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Michelle EffrosView profile

Professor

• Information Theory
• Data Compression
• Communications
• +6 more

Michelle Effros is the George Van Osdol Professor of Electrical Engineering and Vice Provost at the California Institute of Technology. She holds B.S., M.S., and Ph.D. degrees from Stanford University (1989–1994). Her research focuses on information theory, data compression, communications, theoretical neuroscience, and network coding. She founded the Caltech Data Compression Laboratory (1994) and co-founded Code On Technologies (2009–2016). Education: B.S. (with honors), Electrical Engineering, Stanford University, 1989 M.S., Electrical Engineering, Stanford University, 1990 Ph.D., Electrical Engineering, Stanford University, 1994 Research interests include information theory , data compression , neurostability , neuronal memory , and network coding . Her work spans source coding (data compression), channel coding (reliable communication), and the mathematical underpinnings of neural systems. Her publications explore topics like network coding reliability, finite-blocklength analysis, and secure multicast. Awards include IEEE Fellow, NSF CAREER Award, and Okawa Research Grant. She has served as Editor of the IEEE Information Theory Society Newsletter and President of the IEEE Information Theory Society. Grants and advising: While no student names are listed, her leadership roles in funding bodies like the NSF Advisory Committee reflect her influence on research directions. Labs include the Data Compression Laboratory.

Yury PolyanskiyView profile

Professor

• Information Theory
• Machine Learning
• Statistics
• +3 more

Yury Polyanskiy is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Laboratory for Information and Decision Systems (LIDS), the Institute for Data, Systems, and Society (IDSS), and the MIT Statistics and Data Science Center. He holds a Ph.D. from Princeton University (2010) and an M.S. from the Moscow Institute of Physics and Technology (2005). His research focuses on information theory, machine learning, statistical inference, error-correcting codes, and wireless communication. He has contributed to fundamental limits of communication systems, finite-blocklength analysis, and applications of information theory to learning and signal processing. Notable awards include the 2020 IEEE Information Theory Society James Massey Award, the 2013 NSF CAREER Award, and the 2011 IEEE Information Theory Society Paper Award. His work spans theoretical advancements and practical applications, including the development of the SPECTRE toolbox for short-packet communication. He is also co-authoring a textbook on information theory. Recent research highlights include studies on quantization techniques for machine learning (e.g., NestQuant), transformer-based empirical Bayes methods, and novel approaches to massive random access in wireless networks (e.g., unsourced multiple access). His contributions bridge information theory and modern data science, addressing challenges in high-dimensional data representation, neural network dynamics, and efficient communication architectures.

• Wireless Communication
• Information Theory
• Error Correction Coding
• +3 more

Dr. Min Qiu is a Postdoctoral Research Associate at the University of New South Wales (UNSW), with a research focus on coding theory, information theory, and wireless communication systems. He received his Ph.D. in Electrical Engineering from UNSW in 2019 and a B.E. (Hons.) in Electronics and Telecommunications Engineering from the Australian National University in 2012. His work addresses fundamental limits of communication systems and designs reliable networks and storage solutions under unreliable conditions. Dr. Qiu's research spans bandwidth-efficient coded modulation, delay-Doppler communications, finite blocklength coding, lattice coding for interference management, and joint sensing and communications. He has served as a reviewer for over 15 journals and conferences, including IEEE Transactions on Communications and the IEEE International Symposium on Information Theory. Key scientific contributions include awards for exceptional peer-review service and grants such as the 2021 ECA EDI Seed Grant. He is affiliated with IEEE Communication Society, IEEE Information Theory Society, and Engineers Australia.