Assistant Professor in Statistics
Department of Statistics
University of Connecticut
Philip E. Austin Building, Room 330
University of Connecticut
215 Glenbrook Road, U-4120
Storrs, CT 06269-4120
Hi! I’m Mary Lai, an Assistant Professor in Statistics at University of Connecticut (UConn). Prior to joining UConn, I was a Postdoctoral Fellow at the Department of Mathematics at University of Houston. I received my Ph.D. at the King Abdullah University of Science and Technology (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, environmental data science.
My mission is to understand the dynamics of the climate system using statistics and to communicate its laws through statistical models. My research has provided me the opportunity to analyze climate processes, to build models that describe how the components in the climate system interact, and to develop the high-performance computing skills to run big data experiments required for climate research. Through the years, my appreciation to how much story climate data can tell us about our planet has grown.
My research revolves around physics-motivated statistical modeling of the Earth to contribute to the scientific communities’ efforts to understand and tackle climate change. We are still facing significant gaps in understanding and predicting Earth’s climate and weather. Climate change is now outpacing climate models. Nowadays, the weather that we have prepared for is not the weather that we are getting. The systems and infrastructures built for managing disastrous events such as fires, floods, and droughts, were based on climate models which are rendered outdated by the new normal of extreme climate events. Climate models failed to predict the deadly heat wave in the cool and rainy Pacific Northwest region last June 2021, the megadrought in the Southwest US, the changing ecosystem of the Boreal forest (the world’s largest carbon sink), the rapid warming in the Arctic ocean, and the shifting jet stream behavior which caused simultaneously the deadly flooding in Germany and Belgium, the heatwave in Canada, and the Black Sea flooding in the summer of 2021. Unless we update these models, we are left vulnerable to these “unforeseen” climate events.
As an Assistant Professor in Statistics at the UConn, I lead the research on Extreme Events, Risks, and Disasters that develops state-of-the-art models that help advance climate science and shape our awareness of future disaster chains. My expertise are on three key areas which I believe are the most pressing issues of our time: Extreme Events, Risks, and Disasters Modeling.
By thinking of the Earth as a complex system and by identifying the vulnerabilities of its components due to extreme events, we can build resilience and minimize losses of any kind. By combining space-time statistics, extreme statistics, Bayesian statistics, machine learning techniques, physics models, and other methods for massive datasets with complex dependence structures, powered by high-performance computing technologies, we can develop new models that can more faithfully render topographic, geologic, atmospheric, and biological details over small regions.
My research outputs include a high-resolution and high-dimensional map of space-time correlations of different climate variables anywhere on Earth and forecasts of climate events such as floods, droughts, and fires, from days to years in advance. Furthermore, my research in these three areas will address the concerns of the following institutions, among many others.
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
PHD IN STATISTICS, King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia (Jan 2017 – Jul 2021)Thesis: Lagrangian Spatio-Temporal Covariance Functions for Multivariate Nonstationary Random Fields, Advisor: Marc G. Genton
MS IN APPLIED MATHEMATICS, Ateneo de Manila University, Manila, Philippines (Aug 2015 – Jul 2016)
BS IN APPLIED MATHEMATICS, Ateneo de Manila University, Manila, Philippines (Jun 2011 – Mar 2015)
Assistant Professor in Statistics, Department of Statistics, University of Connecticut (December 2023 - present)
Postdoctoral Researcher, Department of Mathematics, University of Houston (August 2021 – July 2023)
Al-Kindi Statistics Research Student Award, King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia (2021)
American Statistical Association Member
New England Statistical Society Member
Ateneo Innovation Center Research Fellow
Chair & Organizer, The 36th New England Statistics Symposium, Boston University, MA, USA (June 3-6, 2023) — Opportunities and Challenges in the Use of Statistics in Disaster Science
University of Connecticut, Undergraduate Course, Spring 2024
Upon completion of this course, students are expected to understand and apply basic concepts in mathematical statistics. In particular, students will study concepts of probability theory, discrete and continuous distributions and their probability distributions, multivariate probability distributions and functions of random variables.