research associate at the Massachusetts Institute of Technology (2006), and a visiting scholar at Stanford University (2014). His research interests lie in information and coding theory, quantum information processing, data science, communications, and signal processing.

Dr. Vontobel was an Associate Editor for the IEEE Transactions on Information Theory (2009-2012), an Awards Committee Member of the IEEE Information Theory Society (2013-2014), a Distinguished Lecturer of the IEEE Information Theory Society (2014-2015), an Associate Editor for the IEEE Transactions on Communications (2014-2017), and a Thomas Cover Dissertation Awards Committee Member of the IEEE Information Theory Society (2023-2025). Moreover, he was / will be a TPC co-chair of the IEEE International Symposium on Information Theory (2016, 2027), the IEICE International Symposium on Information Theory and its Applications (2018), and the IEEE Information Theory Workshop (2018). He was the director of the Croucher Summer Courses in Information Theory (2021, 2023, 2025), co-organized several topical workshops, and was on the technical program committees of many international conferences. Furthermore, he was multiple times a plenary speaker at international information and coding theory conferences, he received an exemplary reviewer award from the IEEE Communications Society, and was awarded the ETH medal for his Ph.D. dissertation. He is an IEEE Fellow.

Plenary on Friday:
How big do machine learning models have to be?

Helmut Bölcskei was born in Mödling, Austria on May 29, 1970, and received the Dipl.-Ing. and Dr. techn. degrees in electrical engineering from Vienna University of Technology, Vienna, Austria, in 1994 and 1997, respectively. In 1998 he was with Vienna University of Technology. From 1999 to 2001 he was a postdoctoral researcher in the Information Systems Laboratory, Department of Electrical Engineering, and in the Department of Statistics, Stanford University, Stanford, CA. He was in the founding team of Iospan Wireless Inc., a Silicon Valley-based startup company (acquired by Intel Corporation in 2002) specialized in multiple-input multiple-output (MIMO) wireless systems for high-speed Internet access, and was a co-founder of Celestrius AG, Zurich, Switzerland. From 2001 to 2002 he was an Assistant Professor of Electrical Engineering at the University of Illinois at Urbana-Champaign. He has been with ETH Zurich since 2002, where he is a Professor of Mathematical Information Science in the Department of Electrical Engineering, also associated with the Department of Mathematics. He was a visiting researcher at Philips Research Laboratories Eindhoven, The Netherlands, ENST Paris, France, and the Heinrich Hertz Institute Berlin, Germany. His research interests are in applied mathematics, machine learning theory, mathematical signal processing, data science, and statistics.

He received the 2001 IEEE Signal Processing Society Young Author Best Paper Award, the 2006 IEEE Communications Society Leonard G. Abraham Best Paper Award, the 2010 Vodafone Innovations Award, the ETH "Golden Owl" Teaching Award, is a Fellow of the IEEE, a 2011 EURASIP Fellow, was a Distinguished Lecturer (2013-2014) of the IEEE Information Theory Society, an Erwin Schrödinger Fellow (1999-2001) of the Austrian National Science Foundation (FWF), was included in the 2014 Thomson Reuters List of Highly Cited Researchers in Computer Science, was the 2016 Padovani Lecturer of the IEEE Information Theory Society, and received a 2021 Rothschild Fellowship from the Isaac Newton Institute for Mathematical Sciences, Cambridge University, UK. He served as an associate editor of the IEEE Transactions on Information Theory, the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, and the EURASIP Journal on Applied Signal Processing. He was editor-in-chief of the IEEE Transactions on Information Theory during the period 2010-2013 and served on the editorial board of the IEEE Signal Processing Magazine, “Foundations and Trends in Communication and Information Theory”, and “Foundations and Trends in Networking”. He was TPC co-chair of the 2008 IEEE International Symposium on Information Theory and the 2016 IEEE Information Theory Workshop and served on the Board of Governors of the IEEE Information Theory Society. He has been a delegate for faculty appointments of the president of ETH Zurich since 2008.

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