Research Interests
My research sits at the intersection of statistics, machine learning, and geospatial data science. Coming from a background in applied mathematics and data science, I am drawn to problems where rigorous quantitative methods can be brought to bear on geospatial and geotechnical challenges that are traditionally approached through domain-specific physical modeling alone. My current work focuses on using satellite-based remote sensing, particularly InSAR (Interferometric Synthetic Aperture Radar), to monitor and characterize ground surface deformation, including subsidence and uplift associated with subsurface fluid extraction and geothermal energy operations. I am especially interested in how time-series statistical methods and machine learning can improve the detection, prediction, and interpretation of deformation signals, connecting noisy remote sensing observations to the underlying geotechnical processes that drive them. More broadly, I aim to contribute to the growing field of GeoAI by translating data science techniques into tools that support reservoir management, geohazard monitoring, and infrastructure resilience.
Education
- Ph.D. Candidate in Civil Engineering, University of Waterloo, May 2025 - present
- Master of Information in Data Science, University of Toronto, Sep 2022 - Aug 2024
- B.Sc./B.A. in Statistics and Applied Mathematics, University of Waterloo, Sep 2026 - April 2020