I am particularly interested in causal inference, uncertainty quantification, and enhancing the robustness of machine learning methods, especially for policy learning, evaluation, and healthcare applications. Before joining the StatML CDT, I worked as an AI Applied Scientist at Microsoft New England Research Center, where I gained two years of experience addressing real-world challenges. I hold a master's degree in Computational and Mathematical Engineering from Stanford University. My passion for causal inference drives my commitment to developing statistical and machine learning solutions that are both rigorous and applicable in critical domains like healthcare.

• Causal Inference
• Robust Statistics
• Uncertainty Quantification