Information Theory and Data Science Workshop

(16 Jan 2023–27 Jan 2023)

Organizing Committee




Contact Information

General Enquiries: Ims-enquiry(AT)
Scientific Aspects Enquiries: scarlett(AT)


Information theory addresses fundamental questions in various areas of science and engineering, including communications, data compression, statistical learning theory, security, and networks. In particular, information theory can be used to identify fundamental limits and gauge the effectiveness of algorithms for various problems associated with these fields.

Recent years have witnessed a renaissance in the use of information-theoretic methods to address problems in the general field of information processing beyond communications, including signal acquisition, signal analysis and processing, high-dimensional estimation, dictionary learning, (un)supervised learning, reinforcement learning, convex optimization, and graph mining. With increasing traction in both academia and industry for new approaches to data science, and impressive new breakthroughs in algorithmic design for statistical machine learning (e.g., deep learning), information theory possesses great potential to further illuminate the underlying theory and algorithms.

This workshop aims to bring together academics in diverse domains in information theory, machine learning, signal processing, statistics, and other related areas, to share expertise along the following inter-related research themes:

  • Information-theoretic methods for quantifying the information content of data sets and signals, and accordingly establishing fundamental performance limits and a better understanding of practical algorithms.
  • Information-theoretic methods for designing, interpreting, and understanding deep neural networks and related machine learning techniques.

The workshop will allow researchers specializing in different topics to exchange ideas and develop a broader understanding of the benefits offered by an information-theoretic perspective, and build towards a deeper understanding of the relevant opportunities and challenges.


The IMS will be closed on the 23 and 24 January 2023 for the Chinese New Year holidays. 

Invited Speakers (this is a tentative list)

  • Jayadev Acharya (Cornell University, USA)
  • Arnab Bhattacharyya (National University of Singapore, Singapore)
  • Clément Canonne (University of Sydney, Australia)
  • Wei-Ning Chen (Stanford University, USA)
  • Aditya Gopalan (Indian Institute of Science, India)
  • Reinhard Heckel (Technical University of Munich, Germany)
  • Prashanth L.A. (Indian Institute of Technology Madras, India)
  • Kangwook Lee (University of Wisconsin-Madison, USA)
  • Marco Mondelli (Institute of Science and Technology Austria, Austria)
  • Mehul Motani (National University of Singapore, Singapore)
  • Frederique Oggier (Nanyang Technological University, Singapore)
  • Galen Reeves (Duke University, USA)
  • Miguel Rodrigues (University College London, UK)
  • Cynthia Rush (Columbia University, USA)
  • Cong Shen (The University of Virginia, USA)
  • Ali Tajer (Rensselaer Polytechnic Institute, USA)
  • Himanshu Tyagi (Indian Institute of Science, India)
  • Antonios Varvitsiotis (Singapore University of Technology and Design, Singapore)
  • Yao Xie (Georgia Institute of Technology, USA)
  • Li Yi (Nanyang Technological University, Singapore)


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