Research within SLMC

Research Interests

The SLMC group has a broad and highly inter-disciplinary research agenda spanning statistical machine learning, formal learning theory, learning in connectionist systems (artificial and biological); robotic, humanoid and biological motor control, adaptive/learning control; and multimodal cue integration and attentional strategies.

• Some of the currently active and archived project pages are listed here.
• A more detailed (somewhat dated) description of some of the projects can be found here.

Software

Publications

Some of the topics of recent interest are listed below (with links to papers).

• Statistical Learning

• Nonparametric Learning in High Dimensions (papers)
• Bayesian Approaches (papers)
• Kernel and Exact Incremental methods- Functional Analysis (papers)
• Active/Query based learning & Learning curves (papers)
• Reinforcement Learning (papers)

• Visual Attention & Oculomotor Control (papers)

• Real Time Learning for Robot Control & High Dimensional Systems (papers)

• Dimensionality Reduction (papers)

• Multimodal Learning and Cue Integration

• Haptic Discrimination: Encoding and Decoding Strategies