Computational and Network Modeling of Neuroimaging Data

Dr. Kendrick Kay is an Assistant Professor at the Center for Magnetic Resonance Research at the University of Minnesota. He received a BA in Philosophy from Harvard University in 2002, a PhD in Psychology from the University of California, Berkeley in 2009, and completed a postdoc at Stanford University in 2013. Research in his Computational Visual Neuroscience lab (http://cvnlab.net) focuses on understanding the computational principles by which the brain processes visual information, and lies at the intersection of cognitive neuroscience, functional magnetic resonance imaging methods, and computational modeling. The lab is highly collaborative, working with diverse groups around the world, with the goal of developing broad integrative insights into brain and behavior. Dr. Kay is a co-founder of the conference, Cognitive Computational Neuroscience, which seeks to bridge researchers across cognitive science, artificial intelligence, and neuroscience. Dr. Kay has published more than 50 scientific articles in top journals including Nature, Nature Neuroscience, Neuron, Nature Methods, eLife, PNAS, Current Biology, Journal of Neuroscience, and NeuroImage. Tools and resources (e.g., experiments, data, code) from the lab's research are made freely available, including the recently completed 7T fMRI Natural Scenes Dataset.

1. Statistical modeling: Harnessing uncertainty and variation in neuroimaging data
2. Sensory modeling: Understanding computation in sensory systems through image-computable models
3. Cognitive modeling: Joint models use cognitive theory to understand brain activations
4. Network modeling: The explanatory power of activity flow models of brain function
5. Biophysical modeling: An approach for understanding the physiological fingerprint of the BOLD fMRI signal
6. Biophysical modeling: Multicompartment biophysical models for brain tissue microstructure imaging
7. Dynamic brain network models: How interactions in the structural connectome shape brain dynamics
8. Neural graph modelling
9. Machine learning and neuroimaging: Understanding the human brain in health and disease
10. Decoding models: From brain representation to machine interfaces
11. Normative modeling for clinical neuroscience