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Chris is a graduate student in the Psychology department. His research looks at decision making biases associated with anxiety and depression. He uses a computational approach, leveraging models from Bayesian statistics and reinforcement learning, to study these cognitive biases. He is also more broadly interested in the application of machine learning to cognitive neuroscience.



By Appointment

Consultations on Python, machine learning, bayesian modeling, linear models, mixed-effects models, non-parametric tests, and reinforcement learning.