My research interests lie within the broad area of trustworthy machine learning. More specifically, I focus on improving the interpretability, fairness, privacy, and explanable capabilities of different kinds of ML models, including generative models and other pre-trained models. My research addresses the following fundamental questions pertaining to human and algorithmic decision-making:

1. How can we build interpretable and accurate models to personalize health recomendations in human decision-making?
2. How do we train and evaluate models when faced with missing counterfactuals?

These questions have far-reaching implications in domains involving high-stakes decisions such as health care, policy, law, and business.