CV

Education

• B.S. in Statistics, Carnegie Mellon University, 2021
• M.S. in Statistical Science, Duke University, 2023
• Ph.D in Computer Science, Dartmouth College, 2028 (expected)

Work experience

• Summer 2022: Research Assistant
  • Atriumn Health
  • Duties included: Implement averaging images algorithm on patients Hernia CT image and build a 7 layered mixed value CNN to successfully identify symptomatic and non-symptomatic hernia with .74 AUC. Build a 5 layer CNN with a pre-trained 4 layer FNN and learnable linear interpolation to predict on recurrence rate of Hernia using pre-operative CT images, arguing that some basic demographic information is more meaningful than the CT scan itself when predicting the recurrence rate.
  • Supervisor: Todd Heniford
• Summer 2021: Machine Learning Intern
  • Blep Analytics
  • Duties included: Using public API to gather the historical daily price, open value, and the trading volume of Bitcoin Performed ACF, PACF, and Ljung-Box test to determine the suitable model for the volatility of Bitcoin daily log return. Conducted Model comparison and diagnostics to choose the suitable parameters for the ARMA-GARCH model Used the suitable ARMA-GARCH model to make prediction on the future Bitcoin volatility Confirm the prediction on volatility with the Long Short-Term Memory Model Collaborated with colleagues to finish a report on the volatility of Bitcoin daily log return
  • Supervisor: Allie Zhang

Publications

OC-008 NEURAL NETWORKS PREDICTING HERNIA RECURRENCE AFTER ABDOMINAL WALL RECONSTRUCTION (AWR) PERFORM BETTER WITHOUT PREOPERATIVE IMAGING

This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations