Talks & Seminars
Title: Data Compression: A Survey
Prof. Madhu Sudan, Harvard University
Date & Time: December 19, 2019 14:00
Venue: Department of Computer Science and Engineering, Room No. 109, 01st Floor, New CSE/CC Building
The task of compressing data coming from a stochastic source has a rich history, with contributions from Shannon, Huffman, and Lempel and Ziv to name just a few. Depending on the nature of the source, and the length of the samples one wants to compress, one gets many variations of the problem. In this survey we'll try to go over the problems systematically and explain the state of knowledge. A particular class of sources of interest are the (Hidden) Markov models - here we will describe several algorithmic questions that seem wide open to this day!
Speaker Profile:
Madhu Sudan is a Gordon McKay Professor in the John A. Paulson School of Engineering and Applied Sciences at Harvard University, where he has been since 2015. Madhu Sudan got his Bachelors degree from IIT Delhi in 1987 and his Ph.D. from U.C. Berkeley in 1992. Between 1992 and 2015, Madhu Sudan worked at IBM Research (Research Staff Member 1992-1997), at MIT (Associate Professor 1997-2000, Professor 2000-2011, Fujitsu Chair Professor 2003-2011, CSAIL Associate Director 2007-2009, Adjunct Professor 2011-2015), and at Microsoft Research (Principal Researcher, 2009-2015). Madhu Sudan is a recipient of the Nevanlinna Prize awarded by the International Mathematical Union for outstanding contributions to mathematics of computer and information science, and the Infosys Foundation Prize in Mathematical Sciences. Madhu Sudan is a fellow of the Association for Computing Machinery, the Institute of Electrical and Electronics Engineers and the American Mathematical Society. He is a member of the American Academy of Arts and Sciences and the National Academy of Sciences. Madhu Sudan's research interests revolve around mathematical studies of communication and computation. Specifically his research focuses on concepts of reliability and mechanisms that are, or can be, used by computers to interact reliably with each other. His research draws on tools from computational complexity, which studies efficiency of computation, and many areas of mathematics including algebra and probability theory. He is best known for his works on probabilistic checking of proofs, and on the design of list-decoding algorithms for error-correcting codes. His current research interests include property testing which is the study of sublinear time algorithms to estimate properties of massive data, and communication amid uncertainty, a mathematical study of the role of context in communication.
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