معرفی
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.
