Michael C. Gastpar
Michael Gastpar is a (full) Professor at EPFL. From 2003 to 2011, he was a professor at the University of California at Berkeley, earning his tenure in 2008.
He received his Dipl. El.-Ing. degree from ETH Zürich, Switzerland, in 1997 and his MS degree from the University of Illinois at Urbana-Champaign, IL, USA, in 1999. He defended his doctoral thesis at EPFL on Santa Claus day, 2002. He was also a (full) Professor at Delft University of Technology, The Netherlands.
His research interests are in network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience.
He is a Fellow of the IEEE. He is the co-recipient of the 2013 Communications Society & Information Theory Society Joint Paper Award. He was an Information Theory Society Distinguished Lecturer (2009-2011). He won an ERC Starting Grant in 2010, an Okawa Foundation Research Grant in 2008, an NSF CAREER award in 2004, and the 2002 EPFL Best Thesis Award. He has served as an Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11), and as Technical Program Committee Co-Chair for the 2010 International Symposium on Information Theory, Austin, TX.
Laboratory for Information in Networked Systems
We gratefully acknowledge support from:
Fields of expertise
|N. Amin, M. C. Gastpar, and F. E. Theunissen
PLOS ONE, 8(4), 2013
|Selective and Efficient Neural Coding of Communication Signals Depends on Early Acoustic and Social Environment|
|B. Nazer and M. Gastpar
IEEE Transactions on Information Theory, 57(10):6463-6486, October 2011. Received the 2013 Communications Society & Information Theory Society Joint Paper Award.
|Compute-and-Forward: Harnessing Interference Through Structured Codes|
|B. Nazer and M. Gastpar
Proceedings of the IEEE, 99(3):438-460, March 2011
|Reliable physical layer network coding|
|G. Kramer, M. Gastpar, and P. Gupta
IEEE Transactions on Information Theory, 51(9):3037-3063, September 2005
|Cooperative strategies and capacity theorems for relay networks|
IEEE Transactions on Information Theory, 53(2):471-487, February 2007
|On capacity under receive and spatial spectrum-sharing constraints|
Teaching & PhD
- Communication Systems,
- Computer Science
- Doctoral program in computer and communication sciences
- Doctoral Program in Neuroscience
This class teaches the theory of linear time-invariant (LTI) systems. These systems serve both as models of physical reality (such as the wireless channel) and as engineered systems (such as electrical circuits, filters and control strategies).