معرفی
Tamás Linder is a Professor of Mathematics and Engineering at Queen's University, Canada, affiliated with the Department of Mathematics and Statistics. He holds a P.Eng. designation and has been at Queen's since 1998. His academic journey includes a Ph.D. from the Hungarian Academy of Sciences (1992) and postdoctoral roles at the University of Hawaii and University of Illinois. Linder's research focuses on information theory, data compression, quantization, and machine learning, with notable contributions to source coding, joint source-channel coding, and privacy-aware estimation.
Education: M.Sc. in Electrical Engineering (Technical University of Budapest, 1988), Ph.D. in Electrical Engineering (Hungarian Academy of Sciences, 1992).
Research interests include information theory fundamentals, machine learning applications, statistical pattern recognition, and privacy-preserving techniques. His work bridges theoretical foundations and practical applications in communication systems, quantization theory, and distributed control.
Key awards include IEEE Fellow (201x), Ontario Premier's Research Excellence Award (2002), and Queen's University Chancellor's Research Award (2003). He has advised numerous graduate students and postdocs, contributing to over 150 publications in top-tier venues like IEEE Transactions on Information Theory and Automatic Control.
Linder's research groups and collaborations involve interdisciplinary projects in control systems, statistical inference, and signal processing. His labs focus on advancing zero-delay coding, distributed optimization, and privacy-aware machine learning systems.
