Pavan Turaga

Turaga_PavanWEBAssistant Professor
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Research expertise
Computer Vision, Multimedia, Human Activity Analysis, Machine Learning, Healthcare applications.

Pavan Turaga joined ArizonaStateUniversity (ASU) in Fall 2011 as an Asst. Prof. jointly between the departments of Arts, Media, andEngineering and ElectricalEngineering (ECEE). He obtained his PhD in 2009 from the ECE Department at the University of Maryland, College Park under the guidance of Prof. RamaChellappa. He then spent two years as a Research Associate at the Center for Automation Research, UMD.  His research interests are in statistics and machine learning with applications to computer vision, human activity analysis, video summarization, dynamic scene analysis, statistical inference on manifolds, and interdisciplinary applications in preventative healthcare.

Professional preparation
Ph.D., electrical engineering, University of Maryland, College Park, 2009
M.S., electrical engineering, University of Maryland, College Park, 2007
B.Tech., electronics and communication engineering, India Institute of Technology, Guwahati, India, 2004

Recognition and awards
UMD Distinguished Dissertation award 2009
IBM Emerging Leader in Multimedia 2008.

Selected publications
J. Ni, P. Turaga, V. M. Patel, R. Chellappa, “Example-driven Manifold priors for Image Deconvolution”, accepted at IEEE Transactions on Image Processing 2011.

N. Shroff, P. Turaga, R. Chellappa, “Manifold Precis: An annealing method for selecting the best citizens on manifolds”, accepted at Neural Information Processing Systems (NIPS) 2011.

P. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa, “Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video based  Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.

N. Shroff, P. Turaga, and R. Chellappa, “Video Precis: Highlighting Diverse Aspects of Videos”, at IEEE Transactions on Multimedia, Dec 2010.

P. Turaga and Y. Ivanov, “Diamond Sentry: Integrating Cameras and Sensors for Real-Time Monitoring of Indoor Spaces”, accepted at IEEE Sensors Journal, Special Issue on Cognitive Sensor Networks, 2010.