My research is in the field of Automated Reasoning in Artificial Intelligence and focused on Graphical Models. Graph based models (e.g., Bayesian and constraint networks, influence diagrams and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both Artificial Intelligence and Computer Science in general. These models are used to accomplish many science and engineering tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification and bioinformatics. These reasoning problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization and probabilistic inference. It is well known that these tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques for significantly advancing the state of the art. Our approach is to devise methods through the understanding and exploitation of tractable reasoning tasks and use those islands of tractability in the design of general anytime algorithms. As their name implies, anytime methods provide a solution anytime during the processing, with the added provision that the quality of the solution improves if more time is available.

To summarize, my research interests are in the areas of Automated Reasoning, Knowledge-Representation, Planning and Learning.