Viveck R. Cadambe is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Tech. He received his Ph.D. From the University of California Irvine in 2011, and was a postdoctoral researcher jointly at the Massachusetts Institute of Technology (MIT)and Boston University between 2011 and 2014. He was an Associate Professor in the Department of Electrical Engineering at Pennsylvania State University until 2024. His core expertise is in information theory, coding theory, communication theory and distributed algorithms. In his research, he studies applications to distributed machine learning, cloud computing, and wireless communications. Dr. Cadambe has received the 2009 Information Theory Society Best Paper Award for his paper that developed "interference alignment" - an interference management technique for wireless communication systems. He has received the 2014 IEEE Network Computing and Applications Best Paper Award, an NSF CRII Award in 2015, an NSF Career Award in 2016, a Google Faculty Award in 2019 and he was a finalist for the 2016 Bell Labs Prize. He has served as an Associate Editor for IEEE Transactions on Wireless Communications, an issue of the IEEE Journal on Special Areas in Information Theory, and the IEEE Transactions on Communications.

Sanghamitra Dutta is an assistant professor in the Department of Electrical and Computer Engineering at the University of Maryland College Park (UMD) since Fall 2022. She is affiliated with the Center for Machine Learning (CML) at UMIACS, the Department of Computer Science, the Values-Centered AI Initiative (VCAI), the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM), and the Applied Mathematics & Statistics, and Scientific Computation (AMSC). Before joining UMD, she was a senior research associate at JPMorgan Chase AI Research New York in the Explainable AI Centre of Excellence (XAI CoE). She received her Ph.D. and Master's from Carnegie Mellon University and B. Tech. from IIT Kharagpur, all in Electrical and Computer Engineering. Her research interests broadly revolve around reliable, efficient, and trustworthy machine learning. She is particularly interested in addressing the challenges concerning explainability, privacy, fairness, and reliability, by bringing in novel foundational perspectives from information theory, statistics, optimization, and causality. Her research has been published at several machine learning conferences such as NeurIPS, ICML, ICLR, etc. along with leading information-theory venues, featured in New Scientist and Montreal AI Ethics Brief, and adopted at JPMorgan. Her earlier works on coded computing have received widespread attention from across communities. She is a recipient of the 2024 NSF CAREER Award, 2024 George Corcoran Memorial Award, 2023 Northrop Grumman Seed Grant, 2023 JPMorgan Faculty Award, 2022 Simons Fellowship for Causality, 2021 AG Milnes Outstanding Thesis Award from CMU, and 2019 K&L Gates Presidential Fellowship in Ethics and Computational Technologies. She has pursued summer research internships at IBM Research and Dataminr. She featured in the 2025 List of 100 Brilliant Women in AI Ethics. 

Elza Erkip is an Institute Professor in the Electrical and Computer Engineering Department at New York University Tandon School of Engineering. She received the B.S. degree in Electrical and Electronics Engineering from Middle East Technical University, Ankara, Turkey, and the M.S. and Ph.D. degrees in Electrical Engineering from Stanford University, Stanford, CA, USA. Her research interests are in information theory, communication theory, and wireless communications. Dr. Erkip is a member of the Science Academy of Turkey and is a Fellow of the IEEE. She received the NSF CAREER award in 2001, the IEEE Communications Society WICE Outstanding Achievement Award in 2016, the IEEE Communications Society Communication Theory Technical Committee (CTTC) Technical Achievement Award in 2018, and the IEEE Communications Society Edwin Howard Armstrong Achievement Award in 2021. She was the Padovani Lecturer of the IEEE Information Theory Society in 2022. Her paper awards include the IEEE Communications Society Stephen O. Rice Paper Prize in 2004, the IEEE Communications Society Award for Advances in Communication in 2013 and the IEEE Communications Society Best Tutorial Paper Award in 2019. She was a member of the Board of Governors of the IEEE Information Theory Society 2012-2020, where she was the President in 2018.  She was a Distinguished Lecturer of the IEEE Information Theory Society from 2013 to 2014. She is currently the Editor-in-Chief of IEEE Journal on Selected Areas in Information Theory and the Chair of IEEE Communications Society Communication Theory Technical Committee.

Gauri Joshi is an associate professor in the ECE department at Carnegie Mellon University. Gauri completed her Ph.D. from MIT EECS, and received her B.Tech and M.Tech from the Indian Institute of Technology (IIT) Bombay. Her awards include the MIT Technology Review 35 under 35 Award, ONR Young Investigator and NSF CAREER Award, Best Paper awards at MobiHoc 2022 and SIGMETRICS 2020, and the Institute Gold Medal of IIT Bombay (2010).

Nihar B. Shah is an Associate Professor in the Machine Learning and Computer Science departments at Carnegie Mellon University (CMU). His research broadly lies in the fields of statistics, machine learning, information theory, and game theory, with a focus on applications to learning from people. The recent focus of his research is on addressing problems in peer review and other distributed human evaluations, where his group develops and analyzes computational tools with strong theoretical guarantees. His work has been used for the review of well over 100,000 papers and thousands of proposals. He is a recipient of the Young Alumnus Medal from the Indian Institute of Science, a JP Morgan faculty research award, Google Research Scholar Award, an NSF CAREER Award 2020-25, the 2017 David J. Sakrison memorial prize from EECS Berkeley for a "truly outstanding and innovative PhD thesis", the Microsoft Research PhD Fellowship 2014-16, the Berkeley Fellowship 2011-13, and several Best Paper Awards.

Ilan Shomorony is an assistant professor of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign (UIUC), where he is a member of the Coordinated Science Laboratory. He obtained his Ph.D. in Electrical and Computer Engineering from Cornell University in 2014 and was a postdoctoral scholar at UC Berkeley through the NSF Center for Science of Information (CSoI) until 2017. Before joining UIUC, he spent a year working as a researcher and data scientist at Human Longevity Inc., a personal genomics company. He received the NSF CAREER Award in 2021. His research interests include information theory and computational biology.

Mahdi Soltanolkotabi is the director of the center on AI Foundations for the Sciences (AIF4S) at the University of Southern California. He is also a professor in the Departments of Electrical and Computer Engineering, Computer Science, and Industrial and Systems engineering. Prior to joining USC, he completed his PhD in electrical engineering at Stanford in 2014. He was a postdoctoral researcher in the EECS department at UC Berkeley during the 2014-2015 academic year. Mahdi is the recipient of the Information Theory Society Best Paper Award, Packard Fellowship in Science and Engineering, an NIH Director’s new innovator award, a Sloan Research Fellowship, an NSF Career award, an Airforce Office of Research Young Investigator award (AFOSR-YIP), and faculty awards from Google and Amazon. His research focuses on developing the mathematical foundations of modern data science via characterizing the behavior and pitfalls of contemporary nonconvex learning and optimization algorithms with applications in AI, deep learning, large scale distributed training, federated learning, computational imaging, and AI for scientific and medical applications. Most recently his applied research focuses on developing and deploying reliable and trustworthy AI in healthcare.

Ali Tajer received a B.Sc. and an M.Sc. degree in Electrical Engineering from Sharif University of Technology, an M.A. in Statistics, and a Ph.D. in Electrical Engineering from Columbia University. During 2010-2012, he was a Postdoctoral Research Associate at Princeton University. He is currently a Professor of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute. His research interests include mathematical statistics, machine learning, and information theory. He is currently an Associate Editor for the IEEE Transactions on Information Theory and a Senior Area Editor for the IEEE Transactions on Signal Processing. In the past, he has served as an Associate Editor for the IEEE Transactions on Signal Processing, an Editor for the IEEE Transactions on Communications, and a Guest Editor for the IEEE Signal Processing Magazine. He received the Jury Award (Columbia University), School of Engineering Research Excellence Award for Junior Faculty (Rensselaer), School of Engineering Classroom Excellence Award (Rensselaer), James M. Tien '66 Early Career Award for Faculty (Rensselaer), School of Engineering Classroom Excellence Award for Senior Faculty (Rensselaer), a CAREER award from the U.S. National Science and a U.S. Air Force Fellowship Award. He is a member of the 2025-2026 class of Distinguished Lecturers of the IEEE Information Theory Society.

Emre Telatar received the B.Sc. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1986 and the S.M. and Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, in 1988 and 1992, respectively.

From 1992 to 1999, he was with the Mathematical Sciences Research Center, AT&T (later, Lucent Technologies) Bell Laboratories, Murray Hill, NJ.  Since 1999, he has been a professor at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.  His research interests are in communication and information theories.

Ramya Korlakai Vinayak is the Dugald C. Jackson assistant professor in the Dept. of Electrical and Computer Engineering and affiliated faculty in the Dept. of Computer Science and the Dept. of Statistics at the UW-Madison. She is also a faculty affiliate at the Data Science Institute and Discovery Fellow at the Wisconsin Institute of Discovery at UW-Madison. Her research interests span the areas of foundations of machine learning, statistical inference, and crowdsourcing, with a focus on preference learning and alignment under heterogeneity, reliable and efficient dataset creation, and human-in-the-loop systems. Her works address theoretical and practical challenges that arise when learning from heterogeneous societal data. Prior to joining UW-Madison, Ramya was a postdoctoral researcher in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. She received her Ph.D. in Electrical Engineering from Caltech. She obtained her Masters from Caltech and Bachelors from IIT Madras. She is a recipient of the Schlumberger Foundation Faculty of the Future fellowship from 2013-15, and an invited participant at the Rising Stars in EECS workshop in 2019. She is the recipient of NSF CAREER Award in 2023.

Qian Yu is an assistant professor of Electrical and Computer Engineering at the University of California, Santa Barbara. Previously, he completed a postdoctoral appointment at the Department of Electrical and Computer Engineering at Princeton University. Qian received his Ph.D. from the Department of Electrical and Computer Engineering at University of Southern California (USC). He received an M.Eng. degree in Electrical Engineering and a B.S. degree in Physics and EECS, both from Massachusetts Institute of Technology (MIT). His interests span information theory, learning theory, distributed computing, and many other problems math-related. Qian is the recipient of the Thomas M. Cover Dissertation Award in 2022, and the Jack Keil Wolf ISIT Student Paper Award in 2017.