My research sits at the intersection of glaciology, climate science, and statistical methods. I’m interested in understanding uncertainty in ice sheet and alpine glacier models, and in translating that understanding into insights for adaptation and risk management.
Key methods include: statistical generation and emulation, large ensemble simulations, uncertainty quantification, and probabilistic hazard assessment.
Selected Research Themes
Ice Sheet Modeling and Uncertainty
I work on statistical methods to characterize and reduce uncertainty in ice sheet models. This includes developing emulators for ocean forcing, running large ensemble simulations, and quantifying how different sources of uncertainty propagate through to sea-level projections.
Related projects: The Antarctic Ice Sheet Large Ensemble, Statistical Generation of Ocean Forcing
Compound Coastal Flooding
Coastal communities face compound flooding from storm surge, rainfall, and sea-level rise acting simultaneously. I’m developing machine learning emulators of physics-based flood models to enable faster exploration of adaptation options under uncertainty.
Related projects: Emulation of Compound Coastal Flooding
Mountain Hydrology and Climate
Mountain regions—particularly the Himalayas—face unique challenges in water security. I’m involved in projects studying spring systems, glacier mass balance, and climate resilience in agricultural systems.
Related projects: Citizen Science Springs Observatory, Climate‑Resilient Seed Systems, Learning Geodetic Mass Balance
Publications
Journal Articles
- Muruganandham, S., A.A. Robel, M. Hoffman, S. Price (2023). Statistical Generation of Ocean Forcing with Spatiotemporal Variability for Ice Sheet Models. Computing in Science & Engineering (IEEE). Open Access
(Full list in CV)
Conference Abstracts
(Selected abstracts in CV)