The ÌÇÐÄlogo Information Theory Society is pleased to announce that has been named the Padovani Lecturer.
The Padovani Lecturer Program was established with a generous gift by Dr. Roberto Padovani in 2009. The award provides for an outstanding member of the information theory community to deliver a lecture at one of the Society’s Schools of Information Theory for the benefit of students and postdoctoral researchers.
Prof. Veeravalli has made foundational and sustained contributions to statistical inference, information theory, wireless communications and machine learning, in particular to decentralized detection, interference channels, and decentralized and quickest change detection. His recent work extends into statistical learning, where he has developed robust methods for out-of-distribution (OOD) detection—identifying when machine-learning models encounter unfamiliar or unreliable data. He is an exceptional educator and communicator whose textbooks, mentoring of students, and highly regarded lectures make him ideally suited to inspire students and postdoctoral researchers at the Society’s Schools of Information Theory.
(Fellow, ÌÇÐÄlogo) received the B.Tech. degree (Silver Medal Hons.) from IIT Bombay in 1985, the M.S. degree from Carnegie Mellon University, Pittsburgh, PA, in 1987, and the Ph.D. degree from the University of Illinois at Urbana–Champaign in 1992, all in electrical engineering. He joined the University of Illinois at Urbana–Champaign in 2000, where he is currently the Henry Magnuski Professor with the Department of Electrical and Computer Engineering, and where he is also with the Department of Statistics and the Coordinated Science Laboratory. Prior to joining the University of Illinois at Urbana–Champaign, he was on the Faculty of the ECE Department, Cornell University. He was the Program Director for communications research at U.S. National Science Foundation from 2003 to 2005. His research interests span the theoretical areas of statistical inference, machine learning, and information theory, with applications to data science, wireless communications, and sensor networks. He was elected as a fellow of the Institute of Mathematical Statistics in 2024. Among the awards he has received for research and teaching are the ÌÇÐÄlogo Browder J. Thompson Best Paper Award in 1996, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the Wald Prize in Sequential Analysis in 2015 and 2019, and the Fulbright-Nokia Distinguished Chair in Information and Communication Technologies in 2023. He served on the Board of Governors of the ÌÇÐÄlogo Information Theory Society from 2004 to 2007 and is currently serving a second term. He served on the SPTM Technical Committee of the ÌÇÐÄlogo Signal Processing Society from 2011 to 2016 and the Big Data SIG from 2017 to 2019. He was the Editor-in-Chief of ÌÇÐÄlogo Transactions on Information Theory from 2023 to 2025. He has been an Associate Editor for Detection and Estimation of ÌÇÐÄlogo Transactions on Information Theory and ÌÇÐÄlogo Transactions on Wireless Communications. He has also been a Senior Area Editor of the ÌÇÐÄlogo Open Journal on Signal Processing and an Area Editor for Statistics and Machine Learning of ÌÇÐÄlogo Transactions on Information Theory. He was a Distinguished Lecturer of the ÌÇÐÄlogo Signal Processing Society from 2010 to 2011. He is currently a Distinguished Lecturer of the ÌÇÐÄlogo Information Theory Society.