Duke University

Ph.D. Candidate, Durham, NC, Aug 2021 - Current

  • Developed interpretable method for estimating dynamic treatment regimes that reduced the probability of an adverse medical event by over 20 percentage points when operationalized on a real-world ICU dataset.
  • Engineered new method for large scale, computationally inexpensive, and interpretable causal inference that is over 100 times faster than existing benchmarks.

Meta

Research Scientist Intern, Seattle, WA, May 2024 - Aug 2024

  • Wrote an internal technical report detailing a domain-adapted model-based approach for offline counterfactual evaluation of ranking models. Work accepted as short paper to the 2024 Causality, Counterfactuals, & Sequential Decision-Making workshop at RecSys and the 2024 Conference on Digital Experimentation @ MIT.
  • Provided empirical and theoretical results showing the settings under which different variants of the proposed approach are better than existing benchmarks.

Optum

Data Scientist, Eden Prairie, MN, Jun 2019 - Jul 2021

  • Built and deployed a multimodal time-series model assessing the risk of every development team’s proposed application update that remains used at the company to present day.
  • Detected suspicious activity as lead python programmer for fraud investigation in the payout of the U.S. Government’s COVID-19 HHS CARES Act Provider Relief Fund facilitated by Optum.
  • Automated development team’s Jenkins environment by creating customizable Jenkinsfiles to deploy machine learning models to Kubernetes.
  • Summarized bank transaction and account activity logs into feature vectors using PySpark, and used Spark’s MLlib library to detect irregular account activity.

Data Scientist Intern, Eden Prairie, MN, Jun 2018 - Aug 2018

Big Data Intern, Eden Prairie, MN, Jun 2017 - Aug 2017