Stochastic generative models for complex visual phenomena.
Gaussian processes, non-linear Bayesian inference, evaluation and comparison of network models.
Decision making under uncertainty, reinforcement learning, unsupervised learning.
Machine learning of dynamic data, graphical models and Bayesian networks, neural networks.
Machine learning for medical diagnosis and biological data analysis.
Reinforcement learning and conditioning, mathematical models of neural processing.
Learning and memory in the brain, hippocampus.
Unsupervised learning, probabilistic density estimation, machine vision.
Object recognition, cognitive neuroscience, interaction between vision and motor movements.
Reinforcement Learning, Adaptive Critic Designs, Control, Optimization, Graph Theory, Bioinformatics, Intrusion Detection.