2022 Distinguished Lecturers
The IEEE Information Theory Society (ITSoc) is also pleased to announce that Prof. Pulkit Grover, Prof. Hamed Hassani, Prof. Parastoo Sadeghi, and Prof. Michèle Wigger, have been named ITSoc Distinguished Lecturers for 2022–2023.
Mar 4, 2022

The IEEE Information Theory Society (ITSoc) is also pleased to announce that Prof. Pulkit Grover from Carnegie Mellon Univeristy, Prof. Hamed Hassani from the University of Pennsylvania, Prof. Parastoo Sadeghi from the University of New South Wales, and Prof. Michèle Wigger from Telecom-ParisTech, have been named ITSoc Distinguished Lecturers for 2022–2023. The IEEE Information Theory Society established the Distinguished Lecturers Program to promote interest in information theory by supporting its local chapters to invite prominent information theory researchers to give lectures at their events.

Pulkit Grover (Ph.D. UC Berkeley'10, B.Tech, M.Tech IITK'05) is the Angel Jordan Associate Professor at CMU. His main contributions are towards advancing and experimentally validating science of information for engineering (and reverse engineering) computing systems, energy-efficient communication, distributed control, neural inference, and neural engineering. His lab is focused on advancing theory, algorithms, and hardware for fair and explainable machines on one hand, and for understanding, diagnosing, and treating disorders such as epilepsy, stroke, and traumatic brain injuries on the other. On these problems, his lab works extensively with data scientists, system and device engineers, neuroscientists, and clinicians. A goal of his neuroengineering work is ensuring that advances in engineering are accessible to all, regardless of their hair-type or skin color. Some of his work is impacting practice, e.g., in improving neural recordings at Children's Hospital of Pittsburgh. Pulkit received the 2010 best student paper award at IEEE Conference on Decision and Control; the 2011 Eli Jury Dissertation Award (UC Berkeley); the 2012 Leonard G. Abraham award (IEEE ComSoc); a 2014 NSF CAREER award; a 2015 Google Research Award; the 2018 inaugural award from the Chuck Noll Foundation for Brain Injury Research; the 2018 Spira Excellence in Teaching Award (CMU), and the 2019 best tutorial paper award (IEEE ComSoc). He has given two tutorials at ISIT (2018 and 2021), and a talk at NASIT'15. His students have won several awards and fellowships, founded an impactful healthcare startup, and have positions in academia, industry, and research labs. From 2019–2023, he’s leading the SharpFocus center, a multi-institution effort aimed at high-resolution noninvasive brain sensing and stimulation.

Hamed Hassani is currently an assistant professor of Electrical and Systems Engineering department as well as the Computer and Information Systems department at the University of Pennsylvania. Prior to that, he was a research fellow at Simons Institute for the Theory of Computing (UC Berkeley) affiliated with the program of Foundations of Machine Learning, and a post-doctoral researcher in the Institute of Machine Learning at ETH Zurich. He received a Ph.D. degree in Computer and Communication Sciences from EPFL, Lausanne. He is the recipient of the 2014 IEEE Information Theory Society Thomas M. Cover Dissertation Award, 2015 IEEE International Symposium on Information Theory Student Paper Award, 2017 Simons-Berkeley Fellowship, 2018 NSF-CRII Research Initiative Award, 2020 Air Force Office of Scientific Research (AFOSR) Young Investigator Award, 2020 National Science Foundation (NSF) CAREER Award, and 2020 Intel Rising Star award.

Parastoo Sadeghi is a Full Professor with the School of Engineering and Information Technology, the University of New South Wales (UNSW), Canberra, Australia. She holds a bachelor’s and a master’s degree in electrical engineering from Sharif University of Technology, Tehran, Iran and a PhD degree in electrical engineering and telecommunications from UNSW, Sydney, Australia. Prior to joining UNSW in 2020, she was a faculty at the Australian National University. Currently, she is intensively pursuing research in data sharing with strong privacy against inference attacks. However, she also makes regular contributions to network coding, index coding, communications and signal processing theory. Over her career, she has enjoyed extended visits to MIT, Technical University of Munich and University of California at San Diego and have collaborated with many leading researchers from around the world. She has co-authored a book, titled Hilbert Space Methods in Signal Processing and close to 200 refereed journal articles or conference papers. In 2019, she received a Future Fellowship Award from the Australian Research Council for her research on data privacy. From 2016 to 2019, she served as an Associate Editor for the IEEE Transactions on Information Theory. From 2019 to 2020, she was a member on the Board of Governors of the IEEE Information Theory Society. She was the General Co-chair of IEEE ISIT 2021, which ran as a virtual event in July 2021. Between 2021 and 2022, Prof. Sadeghi is chairing the Schools Subcommittee of the IEEE Information Theory Society. In 2022, she will be serving as the Secretary of the IEEE Information Theory Society.

Michèle Wigger received the M.Sc. degree in electrical engineering, with distinction, and the Ph.D. degree in electrical engineering both from ETH Zurich in 2003 and 2008, respectively. In 2009, she was first a post-doctoral fellow at the University of California, San Diego, USA, and then joined Telecom Paris, France, where she is currently a full professor. Dr. Wigger has held visiting professor appointments at the Technion--Israel Institute of Technology and ETH Zurich. Dr. Wigger has served as an Associate Editor of the IEEE Communication Letters and for the IEEE Transactions on Information Theory. During 2016-2019 she also served on the Board of Governors of the IEEE Information Theory Society. Dr. Wigger's research interests are in multi-terminal information theory, in particular in distributed source coding and hypothesis testing, in capacities of networks with cooperation, feedback, or cache memories, and in capacities of wireless optical channels.