AMIT K. ROY-CHOWDHURY

   Assistant Professor

   Room 322 EBU-II
   Department of Electrical Engineering,

   University of California, Riverside, CA 92521

   Affiliations:

   Cooperating Faculty, Dept. of Computer Science and Engineering

   Member, Center for Research in Intelligent Systems

   Member, Center for Plant Cell Biology

 

   Phone: 951-827-7886

   Fax:     951-827-2425

   Email

 

Short Bio

Amit K. Roy-Chowdhury is an Assistant Professor of Electrical Engineering and a Cooperating Faculty in the Dept. of Computer Science at the University of California, Riverside. He completed his PhD in 2002 from the University of Maryland, College Park, where he also worked as a Research Associate in 2003. Previous to that, he received his Masters in Systems Science and Automation from the Indian Institute of Science, Bangalore. His research interests are in the broad areas of image processing and analysis, computer vision, video communications and statistical methods for signal processing, pattern recognition and machine learning. His current research projects include network-centric scene analysis in camera networks, physics-based mathematical modeling of image appearance, activity modeling and recognition, face and gait recognition, biological video analysis and distributed video compression. The work is supported by grants from the National Science Foundation, Office of Naval Research, Army Research Office, and private industries. Dr. Roy-Chowdhury has over seventy papers in peer-reviewed journals, conferences and edited books. He is an author of the book titled "Recognition of Humans and Their Activities Using Video". He is on the program committee of many major conferences in computer vision and image/signal processing and is a regular reviewer for the main journals in these areas. He is an Associate Editor of the IAPR journal Machine Vision and Applications and is on the organizing committees of CVPR 2008 and ICIP 2008. For more details, please see his CV.

RESEARCH INTERESTS

  • Computer Vision and Image Processing
  • Imaging/Non-imaging Sensor Networks
  • Video Communication
  • Statistical Methods in Signal Processing, Pattern Recognition and Machine Learning.
  • Biological Image Processing.