Lampros Gavalakis

Lampros Gavalakis is a Postdoctoral Research Associate at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, affiliated with the INFORMED AI Hub. Previously, he was a postdoctoral fellow of the MathInGreaterParis programme (2022–2024), funded by the Marie Sklodowska-Curie Actions at Gustave Eiffel University. He holds a Ph.D. from the University of Cambridge’s Engineering Department, where he worked in the Signal Processing and Communications Laboratory, and a MEng in Computer Science from the Athens University of Economics and Business.

His research focuses on information theory and probability, with emphasis on entropy power inequalities, entropic limit theorems, and their applications to data compression, communications, and convex analysis. Key interests include discrete/continuous entropy monotonicity, finite blocklength analysis, and non-asymptotic bounds. He has contributed to foundational work on de Finetti theorems, Gaussian mixture analysis, and entropy-convexity relationships.

Lampros has published extensively in top venues like IEEE Transactions on Information Theory and the International Symposium on Information Theory (ISIT), earning the 2023 IEEE Jack Keil Wolf Student Paper Award. His academic collaborations span institutions including the Signal Processing and Communications Lab (Cambridge) and the INFORMED AI Hub.

• Education:
  • PhD in Engineering (University of Cambridge, 2022)
  • MEng in Computer Science (Athens University of Economics and Business, 2017)
• Awards:
  • IEEE Jack Keil Wolf ISIT Student Paper Award (2023)
• Teaching:
  • Lecturer for Concentration Inequalities (Part III Mathematics, Cambridge, 2025)

His research bridges probability theory and information science, with applications to coding, stochastic processes, and high-dimensional analysis.