Yanina Shkel

Yanina Shkel is an Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences. She holds appointments in the Information Processing Group, SSC-ENS, and SIN-ENS departments, with her office located at INR 131. Her academic journey includes a PhD from the University of Wisconsin-Madison (2014), postdoctoral work at Princeton University and University of Illinois at Urbana-Champaign, and prior industry experience at Morningstar Inc. and 3M Corporate Research Labs.

Her research spans theoretical aspects of data science with focus on information theory applications to privacy, secrecy, and data compression. Key areas include mathematical models for privacy-aware information processing, finite blocklength data compression, secret key generation from common randomness sources, and information-theoretic methods in cryptography. She employs tools from information theory, learning theory, coding theory, statistics, and cryptography in her work.

Analysis of her recent publications reveals a strong trend toward privacy-preserving information processing, with maximal leakage emerging as a central metric across multiple applications including biometric security and data compression. Her work bridges theoretical foundations with practical applications in cryptography and secure information systems, demonstrating increasing focus on quantifiable privacy metrics and their fundamental limits.

• Swiss NSF Starting Grant

Professor Shkel leads a research lab supported by the Swiss NSF Starting Grant, mentoring doctoral students including Coban Serhat Emre, Yadav Anuj Kumar, and Çadir Cemre. Her funding history includes the NSF Center for Science of Information Postdoctoral Fellowship during her postdoctoral research. She actively recruits PhD students through EPFL's EDIC program with emphasis on theoretical data science candidates.

Her laboratory focuses on theoretical data science with particular emphasis on privacy-aware information processing systems, developing mathematical frameworks for quantifying and optimizing information leakage in various applications.