University of Hawaii

Electrical Engineering

Emerging Frontiers of Science of Information

Date: 2017-03-14           Add to Google Calendar
Time: 1:30pm-2:30pm
Location: Keller 214
Speaker: Prof. Wojciech Szpankowski, Saul Rosen Distinguished Professor of Computer Science, Purdue University

Information is one of the defining aspects of our era, permeating every facet of our lives. Our ability to extract and effectively utilize information from various processes has the potential for significant advances in our day-to-day lives. Our current understanding of information dates back to Claude Shannon's formalized modern digital communication and storage principles, paved the way for the Internet, DVDs and iPods of today. Shannon's focus on what is fundamental, and his precise quantitative analyses, continue to motivate and inspire. However, in the current world, information is not merely communicated; it is also acquired, curated, suitably abstracted and represented, aggregated, analyzed, retrieved, inferred, secured, and used in various scientific, engineering, and socio-economic processes. A comprehensive Science of Information (CSoI) to meet the new challenges posed by the rapid advances in basic and social sciences, economics and commerce, and the engineering, coupled with the ability to collect, communicate, and analyze large amounts of data. Its mission is to advance science and technology through a new quantitative understanding of the representation, communication, and processing of information. Led by Purdue, Center member institutions include Berkeley, Bryn Mawr, Howard, MIT, Princeton, Stanford, Texas A&M, UCSD, UIUC, U. Hawaii. Other institutions (e.g., Rutgers, ETH, and LINCS, Paris) are affiliated with the Center in various roles. In this talk, after briefly reviewing some of Shannon's accomplishments, we proceed to explain novel challenges in analyzing multi-modal data, present representative results from our approach in provable security in networks, algorithms for classifying tweets through joint string complexity, and offer some interesting information-theoretic models for biological systems.