Jay Hennig

Jay Hennig holds dual academic roles: he is an Assistant Professor of Neuroscience at Baylor College of Medicine and an Adjunct Assistant Professor of Electrical and Computer Engineering at Rice University. His work bridges neuroscience and engineering, with a focus on theoretical and computational approaches to understanding neural learning mechanisms.

Education:

• Bachelor’s Degree in Pure Mathematics, University of Texas at Austin (2011)
• Ph.D. in Neural Computation and Machine Learning, Carnegie Mellon University (2021)
• Postdoctoral Fellow in Psychology, Harvard University

Research Interests:

Hennig’s lab investigates how learning manifests in neural activity through three core lenses:

• Normative models: Exploring theoretical frameworks for optimal learning strategies
• Machine learning applications: Using reinforcement learning in artificial agents to mirror biological systems
• Neural data analysis: Advanced statistical modeling of high-dimensional neural datasets

Current projects include studying motor cortex control via brain-computer interfaces (BCI) and dopamine system dynamics using recurrent neural network (RNN) models. Future research emphasizes meta-learning and the brain’s probabilistic representation formation.

Awards:

• McNair Scholar at Baylor College of Medicine

Advising & Grants:

No advisees or grants are explicitly mentioned in the provided information.

Labs & Teams:

His primary lab at Baylor College of Medicine employs interdisciplinary methods to dissect neural mechanisms of learning, merging computational theory with experimental neuroscience.