STR Tech Talk: "A Mathematical Medley: Multi-Target Tracking and Mathematical Tools for Finding Patt

Wednesday, April 5, 2017
12:00 p.m.-2:30 p.m.
1146 AV Williams
Kara Stamets
301 405 4471
stametsk@umd.edu

“A Mathematical Medley: Multi-Target Tracking and Mathematical
Tools for Finding Patterns in Large Datasets”

Speakers:
Ed Baranoski, Vice President
Peter Chin, Chief Scientist
Stefano Coraluppi, Principal Scientist & ECE/ISR Alum

12-1 PM: STR Presentation to students and faculty. Technical, company overview and employment information. Pizza included.

2-2:30 PM: STR and student discussions, towards employment opportunities

Abstract: Systems and Technology Research (STR) is a small business based in the Boston MA area, with offices in the DC area as well as in Dayton OH. STR is active in several areas of science and technology including signal processing, control, estimation, machine learning, optimization, and decision support. Students interested in internship or employment opportunities, as well as all those conducting research in these areas, are encouraged to attend. The first part of the presentation will provide a brief company overview, as well as some discussion of research efforts in multi-target tracking using both multiple-hypothesis and graph-based solution paradigms. The second part of the presentation will address Nash, Nyquist, and neural networks.

Standing tall among the major mathematical achievements of 20th century are two theorems whose subsequent impacts far outweighed their original intent. One such theorem is due to John Nash, whose proof of the existence of equilibrium in a non-cooperative game gave rise to the concept of the eponymous Nash Equilibrium, which in many ways revolutionized the field of economics. Another is due to Harry Nyquist, whose Nyquist–Shannon sampling theorem, which states that every time-varying band-limited signal can be perfectly reconstructed from an infinite sequence of samples acquired at the twice rate of its maximum frequency, laid the foundation of the modern information and communication theory. We will describe some recent progress in extending these results - in dynamic game theory, where the rules of the game change over time, and in the theory of compressive sensing, which guarantees perfect reconstruction of signals from far fewer samples than required by the Nyquist theorem, if the signals are sparse in some appropriate domain. We will then describe some applications of these extensions for cyber security and wireless security, respectively, and a potential surprising connection between these two important theorems. Furthermore, as cyber systems are often aptly described as graphs/networks, we will describe a recent result of Chin, Rao and Vu on finding communities in networks. Finally, we will address how recent development of deep neural networks can help finding patterns in large data sets.

Audience: Graduate  Undergraduate  Faculty 

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