On the theoretical side, my research interests lie in the general area of mathematical statistics and probability, and specifically in sequential hypothesis testing, with particular emphasis on the problem of (optimal) sequential quickest change-point detection. This problem is considered under different settings: composite hypotheses where the pre- and/or post-change parameters are unknown, distributed multi-sensor setup where the information available for decision making is distributed across a set of (geographically separated) sensors, and multi-channel detection-identification where it is also desired to identify the channel(s) that ran out of control. This problem has an enormous spectrum of applications: quality control, financial markets, target tracking and detection — to name a few.

On the applied side, I am particularly interested in such areas of application as rapid intrusion/anomaly detection in ultra-high-speed large-scale computer networks, information assurance and data fusion in multi-sensor & distributed systems, computer vision, specifically object/target detection and tracking, and scientific computing, particularly processing large arrays of data on parallel clusters. In the first two these areas, I am currently active on two projects, sponsored by the National Science Foundation and the U.S. Army Research Office. A particular outcome of these projects — the Hybrid Intrusion Detection System — is currently in preparation to be patented. In the area of computer vision and visual object/target detection and tracking, I am interested in the design and analysis of efficient algorithms for clutter rejection and scene stabilization, as well as for image resolution enhancement. I have a record of one successfully completed and one on-going project on this subject. The funding for these projects is provided by the U.S. Office of Naval Research and the U.S. Army Research Office.