My parents fostered my love of math, science and technology from a very young age – my dad by trying to teach me mathematical concepts when I was far too young, and my mum by showing me at the age of 5 that the answer to any question I could dream of could be found on a magical website called Google. My decision to pursue academic research may have subconsciously been a pushback: seeking questions that Google had no answer to and finding them myself. It looks like I may have had a rebellious phase after all!
Attempting to cement into place my identity as a geek, my parents sent me off to a bluntly named (but dear to my heart) middle and high school, the Advanced Math and Science Academy. After graduating as valedictorian, I attended Brown University where I completed an undergraduate thesis in the lab of John Donoghue before pursuing my PhD in Neural Computation at Carnegie Mellon University with Aryn Gittis and Jonathan Rubin, funded in part through an individual NIH F-31 fellowship award.
Throughout undergrad and grad school, I’ve worked on a wide variety of computationally focused projects, from uncovering unexpected disease correlations using over 100 million health records (back when the term “big data” wasn’t quite so cliche yet) to analyzing dense neural recordings to predict motor errors before they occur. My work in my PhD has focused on understanding the mechanisms behind pathological brain waves in Parkinson’s disease and how they relate to and predict motor symptoms. In addition to performing the experiments, I implemented and adapted a variety of statistical modeling and signal processing techniques to make sense of my data. I used these tools to reliably detect brain waves amidst high levels of neural noise, demonstrated how the strength of these brain waves predicts Parkinsonism better than traditional neural measures, and elucidated the timeline and pathway by which these oscillations propagate through the brain.
In my research projects, I’ve seen my role as a bridge between the analytical and the experimental, closing the gap between these two often disparate worlds in neuroscience and facilitating communication and collaboration to bring the strengths of both approaches to the groups I’ve worked in. A computational model fails to provide insight when it isn’t grounded in the realities of the system being modeled, so during my PhD, I’ve strived to deeply engross myself in the experimental realities of Parkinson’s disease research so I can ask and answer the right questions with my computational skills.
As I complete my PhD, I am seeking roles not only in neuroscience but in adjacent fields where my analytical expertise can provide value. Solving problems at the forefront of neuroscience requires a deep knowledge of the necessary tools, the ability to independently acquire that knowledge, and the creativity to modify and develop those tools for the particular question at hand. As such, I’m interested in projects where the tenacity and analytical toolbox I’ve developed during my PhD can be used to solve challenging problems with data-driven solutions.
Outside of my career, I like to focus my creativity on more analog pursuits. I’ve played guitar nearly all my life, but I think I peaked around the age of 7. I perform improv comedy, and I play and design board games that sometimes seem needlessly complex. I also have albinism – the red eyes are a common misconception, but the blindingly blond hair is not.