Vikranth Rao Bejjanki

Vikranth Rao Bejjanki is an Associate Professor of Psychology at Hamilton College. His research focuses on the neural and computational mechanisms underlying human learning, employing methods such as psychophysics, computational modeling, and functional neuroimaging. He is affiliated with the Princeton Neuroscience Institute as a former postdoctoral researcher and maintains memberships in the Society for Neuroscience, Vision Sciences Society, Sigma Xi Scientific Honor Society, and the American Association for the Advancement of Science.

• Ph.D. in Brain and Cognitive Sciences, University of Rochester
• M.A. in Brain and Cognitive Sciences, University of Rochester
• B.A. in Cognitive Science, State University of New York at Buffalo
• B.S. in Computer Engineering, State University of New York at Buffalo

Bejjanki’s research explores how humans learn from experiences through probabilistic inference, cue integration, and neural plasticity. His work bridges experimental psychology with computational models to understand visual perception, spatial localization, and the impact of video game training on cognitive adaptability. He has contributed to journals like Nature Neuroscience, Proceedings of the National Academy of Sciences, and PLoS Computational Biology, often integrating neuroimaging and behavioral analysis to decode learning mechanisms.

His publications highlight interdisciplinary themes, merging neuroscience with artificial intelligence, cognitive modeling, and sensory processing. Key trends include the role of noise correlations in brain pattern classification, the development of perceptual templates through video games, and the application of Bayesian inference to spatial tasks.

Scientific Awards & Affiliations:

• Sigma Xi Scientific Honor Society
• Society for Neuroscience
• Vision Sciences Society
• American Association for the Advancement of Science

Bejjanki has taught courses such as Fundamentals of Human Neuroscience and Psychology and Neuroscience of Learning, reflecting his expertise in cognitive systems and neural mechanisms of learning. While his student advising record is not explicitly detailed, his collaborative research with institutions like Princeton underscores his commitment to advancing neuroscience through both theoretical and empirical approaches.