
Information Theory Society
The IEEE Information Theory Society is the premier professional society dedicated to the advancement of the mathematical underpinnings of information technology for the benefit of humanity. Information theory encompasses the processing, transmission, storage, and use of information, and the foundations of the communication process.
Information Theory Awardees
Alon Orlitsky
2021 Shannon Award WinnerRobert M. Gray
Aaron D. Wyner Award WinnerUpcoming Events
Melbourne, Australia
2021 IEEE International Symposium on Information Theory (ISIT)
2021 IEEE Information Theory Workshop (ITW), Kanazawa
News
Yuejie Chi named the IEEE Information Theory Society 2021 Goldsmith Lecturer
Yuejie Chi announced as the 2021 Goldsmith Lecturer
Muriel Médard named the IEEE Information Theory Society 2021 Padovani Lecturer
Muriel Médard announced as the 2021 Padovani Lecturer
The IEEE Information Theory Society names 2021-22 Distinguished Lecturers
2021-22 Distinguished Lecturers announced
Registrations for ITW 2020
Registrations for ITW 2020 are now open.
Workshop dates: April 11-15, 2021.
Detailed…
Conferences
BOG Meeting - June 2021
The second BOG meeting of 2021 will be held virtually on zoom. BOG members and approved presenters…
Sixth London Symposium on Information Theory (LSIT)
The symposium continues the tradition of the historical first four editions of LSIT, which were…
Online conference "Youth in High Dimensions", 15-18 June
We are excited to announce the second edition of our conference “Youth in High Dimensions” that…
Jobs Board
Postdoctoral Position in Data Driven Compression
We are soliciting a post-doctoral position in the area of source coding using machine learning…
Postdoctoral Research Fellow in Science of Information
The Center for Science of Information (CSoI) seeks a postdoctoral researcher to work on broad…
Post-Doctoral Researcher in Distributed Computing
Post-Doctoral Researcher in Distributed Computing at The Hong Kong University of Science and…
Call to Action
ISIT 2021 Call for Recent Results
Submit to IEEE Journal on Selected Areas in Information Theory
The IEEE Journal on Selected Areas in Information Theory (JSAIT) covers various aspects of information theory and its applications, and accepts submissions in response to specific calls for papers.
Recent Journal Issues
JSAIT is a multi-disciplinary journal of special issues.
The IEEE Transactions on Information Theory publishes papers concerned with the transmission, processing, and utilization of information.
Videos on Information Theory
Research In Information Theory
Quantum Blahut-Arimoto Algorithms
We generalize alternating optimization algorithms of Blahut-Arimoto type to the quantum setting. In particular, we give iterative algorithms to compute the mutual information of quantum channels, the thermodynamic capacity of quantum channels, the coherent information of less noisy quantum channels, and the Holevo quantity of classical-quantum channels. Our convergence analysis is based on quantum entropy inequalities and leads to a priori additive eps-approximations after O(eps^(-1)*log N) iter...
A Universal Low Complexity Compression Algorithm for Sparse Marked Graphs
Many modern applications involve accessing and processing graphical data, i.e. data that is naturally indexed by graphs. Examples come from internet graphs, social networks, genomics and proteomics, and other sources. The typically large size of such data motivates seeking efficient ways for its compression and decompression. The current compression methods are usually tailored to specific models, or do not provide theoretical guarantees. In this paper, we introduce a low-complexity lossless com...
Exact Recovery in the Stochastic Block Model
The stochastic block model with two communities, or equivalently the planted bisection model, is a popular model of random graph exhibiting a cluster behavior. In the symmetric case, the graph has two equally sized clusters and vertices connect with probability p within clusters and q across clusters. In the past two decades, a large body of literature in statistics and computer science has focused on providing lower bounds on the scaling of | p - q| to ensure exact recovery. In this paper, we i...
Asymptotics of MAP Inference in Deep Networks
Deep generative priors are a powerful tool for reconstruction problems with complex data such as images and text. Inverse problems using such models require solving an inference problem of estimating the input and hidden units of the multi-layer network from its output. Maximum a priori (MAP) estimation is a widely-used inference method as it is straightforward to implement, and has been successful in practice. However, rigorous analysis of MAP inference in multi-layer networks is difficult. Thi...
Channel Coding Techniques for Network Communication
Next-generation wireless networks aim to enable order-of-magnitude increases in connectivity, capacity, and speed. Such a goal can be achieved in part by utilizing larger frequency bandwidth or by deploying denser base stations. As the number of wireless devices is exploding, however, it is inevitable that multiple devices communicate over the same time and same spectrum. Consequently, improving the spectral efficiency in wireless networks with multiple senders and receivers becomes the key chal...