Bio

I am a postdoctoral research associate in the Department of Electrical Engineering at Stanford University, where I am jointly advised by Professors Krishna Shenoy (Stanford EE), Bill Newsome (Stanford Neurobiology), and David Sussillo (Google Brain). My research interests are at the intersection of machine learning and basic systems neuroscience. My current projects are focused on developing deep learning techniques for understanding population-level neural computations underlying decision making and motor control.

I completed my PhD at Carnegie Mellon University in 2015, where I was jointly advised by Professors Byron Yu and Steve Chase. My dissertation, titled "Interpreting neural population activity during sensorimotor control," was awarded the A.G. Milnes Best Thesis Award by the Department of Electrical & Computer Engineering.

Contact information:

CV available upon request.

Research interests

Machine learning for neural data analysis

Probabilistic latent variable models, deep neural networks, and other optimization-based frameworks. How can we use tools like these to extract meaning from recordings of large neural populations? What new statistical techniques are needed to test hypotheses about neural computation?

Decision making and motor control

How do populations of neurons generate decisions based on sensory evidence, and how do these decision-making processes flexibly interlace with the action selection and execution processes that they inform?

Population-level changes in neural activity during learning

How do populations of neurons change their joint patterns of activity during learning? What are the limitations of these changes, what are the timescales of those limitations, and how can those limitations be overcome to facilitate faster learning to higher levels of proficiency?

Brain-machine Interfaces (BMIs)

By translating intracortical recordings into signals for driving prosthetic devices, BMIs offer restored movement and communication for those with spinal cord injuries, neurodegenerative diseases or limb amputations. These systems can also serve as a powerful testbed for basic neuroscientific discovery.