Hi! Welcome to my personal website. I’m a statistician turned computational biologist. I finished my PhD in Genome Sciences at the University of Washington in 2026, working with Erick Matsen at Fred Hutch, and before that I studied statistics and computational modeling at Virginia Tech with Leah Johnson and Allison Tegge.
Most of my work lives between a biological question and the machinery needed to answer it honestly. I like problems where the hard part isn’t fitting a model off of the shelf, but rather it’s knowing whether the number that comes out means anything. My dissertation is a fair example: I essentially developed different models and tools for studying how changes to the sequence of viral proteins impacts its “fitness”. While fitness takes on different forms throughout my PhD work, the motivation remained: develop a model that is bowth predictive and interpretable.
In torchdms, I developed a software package that fits biophysically-inspired neural networks to infer mutational effects and fitness landscapes of proteins sampled by Deep Mutational Scanning (DMS) experiments. In polyclonal, I helped the Bloom lab focus this approach on viral escape from polyclonal antibodies. And in antigen-prime, the objective shifted to the macro: can we simulate a model of an evolving fitness landscape to benchmark other models and tools that are typically trained on noisy or incomoplete surveillance data. These reseach experiences have shaped me to think about what a model actually learns about a sequence, and how much of an apparent improvement is the model rather than a leak in genetic or evolutionary information.
I’ve also been interested in statistical models and machine learning since my early years of college. I remember learning about neural networks near the end of my time as an undergrad, and focused a lot of my PhD work on getting hands-on experience in the space (torchdms primarily). However, I quickly learned about attention and transformers during my first year of grad school, and have become captivated by the incredible progress in the field of large-language models (LLMs). With my past experiences and passions for machine learning and biology, the direction I’m looking to head in for my next chapter is applying large language models to biology, with the evaluation taken as seriously as the architecture.
Away from a screen I powerlift and play more video games than I’ll admit to. Everything I’ve built is on the projects page. Any questions about my past, present, or plans for the future? Please reach out! The links above are the best way to reach me.