Research
Sensors convert physical information into actionable data, used for modeling, estimation, and prediction—in short, they provide us with decision-driving insights. Our team especially studies the mathematical foundations of different kinds of sensing systems:
- fundamental limits of sensing systems
- distributed estimation and detection over networks
- security in distributed sensing
- optimization of sensor networks
- estimation and detection in the context of machine learning
- integrated sensing and communication for future cellular networks and optical fibers
- quantum sensing systems
Team members
- Michèle Wigger, Professor, team leader
- Robert Graczyk, Associate Professor
- Mireille Sarkiss, Associate Professor Télécom SudParis
Keywords
- Information theory
- Statistics
- Estimation
- Detection
- Communications
Team publications
- [1] M. Ahmadipour, M. Kobayashi, M. Wigger, and G. Caire, « An Information-Theoretic Approach to Joint Sensing and Communication, » IEEE Trans. on Inf. Theory, Feb. 2024.
- [2] M. Hamad, M. Wigger, and M. Sarkiss, « Multi-hop Network with Multiple Decision Centers under Expected-Rate Constraints, » IEEE Trans. on Inf. Theory, July 2023.
- [3] M. Ahmadipour and M. Wigger, « An Information-Theoretic Approach to Collaborative Integrated Sensing and Communication for Two-Transmitter Systems, » Journal on Sel. Areas in Inf. Theory, June 2023.
- [4] R. Graczyk, A. Lapidoth, and M. Wigger, « Conditional and Relevant Common Information,” Information and Inference: A Journal of the IMA, 2022.
- [5] N. Le Gouic, R. Graczyk, and S. Moser, « Approximate Hypothesis Testing, » to be submitted to Int. Symp. Inf. Theory, July 2025.
- [6] A. Bounhar, M. Sarkiss, M. Wigger, “Covert Distributed Detection over Discrete Memoryless Channels”, Int. Symp. Inf. Theory 2024, 7-12 July 2024.
LTCI latest news
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