Research in VLL generally focuses on computational neuroscience and artificial intelligence with emphasis on vision and learning.
We are interested in both computer and human vision research. Regarding computer vision, we strive to develop purely computational or biologically-inspired algorithms
for solving real-world vision tasks. With respect to human vision, we aspire to understand neural and behavioral mechanisms by which humans (as well as some animals) perceive the visual world to construct meaningful interpretations. Some topics of our interest are: Bottom-up and Top-down Visual Attention, Visual Search, Ego-centric Vision, Mind Reading (By means of eye movements),
Object Recognition, Detection, and Natural Scene Understanding (e.g., Rapid Scene Categorization), ...
Learning is another research focus in VLL. Some related topics include: Active Learning, Optimal Learning, Information Foraging, Bayesian Search Theory, Function Learning, Bayesian Global Optimization, Gaussian Processes, Reinforcement Learning, Category Learning, Feature Learning, Theory of Mind, Biology-inspired Computing, ...
We conduct research studies which include both an experimental component (e.g., psychophysics, eye-tracking, EEG, fMRI, etc) and a computational component (e.g., data modeling, neural networks, neurally-inspired artificial intelligence, ...).