Michael Gastpar
EPFL IC IINFCOM LINX
INR 130 (Bâtiment INR)
Station 14
1015 Lausanne
Web site: Web site: https://linx.epfl.ch/
+41 21 693 75 23
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Fields of expertise
Biography
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
We gratefully acknowledge support from: European Research Council (ERC)U.S. National Science Foundation (NSF)U.S. National Institutes of Health (NIH)The Okawa FoundationTeaching & PhD
Teaching
Communication Systems
Computer Science
PhD Students
Bondaschi Marco, Kanabar Millen Bharat, Vandenbroucque Adrien, Yadav Anuj Kumar, Yao Yunzhen, Çadir Cemre,Past EPFL PhD Students
Esposito Amedeo Roberto , Li Su , Op 't Veld Guillaume Jean , Pradeep Aditya , Sahraei Saeid , Sula Erixhen , Wang Chien-Yi , Zhu Jingge ,Courses
Advanced information, computation, communication II
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
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.
Advanced Topics in Information Theory
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.