• Trained and deployed a ResNet neural network to use aerial imagery to identify damage to Travelers-insured properties after severe wind events
• Used large language models (LLMs) to extract key information from, and transformer models for multi-label classification of, legal complaints associated with general liability claims
• Managed an intern, provided mentorship to data scientists, and chaired a committee to improve a leadership development program

• Led the migration of a business-critical modeling code base from SAS to Python and from on-prem to AWS. Technical requirements included the ability to handle over 100M auto policies while improving run-time.
• Evaluated the feasibility of using tree-based models trained on features derived from aerial imagery to improve price segmentation in an auto insurance product

• Used double generalized linear models (DGLMs) to determine appropriate pricing factors for millions of personal insurance policies.
• Trained an ensemble model to identify cross-selling opportunities for existing customers as part of a data science competition, coming in 2nd place out of 30 teams.

• Refactored, added functionality to, and improved run-time (up to 2x) of an Oracle PL/SQL code base underlying a CRM application which serves dozens of non-profit clients
• Trained models to improve the response rate to a client’s direct mail campaign and to identify customers most likely to churn. The direct mail model improved response rate by over 20%.
• Worked with client-facing teams to identify user needs and to diagnose and fix bugs in the application

• Conducted analyses and created visualizations using Python, R, SQL, and Tableau in projects related to climate change, hydraulic fracturing, drinking water contaminants, greenhouse gas emissions, and home energy use
• Created a web app incorporating Tableau dashboards for the Army Corps of Engineers to prioritize assets most vulnerable to climate change
• Developed database applications in support of a variety of EPA programs
• Previous roles included Research Analyst, Analyst, and Senior Analyst

Certificate in Statistical and Computational Data Science. Coursework included regression, Bayesian statistics, machine learning, neural networks, natural language processing (NLP), survival analysis, design of experiments, and visualization

General Honors, Departmental Honors, Phi Beta Kappa Society. Publications in ACS Omega and Biophysical Journal

• Created a web application using Streamlit to classify photos of dogs into one of 150 AKC-recognized breeds
• Used fastai to fine-tune a ResNet convolutional neural network on 20,000 images of dogs scraped from the internet, achieving a test set accuracy of 53.8%

• Developed a high-level Python API to greatly simplify the process of downloading public comments from Regulations.gov. The project has attained 20 stars on GitHub as of July 2024.
• Abstracted away the complex pagination scheme and layers of requests while also handling API request limits