Jay Hennig is an assistant professor and McNair Scholar in the Department of Neuroscience at the Baylor College of Medicine. He earned his undergraduate degree in pure mathematics from the University of Texas at Austin (2011), and then worked as a mathematics consultant and software engineer in Melbourne, Australia. He completed his Ph.D. in neural computation and machine learning from Carnegie Mellon University (2021), and was later a postdoctoral fellow in the Department of Psychology at Harvard University.

His lab at Baylor is focused on understanding how learning happens in the brain in terms of changes in the brain's neural activity. This interest spans many different types of learning, such as motor learning and reinforcement learning. To understand these processes, the Hennig lab uses ideas and tools from machine learning, artificial intelligence, and statistics to reason about changes in neural population activity during learning. In general, his lab's research merges three perspectives:

- theory (e.g., normative models: “How should someone learn this task?”)
- machine learning (e.g., artificial agents trained with reinforcement learning)
- neural data analysis (e.g., statistical models of high-dimensional neural activity)

Some of their past and current work considers learning in the following contexts:
- neural control of movement in motor cortex, using brain-computer interfaces (BCI)
- reinforcement learning in the dopamine system, using recurrent neural network (RNN) models

Moving forward, the Hennig lab has two primary research interests: meta-learning (“learning to learn”), and the emergence of probabilistic representations in the brain.