Research & Professional Experience

Alongside my dissertation research, I have worked as a quantitative researcher in industry and on sponsored research at Georgia Tech, building statistical and machine-learning systems that shipped to real users.

Industry

• Summer 2025

  Quant PhD Intern, Quantitative Risk Analytics

  Bloomberg L.P. · New York, USA

  Built a Kalman-filter hazard-curve model for pricing credit instruments from latent Nelson–Siegel factors and macroeconomic covariates, integrated into Bloomberg Terminal's Credit VaR engine; implemented a credit-rating-migration module using macro-conditioned multinomial logit and intensity-based models to simulate obligor- and index-level rating paths.

• Summer 2024

  Research Scientist Intern, Data Science & AI Research

  AT&T Labs, Inc. · New Jersey, USA

  Developed a framework integrating large language models with multivariate functional data analysis for sparse longitudinal customer-journey text, now an arXiv preprint; shipped a production anomaly-detection application (Emotional AI) for behavioral interventions.

• 2019 – 2021

  Senior Quantitative Analyst, Wholesale Credit Risk Modeling

  Australia and New Zealand Banking Group (ANZ) · Bengaluru, India

  Built the Financial Ratio Surveillance Tool (FRST), a production R Shiny stress-testing application forecasting default probabilities and assessing the predictive power of financial ratios; led the bank's statistical COVID-19 impact assessment across commercial portfolios for APRA regulatory requirements.

• Summer 2018

  Research Intern, Data Science (International Pricing & Demand Forecasting)

  Walmart Labs · Bengaluru, India

  Built a two-stage price-elasticity model to forecast category-level sales from item-, region-, and covariate-specific price elasticities: k-means clustering, then an ensemble of ridge, lasso, and elastic-net models fit within each cluster, reducing mean absolute percentage error by 42.4% relative to the incumbent method.

Sponsored Research

• Jan – Sep 2026

  Graduate Research Assistant, Georgia DOT Sponsored Research

  Georgia Institute of Technology · Atlanta, GA, USA

  Developed a statistical framework establishing how many valid days a traffic-monitoring station needs in a month with incomplete data before its monthly average can be published, and applied it across Georgia's network of 233 continuous count stations. Delivered to transportation planners as the Data-Sufficiency Explorer, an interactive dashboard. PI: Prof. Xiaoming Huo.

  Code