Arish is interested in uncovering information processing principles underlying face perception in the brain. Specifically, he seeks to understand the neural code for faces in terms of what (features of a face), where (areas of the brain), when (e.g. how long after we see a face does a particular region of the brain get involved?) and how (e.g. is the neural code for faces modulated by real world social situations?). He develops and uses machine learning techniques to analyze intracranial EEG recordings of human brain activity during visual neuroscience experiments to answer these questions.

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Mary Kate is a Data Science master’s student in the University of Pittsburgh’s School of Computing and Information and a Research Specialist in the Laboratory of Cognitive Neurodynamics. Her work applies computational methods to human intracranial EEG data, with a focus on how affective information is represented in neural signals. She earned her BS in Natural Sciences from the University of Pittsburgh and previously worked as a Clinical Laboratory Scientist before transitioning into academic neuroscience. More broadly, she is interested in what neural signals encode, how they can be anchored to behavioral or experimental ground truth, and how these assumptions shape downstream analysis.

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Mo is a Bioengineering PhD student at the University of Pittsburgh. Mo’s research interests lay in psychology, neuroscience, statistics, and machine learning. She’s broadly interested in studying human visual perception and higher level cognition problems. She has experience working on understanding face perception and its mechanisms with deep learning/reinforcement learning techniques. Mo is also interested in using neuroimaging methods to study the structure and function of human brain. Before coming to The University of Pittsburgh, Mo obtained her master’s degree in statistics from Columbia University and bachelor’s degree in psychology and mathematics from Connecticut College.

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