I completed my bachelor's degree in Biomedical Engineering at Marquette University in 2010. I then completed my master's degree, also in Biomedical Engineering, at Marquette University in 2011, under the direction of Dr. Scott A. Beardsley. The focus of my thesis was theoretical and computational neuroscience. Specifically, we designed and implemented large-scale attractor (recurrent) neural networks with physiological properties.

In 2011, I joined Dr. Reza Shadmehr's laboratory in the Department of Biomedical Engineering at Johns Hopkins University. In Dr. Shadmehr's laboratory, I studied motor learning and motor control using both human subjects and primate neurophysiology. After completeing my PhD in 2016, I stayed in the Shadmehr laboratory for a year as a post-doctoral fellow.

In 2018, I moved to Duke University to study in Dr. Stephen G. Lisberger's laboratory in the Department of Neurobiology. As a post-doctoral fellow, I study the neural basis of learning in pursuit eye movements in non-human primates.

Research interests

I am primarily interested in motor control and motor learning. This includes determining how healthy human subjects learn and re-learn motor perturbations as well as how humans with neurological issues accomplish the same tasks. I use behavioral tasks, such as moving a robotic handle and brain stimulation, to determine the neural substrates that are involved with the learning process.

Behavioral psychophysics

I study healthy human subjects as they make reaching movements. The robotic handle applies either a physical or visual perturbation (such as rotating the cursor) as I measure how the subject responds to the error. Using brain stimulation, imaging, or subjects with neurological issues, we can attempt to determine the brain region(s) responsible for this learning process.


Measuring human behavior allows us to determine the brain regions involved in motor learning at a macro scale. How does this relate to what is happening at the neuron level in the brain? I analyse data recorded from single neurons in the monkey during the simplest of all movements, saccades. The kinematics of these brief eye movements are quite simple, allowing us to relate neuron signals to the movement.

Computational modeling

The brain is an incredibly complex organ, with billions of neurons and trillions of synapses. Using mathematical models, we can discover and predict the rules that the brain uses to learn. We use models of all scales (e.g., a single neuron or the entire learning process) to understand how the brain predicts and adapts to its environment.