We are interested in research, education, and engineering applications of signal and image processing. Our research is focused around the general theme of developing statistically-based methods for robust and efficient information extraction from observed uncertain, limited data. Our current research spans a wide variety of topics including (1) inverse problems and computed imaging with applications to radar, biomedical imaging, nondestructive evaluation, and array processing; (2) sparse signal representation and compressed sensing; (3) signal and pattern analysis, with distinct research thrusts in image segmentation, EEG-based brain-computer interfaces, and facial expression analysis based on video data; (4) data fusion, distributed inference, and sensor networks with a focus on communication-constrained inference and with applications to multi-sensor multi-target tracking and random field estimation.
Our laboratory provides an extremely interactive research environment for faculty, post-doctoral researchers, as well as both graduate and undergraduate students. Our laboratory is closely affiliated with the Computer Vision and Pattern Analysis (VPA) Laboratory, and holds strong collaborations with a number of groups around the world.