Research

My research interests are in the area of automated reasoning and planning, key building blocks of artificial intelligence. Some of the keywords that can be associated with my research include graphical models, probabilistic inference, sequential decision making, multiagent systems, Markov decision processes, mathematical optimization and machine learning.

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Multiagent Planning and Decision Making Using Probabilistic Inference

Traditionally, the area of decision making and planning in agent-based models such as Markov decision processes (MDPs) and its multiagent extension (Dec-MDPs) has evolved relatively independently from the area of machine learning. Recently some intriguing connections have been developed between problems in machine learning (ML) such as likelihood maximization and that of planning and decision making in agent-based models. My ongoing work explores this connection in the area of multiagent systems leading to efficient and scalable algorithms for several problems. Applying ML techniques to that of decision making promises to be a very exciting research direction for the future.

Relevant publications along this direction are:

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Decision Making and Learning for Computational Sustainability

Computational sustainability is an emerging interdisciplinary field that aims to bring together computational techniques from different areas of computer science and operations research for intelligent decision making to manage and conserve society and environment’s limited resources. Addressing important problems in sustainability such as network design for spatial conservation planning, understanding the population dynamics of an endangered species, and decision making for smart power grid management represent some of the core issues of my research agenda.

Relevant publications along this direction are:

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Approximate Inference in Graphical Models

Probabilistic graphical models (PGMs) are a powerful framework that arise in a variety of practical applications such as multiagent systems, computer vision and bioinformatics. PGMs succinctly encode the structure present in real world problems by combining the language of graphs with probability theory. Such structure can then be exploited to develop efficient inference algorithms.

In my work, I have developed several message-passing based inference algorithm for the problem of maximum-a-posteriori (MAP) estimation in PGMs. I have been also active at addressing inference problems for collective graphical models (CGMs) that are graphical models defined over aggregate-level data rather than the individual model. CGMs are particularly useful when one has access to aggregate level data such as modeling bird migration, visitor diffusion in theme parks and learning from anonymized datasets.

Relevant publications along this direction are: