Welcome to my research site!

I am currently a Ph.D. student in statistics (cohort of 2021) and machine learning at Carnegie Mellon University, a joint degree program. Before joining CMU, I worked as a data scientist in Washington, D.C. Simultaneously, I completed a master’s degree in mathematics and statistics at Georgetown University. My earlier background is in international development.

My primary area of interest is uncertainty quantification (UQ) on temporal data using black-box methods such as recurrent neural networks and transformers. I am advised by Arun Kuchibhotla, whose expertise includes forecasting methods and prediction intervals for temporal and online datasets. My machine learning co-mentor is Jeff Schneider, who works on applications in nuclear fusion and reinforcement learning.

Additional topics that interest me include:

• Time-series forecasting and online prediction—particularly with black-box models such as neural networks
• Stochastic Gradient Descent (SGD)-based Central Limit Theorems
• Applications to international development—such as natural disaster management using drone imagery