Peter Bartlett is Professor of the Graduate School in Computer Science and Statistics at UC Berkeley, Principal Scientist at Google DeepMind, Machine Learning Research Director at the Simons Institute for the Theory of Computing, Director of the Foundations of Data Science Institute, Director of the Collaboration on the Theoretical Foundations of Deep Learning, and Honorary Professor of Mathematical Sciences at the Australian National University. His research interests include machine learning and statistical learning theory, and he is the co-author of the book Neural Network Learning: Theoretical Foundations. He has been Institute of Mathematical Statistics Medallion Lecturer, winner of the Malcolm McIntosh Prize for Physical Scientist of the Year, and Australian Laureate Fellow, and he is a Fellow of the IMS, Fellow of the ACM, and Fellow of the Australian Academy of Science.

Matthieu Bloch received the Engineering Degree from Supélec, France, and the M.S. in Electrical and Computer Engineering from Georgia Tech in 2003. In 2006, Dr. Bloch received the Ph.D. degree in Engineering Science from the Université de Franche-Comté, France, and in 2008, he received the Ph.D. degree in Electrical and Computer Engineering from Georgia Tech. Dr. Bloch spent a year as a postdoctoral research associate at the University of Notre Dame and joined the faculty at Georgia Tech in July 2009. At the start of his career, he was based at the Georgia Tech Lorraine campus in Metz, France, and in 2013, he moved to the main Georgia Tech campus in Atlanta. In January 2021, Dr. Bloch was appointed as the associate chair for ECE Graduate Affairs. 

Emanuele Viterbo is a Professor in the Department of Electrical and Computer Systems Engineering at Monash University, Australia. He served as Head of Department and Associate Dean of Graduate Research in the Faculty of Engineering at Monash University. Prof. Viterbo obtained his degree and a Ph.D. in Electrical Engineering, both from Politecnico di Torino, Turin, Italy. From 1990 to 1992, he worked at the European Patent Office, The Hague, The Netherlands, as a patent examiner in the field of dynamic recording and error-control coding. In 1997–1998, he was a post-doctoral research fellow in the Information Sciences Research Center of AT&T Research, Florham Park, NJ, USA. Prof. Viterbo is a Fellow of the IEEE (2011), ISI Highly Cited Researcher (2009), Member of the Board of Governors of the IEEE Information Theory Society (2011–2016), Conference Committee Chair of the IEEE Information Theory Society (2016-2018). He served as an Associate Editor for IEEE Transactions on Information Theory, European Transactions on Telecommunications, and the Journal of Communications and Networks. He was the General Co-chair of the 2021 IEEE Int. Symposium on Information Theory and of the 2009 IEEE Information Theory Workshop, and TPC Chair of the 2014 IEEE Information Theory Workshop. His main research interests are in waveform design, polar coding, sphere decoding, lattice codes for Gaussian and fading channels, algebraic coding theory, algebraic space-time coding, digital terrestrial television broadcasting, NAND-flash memory storage, and digital magnetic recording.

Dr. Yuting Wei is currently an Associate Professor in the Statistics and Data Science Department at the Wharton School, University of Pennsylvania. Prior to that, Dr. Wei spent two years at Carnegie Mellon University as an assistant professor and one year at Stanford University as a Stein Fellow. She received her Ph.D. in statistics at the University of California, Berkeley. She was the recipient of the 2025 Gottfried E. Noether Early Career Scholar Award, Google Research Scholar Award, NSF Career Award, and the Erich L. Lehmann Citation from the Berkeley statistics department. Dr. Wei's research interests lie broadly in the span of statistics, mathematical optimization, information theory, and machine learning. She has been developing theoretical and algorithmic foundations for learning from high-dimensional and structured data, and understanding the sample efficiency in reinforcement learning and diffusion models. She is also interested in statistical problems arising from genetics and genomics. 

Mark M. Wilde received the Ph.D. degree in electrical engineering from the University of Southern California, Los Angeles, California. He is an Associate Professor of Electrical and Computer Engineering at Cornell University. He is an IEEE Fellow, he is a recipient of the National Science Foundation Career Development Award, he is a co-recipient of the 2018 AHP-Birkhauser Prize, awarded to “the most remarkable contribution” published in the journal Annales Henri Poincare, and he is an Outstanding Referee of the American Physical Society. His current research interests are in quantum Shannon theory, quantum computation, quantum optical communication, quantum computational complexity theory, and quantum error correction.

Jean Honorio is a machine learning researcher with a focus on algorithms with theoretical guarantees, for combinatorial and non-convex problems, specially those involving latent variables, such as in fairness and robustness. Prior to joining the University of Melbourne in 2024, Jean was an Assistant Professor in the Computer Science Department at Purdue University, as well as in the Statistics Department (by courtesy). Prior to joining Purdue, Jean was a postdoctoral associate at MIT, working with Tommi Jaakkola. His Erdös number is 3. His work has been partially funded by the National Science Foundation (NSF). He is an editorial board reviewer of JMLR, and have served as area chair of NeurIPS and ICML, senior PC member of AAAI and IJCAI, PC member of NeurIPS, ICML, AIStats among other conferences and journals.

Dr Wanchun Liu is a Senior Lecturer at the University of Sydney. Her research focuses on communications, networked control, and cyber-physical human systems for Industry 5.0. She is leading multiple Australian Research Council (ARC)-funded Discovery and Linkage projects and received several honours, including the ARC Discovery Early Career Researcher Award (DECRA) (2022), the Dean’s Award for Outstanding Research (2022), and recognition as one of N2Women’s global Rising Stars (2023).

James Saunderson is a Senior Lecturer in the Department of Electrical and Computer Systems Engineering at Monash University. He received a PhD in Electrical Engineering and Computer Science from MIT (2015), and undergraduate degrees in Mathematics and Electrical Engineering from the University of Melbourne (2008). With Hamza Fawzi and Pablo Parrilo, he was awarded the 2020 SIAM Activity Group on Optimization Best Paper Prize, and was the recipient of an Australian Research Council Discovery Early-Career Researcher Award (DECRA). His research interests lie in mathematical optimisation and its applications.