I build resource-constrained machine learning (ML) systems for science – in vision, timeseries, and text domains – fusing approaches from statistical ML and CS systems.

I am currently a final-year PhD candidate advised by John Cunningham and working closely with Liam Paninski. I am also a part-time NLP researcher at MosaicML/Databricks, under Jonathan Frankle, where I work on LLM parameter-efficient finetuning, evaluation, and data, with an emphasis on code generation.

In my primary PhD work, I develop semi-supervised computer vision systems for tracking animals in videos, reducing the amount of labeled data needed for the task and improving generalization. Our package lightning-pose (bioRxiv, 2023; under review) is widely used in science and industry. I have also tackled this problem via probabilistic representation learning (NeurIPS, 2019, PLOS Comp. Biol., 2021), 3D vision, and physical simulation (NeurIPS DiffCVGP, 2020). In a second line of work, I focus on the computational efficiency and inductive biases of Gaussian processes (ICML, 2021). My ongoing NLP work at MosaicML addresses questions of knowledge acquisition and extinction and its interaction with parameter-efficient finetuning methods (more soon!).

Throughout my PhD, I collaborated closely with Lightning AI, named a Lightning Ambassador, and a featured developer in their first DevCon, June 2022.

Here is my CV.