Meta:

Machine Learning Intern  May 2026 - Aug 2026

• Built an LLM-based advertiser simulation framework to model budget-allocation decisions under limited direct competitor observability, using advertiser trajectories and personas to capture responses to relative positioning.
• Developed a counterfactual evaluation framework to estimate advertiser responses to alternative signal strategies when historical data capture only realized decisions.
• Designed an interpretable LLM-augmented reinforcement learning method over fixed-structure decision-tree policies to support policy optimization under interpretability and low-latency serving requirements.
• Improved simulation throughput and model-serving efficiency through parallelization and caching.

Akamai Technologies:

Research Intern (Mapping)  Jun 2025 - Aug 2025

• Studied traffic patterns of content delivery networks using stochastic modeling.
• Implemented a simulation pipeline for machine utilization under various DNSP load-balancing policies.
• Designed and evaluated load-balancing policies to improve tail latency for end users.

Aetna at CVS Health:

Data Scientist  Jun 2021 - Jul 2022

• Built a distributed Monte Carlo simulation pipeline for member-disenrollment estimation, completing 100K+ simulations on datasets with millions of rows within one hour through parallelization and vectorization.
• Developed optimization and machine-learning methods for campaign experimental design, risk-cohort identification, and member churn analysis using quadratic programming, Dask, Hive, and supervised learning.
• Built automated data, modeling, and reporting pipelines and developed NLP analyses of sales-call transcripts.

Institute for Health Metrics and Evaluation:

Research Assistant  Feb 2019 - Sep 2019

• Developed statistical and simulation methods, including particle MCMC, for modeling travel flows and improved data-processing and storage pipelines for repeated simulation studies.