Dates: March 13-14, 2025
Venue: Ada Lovelace Seminar Room, B-Huset, Entrance 25, Valla Campus, Linköping
Linköping University is organizing a workshop on information and coding theory.
Invited international speakers will provide in-depth tutorials on relevant topics, including a general introduction to the “Information Theory” research area, which was established by Claude E. Shannnon in 1948. We invite all interested parties (e.g., Master’s students, Ph.D. students, postdocs, industrial researchers, faculty members, etc.) to attend this 2-day long workshop to become familiar with and gain in-depth knowledge of the cutting-edge applications of information and coding theory from top female researchers.
Note: Attendance certificates will be provided, which, e.g., Ph.D. students can use to argue for up to 3 credits for their PhD studies, which should make sense given the depth and breadth of the topics that will be discussed.
This technical workshop is open to anyone interested in information and coding theory!
This workshop is supported by the IEEE Information Theory Society through its Distinguished Lecturer Program and by the IEEE Sweden VT/COM/IT Joint Chapter.
Intended Outcomes of the Workshop
- Introducing Information Theory and Coding Theory to researchers from relevant discplines with a basic background in probability theory and/or security/privacy
- Introducing the state-of-the art information-theoretic methods applied to cutting-edge applications
- Supporting the Gender & Diversity aspects within these research areas within Sweden and internationally
- Illustrating the elegance of Shannon theory
- Supporting the strong female researchers working on these areas
- Increasing visibility of information and coding theoretic research and their impact on the future of communication systems, distributed computation systems, cryptography, machine learning, biology, etc.
Organizers of the Workshop
- Onur Günlü, Linköping University
- Michael Lentmaier, Lund University
- Thomas Johansson, Lund University
- Mikael Skoglund, KTH
- Alexandre Graell i Amat, Chalmers University
WICT Speakers
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).
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.
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.
Registration Deadline: March 1, 2025 (In case of too many people FCFS applies)
Registration Link (required to be able to attend the event)*
*Your data will be stored according to LiU’s privacy guidelines, following EU GDPR.
Suggestions for Travel and Accommodation: Please find some informal suggestions here
For questions: Onur Günlü via onur.gunlu@liu.se
