Vijayakumar Bhagavatula

U.A. and Helen Whitaker Professor, Electrical and Computer Engineering
Affiliated Faculty, DSSC
Director, CMU - Africa

Bio

Vijayakumar (Kumar) Bhagavatula is the U.A. and Helen Whitaker Professor of Electrical and Computer Engineering. He received a B.Tech. degree in electrical engineering from the Indian Institute of Technology, Kanpur in 1975, an M.Tech. degree in electrical engineering, also from the Indian Institute of Technology, Kanpur in 1977, and a Ph.D. in electrical engineering from Carnegie Mellon University in 1980. He joined the faculty of Electrical and Computer Engineering in 1982. He has served as interim dean for the College of Engineering and as acting department head of Electrical and Computer Engineering.

Education

Ph.D., 1980 
Electrical Engineering 
Carnegie Mellon University

MTech, 1977 
Electrical Engineering 
Indian Institute of Technology, Kanpur

BTech, 1975 
Electrical Engineering 
Indian Institute of Technology, Kanpur

Research

Pattern Recognition for Biomerics

One way of matching live biometric signatures (e.g., face images, iris images) with stored templates is to correlate the two. Professor Kumar and his students are developing several spatial frequency-domain methods to perform these correlations in the presence of significant appearance variability (e.g., due to illumination changes, expression differences, etc.) in the biometric signatures. These correlation filters have performed very well in the recent Face Recognition Grand Challenge (FRGC) and Iris Challenge Evaluation (ICE) conducted by US National Institute of Standards and Technology (NIST).

Coding & Signal Processing for Data Storage

As we attempt to increase the densities of data storage systems, bits and tracks come much closer, causing increased intersymbol interference and noise. Advanced coding and signal-processing methods are being developed to provide reliable (i.e., low bit error rate) detection of the digital data stored on various media. Professor Kumar's research considers timing recovery methods and the equalization and detection of data recorded on high-density magnetic tape recording systems, holographic data storage systems and optical storage systems. His group is also investigating the use of low density parity check (LDPC) codes for data storage systems.

Keywords

• Coding and signal processing for data storage
• Biometrics
• Pattern recognition
• Computer vision
• Data mining
• Data storage systems
• Machine learning