Yanina Shkel
Postdoctoral Scholar

Department of Electrical Engineering
Princeton University

B311 Engineering Quadrangle
Olden Street
Princeton, NJ 08544

E-mail: yshkel [at] princeton [dot] edu


RESEARCH


I am broadly interested in identifying laws which govern the behavior of information in both engineered and naturally occurring systems, and using these laws to better understand the capabilities of such systems. I am particularly interested in developing theoretical frameworks for novel application domains which are inadequately addressed by current paradigms. My most recent research focuses on using information theory, statistics, and learning theory to understand the interplay between data compression and learning, with an emphasis on lossy compression and sequential prediction.

ABOUT


I am currently a postdoctoral scholar at Princeton University where I work on bounds on IoT security for estimation with Vincent Poor. Before this I was a postdoctoral fellow with the NSF Center for Science of Information where I had the pleasure of collaborating with Sergio Verdú and Maxim Raginsky.

I completed my PhD at the University of Wisconsin-Madison under the supervision of Stark C. Draper. Before graduate school I also worked as a developer for Morningstar Inc. where I administered databases containing and processing large amounts of financial data. More recently, I was an intern at 3M Corporate Research Labs where I utilized my background in computation and information sciences for materials and product driven needs of 3M.

I am on the job market this year. Take a look at my recent work below.

PUBLICATIONS


  • Yanina Shkel, Maxim Raginsky, and Sergio Verdú
    Universal compression, list decoding, and logarithmic loss
    Submitted [PDF]

  • Yanina Shkel, Maxim Raginsky, and Sergio Verdú
    Sequential prediction with coded side information under logarithmic loss
    To be presented at Algorithmic Learning Theory (ALT) 2018 [PDF]

  • Yanina Shkel and Sergio Verdú
    A single-shot approach to lossy source coding under logarithmic loss
    IEEE Transactions on Information Theory, vol. 64, no. 1, pp. 129 - 147, 2018
    [LINK]

    Preliminary version appeared in 2016 Proceedings of IEEE International Symposium on Information Theory, Barcelona, Spain

  • Yanina Shkel, Maxim Raginsky, and Sergio Verdú
    Universal lossy compression under logarithmic loss
    Presented at the 2017 IEEE International Symposium on Information Theory [LINK]

  • Yanina Shkel and Sergio Verdú
    A Coding Theorem for f-Separable Distortion Measures
    [PDF]

    Based on an invited talk in 2016 Information Theory and Applications Workshop, La Jolla, CA

  • Yanina Shkel, Vincent Y. F. Tan, and Stark C. Draper
    Unequal message protection: asymptotic and non-asymptotic tradeoffs
    IEEE Transactions on Information Theory, vol. 61, no. 10, pp. 5396 - 5416, 2015
    [LINK]

    Preliminary versions appeared in 2014 Proceedings of IEEE International Symposium on Information Theory, Honolulu, HI and in 2013 Proceedings of IEEE International Symposium on Information Theory, Istanbul, Turkey

  • Yanina Shkel, Vincent Y. F. Tan, and Stark C. Draper
    Second-order coding rate for m-class source-channel codes
    Proceedings of the 53rd Allerton Conference on Communication, Control, and Computing, 2015
    [LINK]

  • Yanina Shkel, Vincent Y. F. Tan, and Stark C. Draper
    On mismatched unequal message protection for finite block length joint source-channel coding
    2013 Proceedings of IEEE International Symposium on Information Theory, Istanbul, Turkey
    [LINK]

  • Bobak Nazer, Yanina Shkel, and Stark C. Draper
    The AWGN Red Alert Problem
    IEEE Transactions on Information Theory, vol. 59, no. 4, pp. 2188 - 2200, 2013
    [LINK]

  • Yanina Shkel, Stark C. Draper, and Bobak Nazer
    On the cooperative red alert exponent for the AWGN-MAC with feedback
    Proceedings of the 49th Allerton Conference on Communication, Control, and Computing, 2011
    [LINK]

  • Yanina Shkel and Stark C. Draper
    Cooperative reliability for streaming multiple access
    2010 Proceedings of IEEE International Symposium on Information Theory, Austin, TX
    [LINK]