2025 Women in Information and Coding Theory Workshop (WICT)  

Elsa Dupraz received the Ph.D degree in applied physics from the University of Paris-Saclay in 2013. From January 2014 to September 2015 she held a post-doctoral position at ETIS Lab (ENSEA, France) and ECE department of the University of Arizona (United States). Since October 2015, she is an Associate Professor at IMT Atlantique (Brest, France).  Her research interests include channel coding, information theory, noisy in-memory computing, goal-oriented communications, and  DNA data storage. In 2024, she received the young researcher award from IMT and French Academy of Sciences for her research activities on channel codes. Currently, she serves as Editor for the IEEE Transactions on Communications, as TPC co-chair for Track 1 "Fundamentals and Phy" of PIMRC 2025, and as a member of the digital presence committee of IEEE ITSoc. 

Title: Learning Over Compressed Data: From Information Theory to Practical Code Design

Abstract: In goal-oriented communications, the focus has shifted from data reconstruction to directly performing learning tasks—such as clustering, classification, or pattern recognition—on received coded data. This shift raises new fundamental questions in both information theory and practical code design. From an information-theoretic perspective, new analytical tools are needed to characterize the optimal performance of learning over coded data, incorporating learning-specific criteria (e.g., classification accuracy, generalization error) instead of traditional distortion metrics. This tutorial first reviews recent novel analytical tools developed to address these challenges, providing insights into the essential components of coding schemes optimized for learning. Building on these foundations, we next present practical and efficient source coding schemes for several learning tasks, including hypothesis testing, regression, and classification. Finally, we explore new perspectives on the joint optimization of source/channel coding schemes and machine learning models.

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 the IEEE Journal on Selected Areas in Information Theory and the Chair of IEEE Communications Society Communication Theory Technical Committee.

Title: Distributed Compression in the Era of Machine Learning

Abstract: Many applications from camera arrays to sensor networks require efficient compression and processing of correlated data, which in general is collected in a distributed fashion. While information-theoretic foundations of distributed compression are well investigated, the impact of theory in practice has been somewhat limited. As the field of data compression is undergoing a transformation with the emergence of learning-based techniques, machine learning is becoming an important tool to reap the long-promised benefits of distributed compression. In this tutorial, we review the recent progress in the broad area of learned distributed compression, focusing on images as well as abstract sources. In particular, we discuss approaches that provide interpretable results operating close to information-theoretic bounds. We also discuss how learned distributed compression can impact communication in relay networks.

Si-Hyeon Lee received the B.S. (summa cum laude) and Ph.D. degrees in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea, in 2007 and 2013, respectively. She is currently an Associate Professor with the School of Electrical Engineering, KAIST. She was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada, from 2014 to 2016, and an Assistant Professor with the Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea, from 2017 to 2020. Her research interests include information theory, wireless communications, statistical inference, and machine learning. She is currently an IEEE Information Theory Society Distinguished Lecturer (2024-2025). 

Title: Privacy-Preserving Data Utilization with Differential Privacy

Abstract: In today's world, our diverse information is collected through various channels and utilized for a range of purposes, including statistical inference and the development of machine learning models. However, privacy threats continue to emerge, revealing that sensitive personal information can be inferred from statistics or machine learning models. In this tutorial, we introduce differential privacy, a representative privacy protection metric, and explore its applications in machine learning and statistical inference. Furthermore, focusing on distribution estimation, a fundamental problem in statistical inference, we will discuss how classical results from combinatorics can be leveraged to develop communication-efficient differential privacy techniques.

Parastoo Sadeghi received the bachelor’s and master’s degrees in electrical engineering from the Sharif University of Technology, Tehran, Iran, in 1995 and 1997, respectively, and the Ph.D. degree in electrical engineering from the University of New South Wales (UNSW) Sydney, in 2006. She is currently a Professor with the School of Engineering and Technology, UNSW Canberra. She has coauthored more than 200 refereed journal articles and conference papers. Her research interests include information theory, communications theory, data privacy, index coding, and network coding. From 2016 to 2019, she served as an Associate Editor for the IEEE Transactions on Information Theory. In 2022, she was selected as a Distinguished Lecturer of the IEEE Information Theory Society. She is currently serving on the Board of Governors of IEEE Information Theory Society.

Title: An introduction to the science and applications of quantitative information flow (QIF)

Abstract:  This tutorial aims to provide a gentle introduction to the science and applications of quantitative information flow  (QIF), which has been developed since 2009 to characterize and address information flow security in computing systems. QIF can explain precisely what (unintended) information leakage to an adversary is, how it can be assessed quantitatively, and how systems can be constructed that satisfy rigorous information-flow guarantees. 

The tutorial has three parts. It first motivates the study of quantitative information flow and gives an informal overview of some of its important concepts by discussing information leakage in simple contexts. In the second part, these concepts will be made rigorous. This includes modelling secrets and their distributions, formulating the vulnerability of a data system to adversaries who try to guess its secrets, quantification of information leakage from systems via channels, and finally formulating and understanding robustness of systems to various adversaries via developing operational notions of leakage capacity and refinement. In the last part of the tutorial, these concepts will be applied to describe and understand well-known information leakage measures such as differential privacy, local differential privacy, and alpha-based information theoretic privacy measures. It also presents a recent application of QIF leakage capacity to describe reconstruction attacks in machine learning applications.

Linda Senigagliesi (Member, IEEE) received the Ph.D. degree in information engineering from the Università Politecnica delle Marche, Ancona, Italy, in 2019. During her Ph.D., she was a Visiting Student with the Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. She is currently a Research Fellow with the École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA) at the ETIS Lab, France. Her main research interests include information theory and physical layer security, with applications to distributed storage systems and wireless communications. Her activity is focused on machine learning techniques for physical layer authentication and privacy. Dr. Senigagliesi is a member of the IEEE INGR Physical Layer Security Focus Group and Cost Action CA22168—Physical Layer Security for Trustworthy and Resilient 6G Systems (6G-PHYSEC). She has served on the Technical Program Committee of several international conferences.

Title: Fundamentals and Emerging Trends in Physical Layer Security and Authentication

Abstract: This tutorial offers a comprehensive introduction to physical layer security, focusing on essential metrics and foundational concepts needed to understand security from an information-theoretic perspective. The first part covers key principles of information theory, providing the basis for quantifying security at the physical layer. The second part explores recent advancements in physical layer authentication (PLA), discussing various approaches, including statistical methods and modern machine learning-based techniques, along with their respective advantages and limitations. Finally, we examine the potential of angle-of-arrival as a robust feature for enabling lightweight and efficient PLA in future 6G networks.