I am a third year PhD student in Computing and Data Sciences at Boston University (BU), working with Jonathan Huggins and Michael Dietze . I am broadly interested in uncertainty quantification, Bayesian inverse problems, data assimilation, and spatiotemporal statistics, with the goal of developing new methodologies for environmental and ecological applications. My current work involves developing Bayesian methods for calibrating expensive mechanistic models of the terrestrial carbon cycle.

Beyond my research, I am a trainee in BU URBAN, a graduate program in urban biogeoscience and environmental health. I am also an officer in the BU chapter of the American Statistical Association, and previously co-organized the Environment and Climate working group within the Mechanism Design for Social Good (MD4SG) research intitiative.

Prior to BU, I worked as a research assistant at the Federal Reserve Bank of Boston. I received my B.S. in Quantitative Economics from Tufts University in 2018.

Teaching

• Stochastic Methods for Algorithms, Fall 2023 (TF)

Selected Coursework

• Multiscale Methods for Stochastic Processes and Differential Equations
• Numerical Methods for Stochastic Modeling and Inference (MIT)
• Stochastic Methods for Algorithms
• Advanced Optimization Algorithms
• Bayesian Statistics
• Statistical Machine Learning
• Generalized Linear Models
• Probability Theory I/II
• Mathematical Statistics