Michael Gastpar

EPFL IC IINFCOM LINX
INR 130 (Bâtiment INR)
Station 14
1015 Lausanne

Expertise

Information Theory, Signal Processing, Communications, Systems Neuroscience
Research page
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.

SUPPORT

Awards

EPFL Best Thesis Award

0

NSF CAREER award

2004

Okawa Foundation Research Grant

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ERC Starting Grant

European Research Council

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Teaching & PhD

PhD Students

Marco Bondaschi, Anuj Kumar Yadav, Adrien Vandenbroucque, Cemre Çadir, Thomas Weinberger, Millen Kanabar, Yunzhen Yao

Past EPFL PhD Students

Chien-Yi Wang, Jingge Zhu, Giel Op 't Veld, Saeid Sahraei, Su Li, Erixhen Sula, Amedeo Roberto Esposito, Pradeep Aditya

Courses

Advanced Topics in Information Theory

COM-621

The class will focus on information-theoretic progress of the last decade. Topics include: Network Information Theory ; Information Measures: definitions, properties, and applications to probabilistic models.

Advanced information, computation, communication II

COM-102

Text, sound, and images are examples of information sources stored in our computers and/or communicated over the Internet. How do we measure, compress, and protect the informatin they contain?

Foundations of Data Science

COM-406

We discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas and techniques that come from probability, information theory as well as signal processing.