Information Theory Lab

About the lab

The information theory lab carries out research in the area of information theory, which deals with the fundamentals of information processing and transmission. We are interested in its applications to blockchain systems, machine learning, computational biology and wireless networking.

We are always looking for highly motivated Ph.D. students and postdoctoral researchers, with interests in information theory, machine learning and algorithms. We work both on the mathematical theory for these problems as well as in engineering the associated systems. Students with a deep interest in either theory or in systems development are encouraged to apply.


PI: Sreeram Kannan
Assistant Professor
Electrical & Computer Engineering Department
University of Washington, Seattle
EEB 414
Phone: (206) 685-8756
Email: <ksreeram> at uw dot edu


  • I will be speaking at the National Academy of Engineering, Frontiers of Engineering US-Japan meet 2021 (postponed due to Covid19).
  • I will be speaking at the Stanford Blockchain Conference Feb 2020
  • I will be speaking at the Information Theory & Applications workshop at San Diego, Feb 2-7, 2020.
  • I am organizing a session on the "Blockchain Trilemma" at MIT Cryptosystems Summit , Oct 6. 2019.
  • I will be speaking at the Allerton Conference at University of Illinois, Urbana-Champaign in the blockchain session Sep 27, 2019.
  • Speaking at TTI Chicago Workshop on Learning-based Algorithms on "Learning statistical property testers" .
  • Nov-Dec 2018: I will be giving talks on blockchain algorithms at UIUC, Stanford, Berkeley, UCLA, TAMU and UT Austin.
  • Upcoming papers in NIPS 2018: Deepcode: Feedback codes via Deep Learning and Estimators for Multivariate Information Measures in General Probability Spaces
  • I had the pleasure of giving a tutorial with Hyeji Kim and Sewoong Oh at ISIT 2018 on "Information theory and deep learning: An emerging interface"
  • Allerton paper on "Potential conditional mutual inforamtion"
  • Upcoming papers in NIPS 2017: "Estimating Mutual Information for Discrete-Continuous Mixtures" and Discovering Potential Correlations via Hypercontractivity
  • Early Faculty Career Award from NSF for project on "Information theoretic methods for RNA Analytics"
  • Upcoming paper on ``Resolving multicopy duplications in the human genome'' to appear in RECOMB, 2017.
  • Paper on ``Network inference via Directed information: The deterministic limit'' at Allerton 2016. Upcoming: applications to single cell analysis.
  • Tutorial on ``Combinatorial methods for nucleic acid sequence analysis'' at ACM BCB, 2016 with Mark Chaisson.
  • Paper on temporal latent variable inference available at Arxiv. Paper will be presented at IEEE Data Sciences and Advanced Analytics conference 2016.
  • Preprint of ICML 2016 paper on Causal strength inference now available on Arxiv. Applications to single-cell analysis.
  • Preprint of paper on RNA-Seq assembly now available on Biorxiv.
  • Our RNA-Seq assembler, Shannon, is now available under GNU public license 3.0: Shannon
  • NIH R01 Award for "Optimal Algorithms for RNA Sequence Assembly", Sep. 2015 with Lior Pachter , UC Berkeley and David Tse , Stanford University.
  • Preview article "Fundamental limits of Search" in Cell Systems, Aug. 2015.
  • TPC Member for Information theory workshop, Jeju Island, Oct. 2015
  • TPC Member for ACM BCB - Conference on Bioinformatics, Computational Biology, and Health Informatics, Sep. 2015