01
Coastal AI & digital twins
Physics-informed learning, probabilistic surrogates, data assimilation, and fast emulators for coastal water levels and flooding.
Associate Research Scholar · Princeton University
Coastal hazards.
Intelligent models.
Resilient futures.
I develop computational and AI-enabled approaches at the coast–AI interface, including physics-informed machine learning and probabilistic emulation, for compound flooding, tropical-cyclone hazards, coastal water levels, and climate extremes, connecting physical understanding with fast, decision-relevant climate-risk modeling.
Research footprintFrom fundamental hazard science to computational tools.
Peer-reviewed articles28Published and accepted
Citations470+Google Scholar
h-index10Google Scholar
Media coverage150+Coverage mentions
Highlights
Honors, leadership & professional recognition
Selected honors, leadership roles, professional service, and research achievements across coastal and ocean engineering.
Externally Funded Grants
Experience securing and developing externally funded proposals for NSF, NOAA, and DOE calls.
ASCE Manual Co-Authorship
Co-author of Chapter 5 of the ASCE manual on compound flooding.
Ph.D. and Master’s Graduate Excellence Awards
National recognitions for academic excellence in Ocean Engineering.
ASCE Task Committee Appointment
Appointed member of the ASCE Task Committee on Compound Flooding.
Editorship and Leadership
Lead guest editor for Advances in Water Resources and primary chair of AGU Coastal Hydrology sessions.
Research focus
Engineering insight for a changing coastal climate.
My work spans physical modeling, machine learning, uncertainty, and risk. The common thread is making complex coastal-hazard information more predictive, computationally efficient, and usable.
02
Compound flooding
Interactions among storm surge, rainfall, tides, river discharge, and infrastructure in multi-driver flood systems.
03
Tropical cyclones & climate extremes
Rapid intensification, marine heatwaves, destructive potential, and climate-sensitive coastal hazard behavior.
04
Risk, uncertainty & resilience
Probabilistic design, nonstationary extremes, hazard communication, nature-based solutions, and decision support.
Selected work
All research projects →
Recent research, visually distilled.
Physics of Fluids · 2026
How sparse and how noisy? Benchmarking inverse PINNs for Manning friction.
A systematic 1D/2D shallow-water benchmark tests when inverse physics-informed neural networks can recover Manning’s roughness from sparse and noisy observations, and how parameter recovery depends on the observed variables.
Read the paper →
HESS · 2026Compound flooding
A typology for hybrid compound-flood modeling
A systematic framework distinguishing sequential, feedback, and ensemble structures for combining statistical and physics-based models.
Science Advances · 2026Climate extremes
Marine heatwaves and cyclone destructive power
Global analysis of how marine heatwaves and rapid intensification can act synergistically to amplify tropical-cyclone destructive potential.
GC · 2025Risk communication
Visualizing evolving compound-flood dependencies
The Angles method uses geometric relationships to communicate nonstationary dependence among flood drivers to technical and broader audiences.
Research impact
Science that travels beyond the paper.
Work on tropical-cyclone rapid intensification and coastal hazards has reached research, practitioner, and public audiences through interdisciplinary collaboration and media communication.
Washington Post & New York TimesMedia coverage of rapid-intensification research
BBC News & Sky NewsLive interviews / public-facing science communication
AGU session leadershipCoastal hydrology session chairing and community engagement
Open research toolsCode, educational resources, and reproducible workflows on GitHub
Selected publications
Full publication list →Recent contributions.
2026
Salt marsh effects on flood hazard metrics under compound coastal flooding
2026
How sparse and how noisy? Systematic benchmarking of inverse physics-informed neural networks for Manning friction estimation in shallow water equations
2026
Synergistic impact of marine heat waves and rapid intensification exacerbates tropical cyclone destructive power worldwide
2026
Unraveling uncertainty in compound flood modeling: sensitivity of simulations to forcings and model parameters
2026
Towards a typology for hybrid compound flood modeling
Contact
Research, collaboration, or a good coastal-hazards question?
I’m based in the Department of Civil and Environmental Engineering at Princeton University. For research collaborations, talks, and academic inquiries, email is the most direct route.
PositionAssociate Research Scholar
AffiliationPrinceton University · CEE
OfficeE308 Engineering Quad · Princeton, NJ 08544