CATIA is a catastrophe AI system that integrates advanced artificial intelligence, actuarial science, risk analysis, and machine learning to provide robust assessments of natural hazards such as hurricanes, floods, and wildfires, with a focus on financial impacts and mitigation strategies.

Location-level Florida hurricane catastrophe model in Python: HURDAT2-calibrated hazard + physical wind field (Holland, Vickery-Wadhera, Kaplan-DeMaria), synthetic exposure, HAZUS-anchored vulnerability, OED/YLT/ELT formats, XoL reinsurance with reinstatements, gross/net AEP/OEP curves & PMLs, historical backtesting (Andrew, Ian).

Full-stack catastrophe analytics workbench — property cat pricing, ILS parametric triggers, and portfolio risk engines. React + Fastify + Scala + BullMQ.

County-level winter weather loss analysis (SHELDUS) combined with snowfall accumulation data (NOHRSC) and upper-air weather variables (ERA5) to identify key drivers of Midwest DJF property damage.

Monte Carlo simulation of insurance losses using a frequency–severity model with Poisson claim frequency, lognormal and gamma severity distributions, and tail risk analysis.

Probabilistic catastrophe risk model for hurricane loss quantification — simulates a 10,000-year synthetic storm catalog (NOAA HURDAT2 + ENOW data) to price tropical cyclone insurance losses at the property level.

Personal portfolio for Wenyu Chiou — PhD candidate at Lehigh (CEE / Catastrophe Modeling). Research on LLM-agent frameworks coupled with catastrophe flood models. Bilingual EN/中文.

Verisk (formerly ISO) is a leading data analytics and technology company serving the insurance, energy, and financial services industries.