Center for Science of Information Seminar
From Communication to Information Processing: An Information Theoretic Perspective
by Mohsen Heidari, University of Michigan, Ann Arbor
Wednesday, January 23
2:00 pm - 3:00 pm
HAAS Hall Room 111
Over the past half a century, information theory has evolved from a mathematical tool for analyzing communication systems into an essential framework for studying virtually all aspects of information processing. In the first part of this talk, I’ll present methods to characterize the error exponent of multiple-access channel with feedback and variable-length codes. By making a connection between this problem and distributed hypothesis testing, I will present bounds on the error exponent for such settings. In the second part of the talk, I will discuss the approximation of Boolean functions through the lens of information theory. I will show that this problem is related to distributed processing of correlated random sequences. This problem was studied by Witsenhausen in 1975. With this connection, I will present performance guarantees under the mismatch probability for the approximation of a Boolean function with non-uniform inputs. Applications of this problem is in feature selection in data science and learning of Boolean function.
Biography of the speaker:
Mohsen Heidari is a postdoctoral scholar working with Sandeep Pradhan at the University of Michigan in Electrical Engineering and Computer Science department. He received a Ph.D. degree in Electrical Engineering and a M.Sc. degree in Applied Mathematics from the University of Michigan in 2018 and 2017, respectively. Prior to joining Michigan, he received a B.Sc., and a M.Sc. degree in Electrical Engineering from Sharif University of Technology, in 2011 and 2013, respectively. His research interests are in network information theory, communication theory and quantum information theory.
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