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 recurrent neural networks, with applications to nuclear fusion and natural disaster management. This is the subject of my research in summer 2024, which is funded by the AI Institute for Societal Decision Making (AI-SDM). 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.

Additional topics that interest me include:

• Recurrent neural network (RNN) architectures and implementations—especially with a probabilistic outcome
• Time-series forecasting and online prediction
• Reinforcement learning
• Applications to international development—such as natural disaster management using drone imagery

Personal interests

• I love painting, especially portraits. I have taken art classes from an early age at the Maine College of Art and from various artists in Santa Fe, New Mexico.
• I like to run. Pittsburgh has excellent hills, which is one reason I chose CMU. I also sometimes play pick-up soccer.
• I love raising pet chickens. I grew up on a farm in Maine and I sold eggs to neighbors. I also had a pet chicken in Mozambique.