Research

At Dartmouth, my research centers on using data-driven methods and computational tools to improve systems and solve real-world problems. I focus on applying analytical thinking and technology to enhance decision-making across complex environments.

Data Science for Optimization of Healthcare Operations

First Year Research in Engineering Experience

My first year at Dartmouth, I conducted research with Operations and Systems Engineering Professor Vikrant Vaze on optimizing healthcare access through simulation and data analysis. I used Python-based modeling to study patient flow, reduce wait times, and evaluate appointment scheduling strategies.

Final Report

This graph models the daily arrival of new IBD patients using a Poisson distribution, reflecting the random, low-frequency nature of patient visits. Similar graphs were generated for follow-up patients and other centers, including Liver, Pancreas, and Motility. Based on 293 days of data, most days saw between 0 and 3 new patients, closely matching the Poisson curve.

To explore patient wait times, I used SimPy, a Python-based discrete event simulation tool, to model appointment scheduling. An initial simulation assigned ten patients to two providers with 30-minute slots, tracking randomized arrivals and wait times through the system.

This table compares average wait times and their variances across different appointment scenarios for multiple patient groups. Both the 30-30 and 40-20 scenarios reduced average wait times and standard deviations compared to the baseline, with the 40-20 scenario showing the greatest overall improvement in patient scheduling efficiency.