Speakers

Erdal Arikan received the B.S. degree in electrical engineering from the California Institute of Technology, Pasadena, CA, USA, in 1981, and the M.S. and Ph.D. degrees in electrical engineering from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 1982 and 1985, respectively. Since 1987, he has been with the Electrical-Electronics Engineering Department, Bilkent University, Ankara, Turkey, where he is a Professor. He is also the Founder of Polaran Ltd, a company specializing in polar coding products. He was a recipient of the 2010 IEEE Information Theory Society Paper Award, the 2013 IEEE W.R.G. Baker Award, the 2018 IEEE Hamming Medal, and the 2019 Claude E. Shannon Award.

Salman Avestimehr (https://www.avestimehr.com) is the Dean’s Professor and the inaugural director of the USC-Amazon Center on Trustworthy AI at the ECE and CS Department of the University of Southern California, He is also the CEO and co-founder of FedML (https://fedml.ai). His research interests include decentralized and federated machine learning, information theory, security, and privacy. Dr. Avestimehr has received many awards for his research, including the Presidential PECASE award from the White House (President Obama), the James L. Massey Research & Teaching Award from IEEE Information Theory Society, an Information Theory Society and Communication Society Joint Paper Award, and several Best Paper Awards at Conferences. He has been an Amazon Scholar in Alexa-AI, and is a fellow of the IEEE.

Ayfer Özgür an associate professor in the Electrical Engineering Department of Stanford University and a member of ISL. Before joining Stanford, she was a postdoctoral researcher with the Algorithmic Research on Networked Information Group at EPFL, Switzerland. She received her Ph.D. degree in October 2009 from the Information Processing Group at the same university. She received her B.Sc. degrees in electrical engineering and physics from Middle East Technical University, Turkey, in 2001 and her M.Sc. degree in electrical engineering from the same university in 2004. From 2001 to 2004, she worked as a hardware design engineer for the Defense Industries Research and Development Institute in Turkey.

Cynthia Rush is the Howard Levene Assistant Professor of Statistics in the Department of Statistics at Columbia University. She received a Ph.D. and M.A. in Statistics from Yale University in 2016 and 2011, respectively, and she completed her undergraduate coursework at the University of North Carolina at Chapel Hill where she obtained a B.S. in Mathematics in 2010. She received a NSF CRIII award in 2019, was a finalist for the 2016 IEEE Jack K. Wolf ISIT Student Paper Award, was an NTT Research Fellow at the Simons Institute for the Theory of Computing for the program on Probability, Computation, and Geometry in High Dimensions in Fall 2020, and was a Google Research Fellow at the Simons Institute for the Theory of Computing for the program on Computational Complexity of Statistical Inference in Fall 2021.

Lucas Theis is a senior research scientist at Google working on compression using neural networks. He previously worked at Twitter, and before that at Magic Pony Technology, a London based startup working on video compression. He received a PhD from the Max Planck Research School for Neural Information Processing in Tübingen, working in the lab of Matthias Bethge, and a BSc in Cognitive Science from the University of Osnabrück, Germany. His research interests include probabilistic machine learning and applications of information theory.

Yury Polyanskiy is a Professor of Electrical Engineering and Computer Science and a member of LIDS, IDSS, and Center of Statistics. Yury received M.S. degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Moscow, Russia in 2005 and Ph.D. degree in electrical engineering from Princeton University, Princeton, NJ in 2010. His research interests span information theory, statistical machine learning, error-correcting codes, wireless communication and fault tolerance. Dr. Polyanskiy won the 2020 IEEE Information Theory Society James Massey Award, 2013 NSF CAREER award and 2011 IEEE Information Theory Society Paper Award.

Martina Cardone received the Ph.D. degree in electronics and communications from Télécom ParisTech (with work done at Eurecom in Sophia Antipolis, France) in 2015. She is currently a McKnight Land-Grant Assistant Professor with the Electrical and Computer Engineering Department, University of Minnesota (UMN). From November 2017 to January 2018, she was a Postdoctoral Associate with the Electrical and Computer Engineering Department, UMN. From July 2015 to August 2017, she was a Postdoctoral Research Fellow with the Electrical and Computer Engineering Department, UCLA Henry Samueli School. Her main research interests are in estimation theory, network information theory, network coding, and wireless networks with a special focus on their capacity, security, and privacy aspects. She is a recipient of the 2022 McKnight Land-Grant Professorship, the NSF CAREER Award in 2021, the NSF CRII Award in 2019, the Second Prize in the Outstanding Ph.D. Award, Télécom ParisTech, Paris, France, and the Qualcomm Innovation Fellowship in 2014.

Ziv Goldfeld is an assistant professor in the School of Electrical and Computer Engineering, and a graduate field member in Computer Science, Statistics, Data Science, and the Center of Applied Mathematics, at Cornell University. Before joining Cornell, he was a postdoctoral research fellow in LIDS at MIT. Ziv graduated with a B.Sc., M.Sc., and Ph.D. (all summa cum laude) in Electrical and Computer Engineering from Ben Gurion University, Israel. Ziv’s research interests include optimal transport theory, statistical learning theory, information theory, and mathematical statistics. Honors include the NSF CAREER Award, the IBM University Award, and the Rothschild Postdoctoral Fellowship.

Hessam Mahdavifar is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan. He received the B.Sc. degree from Sharif University of Technology in 2007, and the M.Sc. and the Ph.D. degrees from the University of California San Diego (UCSD) in 2009 and 2012, respectively, all in Electrical Engineering. He was with the Samsung Mobile Solutions Lab between 2012 and 2016. He has won several awards including the Qualcomm Innovation Fellowship in 2021 (as advisor), the NSF CAREER award in 2020, the best interactive paper award in the 2018 IEEE Globecom Workshop, the best paper award in the 2015 IEEE International Conference on RFID, the UCSD Shannon Memorial Fellowship, and two Silver Medals at the International Mathematical Olympiad.

Christina Lee Yu is an Assistant Professor at Cornell University in the School of Operations Research and Information Engineering. Prior to Cornell, she was a postdoc at Microsoft Research New England. She received her PhD in 2017 and MS in 2013 in EECS from MIT, and her BS in CS from Caltech in 2011. She received honorable mention for the 2018 INFORMS Dantzig Dissertation Award. She is a recipient of the 2021 Intel Rising Stars Award and a JPMorgan Faculty Research Award. Her research interests include algorithm design and analysis, high dimensional statistics, inference over networks, sequential decision making under uncertainty, online learning, and network causal inference. Her research is supported by NSF and AFOSR awards.

Mohammad Vahid Jamali is a Senior Research Engineer at the Samsung R&D Lab in San Diego. He received his Ph.D. in Electrical and Computer Engineering from the University of Michigan, in 2022. He is the recipient of the Best Paper Award of the IEEE International Conference on Communications (ICC) in 2022, the Qualcomm Innovation Fellowship in 2021, the Best Paper Award of the IEEE GLOBECOM Workshops in 2018, and the Best M.Sc. Thesis Award in Electrical Engineering by the IEEE Iran Section in 2017.