About Me

I am currently working as a Data Scientist for Microsoft under WebXT. In this role, my primarily responsibility is to maintain our deep learning pipelines for training, real-time inference, and offline inference. This consists of updating our pipelines as needed and onboarding our pipeline to additional services. My secondary responsibility is to implement and experiment with new and existing models. This consists of running training experiments, hyperparameter tuning, experimental implementations, error analysis, identifying sources of regressions, etc., in order to optimize our downstream performance.

I completed my masters in computer science (December 2021) at Western Washington University (WWU) advised by Brian Hutchinson. My graduate research work focused on the use of Generative Adversarial Networks to emulate Earth System Models. I’m working towards computationally effecient earth system emulators so that we may more easily understand our earth’s climate system. During my graduate study, I completed a master’s research internship at Pacific North West Laboratory where I worked in the few-shot learning domain.

I also completed my undergraduate in Math/Computer Science at WWU. This allowed me to lean more heavily into the mathematical side of computer science. My undergraduate research focused on the analysis of program performance in cluster environments. During my undergraduate study, I continued my research as a visiting student research assistant as Lawrence Berkeley National Laboratory where I designed a data pipeline for the analysis and visualization of hardware performance counters.