My research focuses on machine learning, applying learning techniques
on real-world applications, and methods for evaluating and testing
decision systems. Specifically, I am working on new methods
for learning causal and probabilistic models for temporal data,
learning and decision making with non-uniform loss functions,
automated feature construction,
active and semi-supervised learning, kernel methods, loss function
decomposition, hierarchical learning, and incorportaing domain knowledge
into learned models.
I am also interested and working on testing and validation methods for
decision systems, learned models, learning algorithms, and large systems that contain
learning components.
In general, my research interests span inductive learning, methods for
scaling up and improving the performance of classification and
regression techniques, computational and statistical learning theory,
unsupervised and reinforcement learning, game theory.