My interests are in the areas of Machine Learning and Data Mining, Artificial Intelligence, and Efficient Algorithms and my research explores theoretical questions, empirical questions, and applications. Most recently my work has focused on problems in two broad areas: Theory, Algorithms and Applications of Graphical models and Stochastic Planning (aka Reinforcement Learning) and its Relation to Probabilistic Inference.
His research interests are in developing agents that can learn from data, build representations of their world, use such knowledge for reasoning and decision making, and act in their environment so as to optimize their objectives. His recent work spans topics in AI (probabilistic planning, knowledge representation), machine learning (graphical models, approximate inference, computational learning theory) and the connections between these areas.
Our recent work has focused on two aspects: developing general algorithms that are applicable to many models and develping a theory that explains why and when Bayesian machine learning algorithms work well. Our work introduced novel fixed-point algorithms that improve convergence speed of variational inference methods.
- Algorithms and Theoretical Computer Science
- Artificial Intelligence and Machine Learning
- Databases and Data Mining
Research Topic: Planning and Inference
Research Topic: Theory, Algorithms and Applications of Graphical Models