Department of Statistics, University of Connecticut
Philip E. Austin Building, Room 330
215 Glenbrook Road, U-4120
Storrs, CT 06269-4120
"Having lived through the devastations of Supertyphoon Sendong (Washi), I witnessed firsthand how a single event can unleash cascading disasters. Power grids failed, transportation systems collapsed, hospitals became overwhelmed, and communication networks went dark—each failure amplifying the next. This experience drives my work advancing self-organized criticality models for next-generation early warning systems, shifting from crisis reaction to crisis prevention."
Invited Session at JSM 2026. Learn more →
I'm an Assistant Professor in Statistics at the University of Connecticut. Prior to joining UConn, I was a Postdoctoral Fellow at the University of Houston. I received my Ph.D. at KAUST, Saudi Arabia.
My research interests include extreme events, risks, disasters, space-time statistics, high-dimensional and multivariate statistics, high performance computing, big data, machine learning, deep learning, artificial intelligence, and environmental data science.
My mission is to understand the dynamics of the climate system using statistics and to communicate its laws through statistical models. Climate change is now outpacing climate models. The systems and infrastructures built for managing disastrous events were based on models rendered outdated by the new normal of extreme climate events.
I lead the research on Extreme Events, Risks, and Disasters that develops state-of-the-art models to advance climate science and shape our awareness of future disaster chains. By combining space-time statistics, extreme statistics, Bayesian statistics, machine learning, physics models, and high-performance computing, we develop new models that more faithfully render topographic, geologic, atmospheric, and biological details.
Extreme and catastrophic events, risks, disasters, crash protection, antifragility, spatial and spatio-temporal statistics, high-dimensional and multivariate statistics, high performance computing, computational statistics, environmental data science, big data, machine learning, deep learning, artificial intelligence
Ph.D. in Statistics, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2017–2021)
Thesis: Lagrangian Spatio-Temporal Covariance Functions for Multivariate Nonstationary Random Fields. Adviser: Marc G. GentonM.S. in Applied Mathematics, Ateneo de Manila University, Philippines (2015–2016)
B.S. in Applied Mathematics, Ateneo de Manila University, Philippines (2011–2015)
Assistant Professor in Statistics, University of Connecticut (2023–present)
Visiting Professor, Statistics Department, KAUST, Jeddah, Saudi Arabia (Oct 2023–Dec 2023)
Postdoctoral Researcher, University of Houston (2021–2023)
Al-Kindi Statistics Research Student Award, KAUST (2021)
American Statistical Association
New England Statistical Society
International Statistical Institute
The International Environmetrics Society
Royal Statistical Society
Institute of Mathematical Statistics
Caucus for Women in Statistics and Data Science, Country Representative (Philippines)
Ateneo Innovation Center
Co-founder, Co-organizer & Webmaster, High-Performance Statistical Computing (2025–present)
Founder, Co-organizer & Webmaster, Spatio-Temporal Statistics and Data Science Online Seminars (2025–present)
Analysis of Experiments (Spring 2025)
Applied Spatio-Temporal Statistics (Fall 2025)
Introduction to Mathematical Statistics I (Spring 2024)
Organizer & Chair · Invited Session, JSM 2026 · Boston, MA Learn more →
Large-Scale Spatial Data Science Short Course · Instructor
ISI World Statistics Congress · The Hague, Netherlands
Applications of Spatio-Temporal Modeling · Invited Speaker
EcoSta 2025 · Waseda University, Tokyo
Multi-Hazard Bayesian Hierarchical Model · Invited Speaker
JSM 2025 · Nashville, TN
Large-Scale Spatial Data Science Short Course · Instructor
JSM 2025 · Nashville, TN
SOC Model of Extreme Events and Cascading Disasters · Invited Speaker
University of Missouri
Large-Scale Spatial Data Science Short Course · Instructor
JSM 2024 · Portland, OR
Large-Scale Spatial Data Science Short Course · Instructor
NESS 2024 · University of Connecticut
