I recently received my PhD in Neuroscience at the Icahn School of Medicine at Mount Sinai working with Peter Rudebeck. I'm now a postdoc in his lab performing high-channel count electrophysiology in awake, behaving macaques. We are interested in understanding how interactions between amygdala, frontal cortex, and striatum guide reward learning. Alongside standard statistical analysis techniques, I'm also working to apply CURBD and other novel RNN-based techniques to further interrogate inter-area interactions during this task.

I received my BS in Neuroscience and my BA in Latin from Mercer University in May, 2019. At Mercer, I worked in the lab of Dr. Katie Northcutt for three years, studying the neural basis of play behavior in rats. I also had the incredible opportunity to study abroad in both Greece and Sicily as part of my Latin training, thanks to the generous support of the Stamps Family Charitable Foundation.

At Sinai, I received my PhD for work in the Rudebeck Lab, where my projects were focused around comparing the nature of interactions between amygdala and frontal cortex across multiple species. I'm interested in the evolution of prefrontal cortex in particular, and how large-scale changes in brain structure throughout evolution might be reflected in both neuroanatomy and neuronal physiology. I'm also passionate about applying modern data science techniques to neuroscience data.

For my thesis, I primarily spent my time applying novel sequencing-based neuroanatomy techniques to understand the structural basis of amygdala-frontal brain networks. We've successfully applied this to rhesus macaques (out in Neuron now!), and we're directly comparing those results to complimentary experiments in mice now. Then, primarily using Python (as well as other statistical programming platforms), I'm comparing the statistical structure of neural spiking activity from a number of model species commonly used in neuroscience.