Research Areas

Financial Data Science

Applying statistical learning to large-scale financial and customer data: transaction records, insurance portfolios, and market data. Recent work includes recommender systems for insurance products and tracking customer risk aversion from observed behavior.

Machine Learning for Finance

Designing and evaluating machine-learning models for prediction and decision support in financial services, with attention to interpretability and to the way model output feeds into actual decisions.

Generative Models for Sequential Data

Evaluating synthetic sequential tabular data, such as time series produced by generative models, with an emphasis on whether temporal structure is preserved. Applications include data augmentation and privacy-preserving data sharing in finance.

Risk and Volatility Modeling

Stochastic volatility models and early-warning indicators for market stress.

Ongoing Research