Research Interests

My research interests include probabilistic artificial intelligence and its applications to wireless sensor networks. Specifically, I am interested in the design and development of sensornet applications for real-world problems. Such problems are characterized by real-world phenomena with intrinsic uncertainty. My research focus on applying information fusion and statistical inference methods such as Bayesian inference, sequential Monte Carlo (SMC), and Markov Chain Monte Carlo (MCMC) methods to such problems. I am also interested in multimodal multisensor data fusion. My current work focuses on multitarget tracking and urban surveillance using Heterogeneous Sensor Networks (HSNs) through multimodal data fusion. I also tackled several implementation issues for HSNs such as time-synchronization and routing.

My long-term research interests are in the areas of ubiquitous/pervasive computing and ambient intelligence. Ambient intelligence is characterized by systems and technologies that are embedded, context-aware, personalized, adaptive and anticipatory. These technologies and systems are able to sense the ambient environment, collect data, transform the data into information, and actuate on the environment based on the information. My long-term research goals are to develop methods for understanding the real-world phenomena, and to design and develop systems and technologies that enable ambient intelligence.