CV

CV

Education

• M.S. in Computer Science (AI/ML), Brown University, 2025 (expected)
  
• B.S. in Computer Science, Arizona State University, 2022
• B.S. in Mathematics (Statistics), Arizona State University, 2022
  

Relevant Coursework

• Intro to NLP, Machine Learning Theory, Reinforcement Learning, Data Visualization, Probability & Statistics, Regression Analysis, Deep Learning

Work Experience

• FM Global – Data Science Intern
  • June 2024 – Current
  • Developed and optimized a Random Forest model to predict and impute missing variables critical for internal risk models.
  • Engineered and implemented a scalable data pipeline for automating the imputation process across diverse data contracts and peril loss scenarios.
  • Collaborated on a proof-of-concept for an Agentic RAG system using Azure OpenAI services, exploring advanced ML techniques for an intelligent tutoring assistant.
• Dewberry – Data Science Intern
  • Jan 2024 – May 2024
  • Developed Power BI dashboards for PM auditing and HR applicant/job flow tracking to facilitate data-driven decision-making.
  • Conducted research on generative AI tools, including Microsoft Copilot, and presented strategic recommendations to senior executives.
• Collective Logic Lab – Undergraduate Researcher
  • July 2022 – Dec 2022
  • Collaborated on a collective decision-making model for bee foraging activities, analyzing data and developing a high-performing XGBoost model with 82% accuracy in predicting foraging events.

Skills

• Languages: Python, Java, C/C++, R, JavaScript, SAS, SQL
• Tools & Frameworks: PostgreSQL, D3.js, Vue.js, Power BI, Tableau, Git, GitHub, Linux/Unix, AWS, VS Code, Databricks
• Libraries & Technologies: NumPy, Pandas, Seaborn, Tidyverse, Matplotlib, Dplyr, Keras, Scikit-Learn, TensorFlow, PyTorch
• Skills: A/B Testing, Data Analysis, Machine Learning, Data Visualization

Projects

• Book Recommender

  Python, FAISS, Flask, Pandas, BeautifulSoup

  • Spring 2024
  • Engineered a book recommender system by scraping Goodreads data and transforming book descriptions into embeddings using Hugging Face’s sentence-transformers.
  • Built and indexed embeddings with the FAISS library for efficient Approximate Nearest Neighbor (ANN) search, significantly speeding up the retrieval of similar books.
  • Created a web interface for the recommender system, enabling real-time, personalized book suggestions.

• Honors Thesis

  Python, Scikit-Learn, NumPy, Pandas

  • Fall 2022
  • Conducted independent research to predict wildfire sizes in the U.S. using regression and classification models, achieving a 31% accuracy in predicting fires within the correct class.

• AutoCat – Class Project

  JavaScript, PostgreSQL, Vue.js

  • Fall 2021
  • Developed AutoCat, an interactive car catalog website, implementing the project’s database structure and facilitating user-friendly car purchasing.

Publications / Achievements

• Validating new Automated Computer Vision workflows to traditional Automated Machine Learning
  • Benchmark paper comparing the AutoML tool TPOT to SEE-Insight tool (SEE-Classify).
  • DOI: https://doi.org/10.1145/3491418.3535174
• SolutionsX Hackathon
  • 3rd place Winner by Bank of the West, Oct 2019
• Hacks for Humanity Hackathon
  • 1st place Winner by Project Humanities, Oct 2019