Neuroinformatics
Neuroimaging has led to a dramatic increase in neuroscience information. Neuroinformatics research concerns new methods for analysis of neuroscience data and efforts to integrate and curate neuroscience databases. Our neuroinformatics research has made basic contributions to neuroimaging, including the first “mind reading” methods for fMRI and PET imaging. We have introduced machine learning in neuroimaging, developed the Brede neuroimaging database, and contributed tools for resampling based optimization of neuroimaging pipelines (NPAIRS). Current work concerns real-time methods for fMRI and EEG, connectivity modeling, and new search engine paradigms.
Machine Learning
Statistical machine learning abstracts data to active knowledge by identifying predictive relations and has become a major driver of the knowledge society. Machine learning drives the Google economy, empowers bioinformatics, and enables mind reading in neuroimaging. Our research in machine learning is rooted in statistics, including Bayesian and in resampling based methods, and has a strong algorithmic component. Past developments include ensembles, approximate inference, blind signal separation, and multiway methods. Current theoretical work concerns sparse representations, infinite models, multiway methods, and complex networks.
Cognitive Psychology
Both humans and computers are information processing devices. Many computing problems are trivial for humans while very difficult for machines, e.g. face recognition, language and content based search. Conversely, many computing problems are trivial for machines but hard for humans, e.g. reasoning, judgment based on probabilities and handling large amounts of information. Through the use of experimentation and modeling cognitive psychology elucidates the difference between humans and machines and helps us understand and improve human-computer interactions. Currently, in the Cognitive Science and Technology lab, we study human cognition and perception through behavioral and neurophysiological experiments combined with mathematical modeling. Our purpose is to understand human cognition in a way that can be used to develop artificial cognitive systems.
Human Computer Interaction
Human-computer interaction (HCI) concerns design, implementation, and evaluation of systems and processes involving human and computers. HCI is of mounting importance as human well-being and productivity increasingly rely on information processing and services. Our research in HCI concerns the interaction between human and intelligent systems with a specific focus on the signals they exchange including audio, video, and physiological signals.
Our vision is to design profound cognitive systems for augmented human cognition in real-life environments.
We have contributed real-life demonstrations of augmented cognition in the context of mobile applications, audio search engines, and scientific discovery.