Trac Tran, PhD

Professor, Dept. of Electrical and Computer Engineering The Johns Hopkins University

rac D. Tran S’94-M’98-SM’08 received the B.S. and M.S. degrees from the Massachusetts Institute of Technology, Cambridge, in 1993 and 1994, respectively, and the PhD degree from the University of Wisconsin, Madison, in 1998—all in Electrical Engineering.

In July of 1998, Dr. Tran joined the Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, where he was recently promoted to the rank of Professor. His research interests are in the field of digital signal processing, particularly in sparse representation, sparse recovery, sampling, multi-rate systems, filter banks, transforms, wavelets, and their applications in signal analysis, compression, processing, and communications. His pioneering research on integer-coefficient transforms and pre-/post-filtering operators has been adopted as critical components of Microsoft Windows Media Video 9 and JPEG XR – the latest international still-image compression standard ISO/IEC 29199-2.

Dr. Tran was the co-director (with Prof. J. L. Prince) of the 33rd Annual Conference on Information Sciences and Systems (CISS’99), Baltimore, MD, in March 1999. In the summer of 2002, he was an ASEE/ONR Summer Faculty Research Fellow at the Naval Air Warfare Center & Weapons Division (NAWCWD) at China Lake, California. He is currently a regular consultant for the U.S. Army Research Laboratory in Adelphi, Maryland. Dr. Tran has served as Associate Editor of the IEEE Transactions on Signal Processing as well as IEEE Transactions on Image Processing. He was a former member of the IEEE Technical Committee on Signal Processing Theory and Methods (SPTM TC) and is a current member of the IEEE Image Video and Multidimensional Signal Processing (IVMSP) Technical Committee. He is currently serving his second term as an Associate Editor for IEEE Transactions on Signal Processing.

Main Day Conference 1

Thursday, January 30th, 2020

4:15 PM Compressive Object Tracking and Classification Using Deep Learning

This session presents a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. The approach has two parts: tracking and classification. Efforts to combine deep learning across military applications has a high utilization capacity, one that could drastically alter conventional radar operations.

-       Deep learning initiatives to improve tracking and classification of objects
-       Efforts to improve collaborative multi-sensor classification
-       Efforts to bolster compressed sensing, sparse recovery, and sparsity-based signal processing 

Check out the incredible speaker line-up to see who will be joining Trac.

Download The Latest Agenda