Yanina Shkel
Research Scholar

Department of Electrical Engineering
Princeton University

B311 Engineering Quadrangle
Olden Street
Princeton, NJ 08544

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


ABOUT


I am currently a research 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.

RESEARCH


I work on theoretical aspects of data science; 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. My research program draws on tools from {information, learning, coding}-theory, statistics, cryptography, and is currently focused on the following two directions:

  • Interplay between data compression, secrecy & learning
  • Novel theoretical frameworks for low-latency data transmission

PUBLICATIONS (SELECT)


Data compression, secrecy & learning

Low-latency data transmission

  • Wei Cao, Alex Dytso, Yanina Shkel, Gang Feng, and H. Vincent Poor
    Sum-Capacity of the MIMO Gaussian Many-Access Channel
    Submitted.

  • 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]

Full publications list

TEACHING


During Fall 2018 I am teaching ELE 530 (Theory of Detection and Estimation) at Princeton University. This course is a graduate-level introduction to the fundamental theoretical principles of signal processing related to detection and estimation.

Alex Dytso and I recently had the pleasure of being guest lecturers at the Princeton University Freshman Seminar on Princeton and the Dawn of the Information Age.

Since my research focuses on theoretical frameworks for fundamental engineering problems, I am particularly enthusiastic about teaching engineering courses with strong mathematical components as well as courses in applied mathematics, statistics, and related fields.