Venkat Anantharam

About

Venkat Anantharam is a professor affiliated with the Electrical Engineering and Computer Sciences department at the University of California, Berkeley. His professional contact information includes an office located at 271 Cory Hall and a phone number (510) 643-8435. His email address is ananth@eecs.berkeley.edu. While his research interests are currently under construction on his webpage, his extensive publication record and supervision of numerous doctoral and master's students indicate a deep engagement with topics in information theory, stochastic processes, game theory, and network information theory. He has supervised a significant number of doctoral and master's students and hosted several postdoctoral fellows, reflecting his active role in mentoring emerging researchers. His work includes contributions to the understanding of Nash equilibria, entropy in stochastic processes, compression of graphical data, and the geometry of information-theoretic problems. He has published in prestigious journals such as IEEE Transactions on Information Theory and has presented at major conferences, demonstrating a strong presence in the academic community. His research spans theoretical and applied aspects of information theory, probability, and optimization, with a focus on problems involving communication channels, data compression, and game-theoretic models.

Research topics

• Computer Science
• Mathematics
• Statistics
• Pure mathematics
• Theoretical computer science
• Algorithm
• Discrete mathematics
• Applied mathematics

Selected publications

• A Unified Framework for One-Shot Achievability via the Poisson Matching Lemma

  IEEE Transactions on Information Theory · 2021 · 28 citations

  Senior authorCorresponding

  We introduce a fundamental lemma called the Poisson matching lemma, and apply it to prove one-shot achievability results for various settings, namely channels with state information at the encoder, lossy source coding with side information at the decoder, joint source-channel coding, broadcast channels, distributed lossy source coding, multiple access channels and channel resolvability. Our one-shot bounds improve upon the best known one-shot bounds in most of the aforementioned settings (except…

  DOI

• Unifying the Brascamp-Lieb Inequality and the Entropy Power Inequality

  IEEE Transactions on Information Theory · 2022 · 20 citations

  1st authorCorresponding

  The entropy power inequality (EPI) and the Brascamp-Lieb inequality (BLI) are fundamental inequalities concerning the differential entropies of linear transformations of random vectors. The EPI provides lower bounds for the differential entropy of linear transformations of random vectors with independent components. The BLI, on the other hand, provides upper bounds on the differential entropy of a random vector in terms of the differential entropies of some of its linear transformations. In this…

  PublisherDOI

• Reversible Markov decision processes and the Gaussian free field

  Systems & Control Letters · 2022-09-30 · 3 citations

  article1st authorCorresponding
  PublisherDOI

• A Universal Lossless Compression Method applicable to Sparse Graphs and heavy-tailed Sparse Graphs

  2021-07-12 · 3 citations

  articleSenior author

  Graphical data arises naturally in several modern applications, including but not limited to internet graphs, social networks, genomics and proteomics. The typically large size of graphical data argues for the importance of designing universal compression methods for such data. In most applications, the graphical data is sparse, meaning that the number of edges in the graph scales more slowly than <tex>$n^{2}$</tex>, where <tex>$n$</tex> denotes the number of vertices. Although in some applicati…

  PublisherDOI

• Sequential Channel Synthesis

  2022 IEEE International Symposium on Information Theory (ISIT) · 2022-06-26 · 2 citations

  articleSenior author

  The channel synthesis problem has been widely investigated over the last decade. In this paper, we consider the sequential version in which the encoder and the decoder work in a sequential way. Under a mild assumption on the target joint distribution we provide a complete (single-letter) characterization of the solution for the point-to-point case, which shows that the canonical symbol-by-symbol mapping is not optimal in general, but is indeed optimal if we make some additional assumptions on th…

  PublisherDOI

Recent grants

• Presidential Young Investigator Award (Computer Research)

  NSF · $291k · 1988–1995

• New Techniques for the Control of Multi-Agent Systems in Uncertain Environments

  NSF · $305k · 2005–2009

• CIF: Small: Poisson matching: A new tool for information theory

  NSF · $500k · 2020–2026

Frequent coauthors

• Amin Gohari

  Chinese University of Hong Kong

  29 shared

• Varun Jog

  University of Cambridge

  18 shared

• Payam Delgosha

  University of Illinois Urbana-Champaign

  18 shared

• Takis Konstantopoulos

  Uppsala University

  13 shared

• Chandra Nair

  Chinese University of Hong Kong

  11 shared

• Soham R. Phade

  11 shared

• Jean Walrand

  10 shared

• François Baccelli

  École Normale Supérieure - PSL

  10 shared

Labs

• CLIMBPI

  Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley

Education

• Ph.D., Electrical Engineering and Computer Sciences

  University of California, Berkeley

  1990

• M.S., Electrical Engineering and Computer Sciences

  University of California, Berkeley

  1986

• B.S., Electrical Engineering

  Indian Institute of Technology, Madras

  1984

Awards & honors

• IEEE Information Theory Society Paper Award (1998)
• IEEE ComSoc Stephen O. Rice Prize (2000)
• IEEE Trans. on Communications (Stephen O. Rice Prize Paper A…
• IEEE Trans. Information Theory (Bits through queues) (1996)
• IEEE Trans. Automatic Control (Asymptotically efficient adap…

Similar researchers at University of California, Berkeley

• Christian Borgsh-65
• Alexandre Bayenh-61
• Ana Claudia Ariash-54