The current research foci of the Machine Learning Research Laboratory include:

• Machine Learning and Data Mining: Statistical, informational theoretic, and structural approaches to machine learning and data mining, Learning and refinement of deep neural networks, support vector machines, kernel classifiers, decision trees, Bayesian, and graphical models, Learning classifiers from distributed data, multiple instance/class data, spatiotemporal data, streaming, and incremental data, On-device machine learning, Ensemble learning, Supervised, unsupervised, semi-supervised, and reinforcement learning.

• Artificial Intelligence: Design of intelligent agents and multi-agent systems, Logical, probabilistic, causal, decision-theoretic, connectionist and symbolic knowledge representation and inference, Computational models of perception and action.

• Automated Machine Learning: Neural architecture search, Automatic selection, configuration and composition of machine learning algorithms, Representation learning, Meta learning.

• Other Topics of Interest: Machine learning algorithms with differential privacy, AI-based algorithms for rehabilitation robots, Bioinformatics and computational biology, Biological computation and evolutionary computation. Computational neuroscience, Computational learning theory.