Applying machine learning to brain imaging data
The human brain is a complex network, consisting of functionally interconnected regions whose coordinated effort gives rise to different functions. Understanding what these regions are, how they interact, and how this interaction forms a wide range of behavior has long been an essential question for human neuroscience. Neuroimaging techniques have provided a unique opportunity to tackle this question in a data-driven way. Advances in neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI), have allowed us to approximately measure the neural activity in the brain. However, fMRI data are not only massive in size but also spatially and temporally complex. One of the research directions in our group is to develop advanced machine learning techniques to study brain function and its link to behavior.
Amin Karbasi received his PhD from EPFL in Switzerland in 2012. He joined Yale in 2014 as an assistant professor. Amin is also a research scientist at Google working on the next generation of AI methods.