Assistant Professor in Statistics

Department of Statistics

University of Connecticut

Office Address

Philip E. Austin Building, Room 330
University of Connecticut
215 Glenbrook Road, U-4120
Storrs, CT 06269-4120

What’s New?

  • I will be one of the instructors for the Large Scale Data Science short course during the 37th New England Statistics Symposium (NESS 2024) hosted by University of Connecticut on May 20, 2024. Check out https://symposium.nestat.org/index.html for more details.
  • I will be joining the Department of Statistics at University of Connecticut as Assistant Professor on December 15, 2023.
  • I will be a visiting professor at King Abdullah University of Science and Technology in Thuwal, Saudi Arabia from October 1-December 14, 2023.

About Me

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for managing disastrous events such as fires, floods, and droughts, were based on climate models which are rendered outdated by the new normal of extreme climate events.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.

  • Insurance Companies—Will the storms tank the insurance industry? How can we serve the coastal states without going bankrupt? How can we design crop insurance to protect farmers? How can we make money with these many extreme events? Can we pay-out all the claims? Will there be reinsurance companies willing to sell insurance?
  • Reinsurance Companies (ex. Swiss Re, Munich Re)—Can the insurance companies sustain the claims they are facing?
  • Real Estate Market—Will there be insurance companies willing to sell insurance?
  • Homeowners—How can I recoup my investments on my house along the beach? How will my neighborhood look like with rising sea levels and more intense storms? Should we reinforce our properties to withstand extreme climate events or retreat to a much safer location?
  • Lawmakers—How can we convince the insurance companies to keep doing business in high-risk areas? Who should bear the cost of climate change? What do we owe the people living in high-risk areas? What are the appropriate incentives and policies to reduce emissions?
  • Florida—Will our economy, which is heavily dependent on real estate along the coast, tourism, and construction, survive when insurance companies are fleeing out of the state?
  • Local Governments—Should we restrict purchasing or building properties in high-risk areas?
  • Corporations— How can we add climate risk into our balance sheet and asset purchases? How do we compute expected losses? How do we hedge against climate risks? Where do we place new assets, such as factories, and what do we do with existing assets in once-safe areas now threatened by extreme climate events? How exposed is my supply chain?
  • Investors—How do we value fixed income assets affected by climate risks?
  • Finance Industry—How will climate risks affect the stability of the financial system? What types of climate-related financial shocks can we expect? How can we build financial products and services that integrate climate risk into new or existing instruments?
  • Central Banks—How climate change could affect macroeconomic forecasting, systemic risks, and monetary policymaking?
  • Farmers—How can we prepare for climate change impacts on our livelihood? Is there a climate-resilient way to produce food? Will there be an increase in the crop insurance premiums?
  • Climate Scientists—How will precipitation change in the future? How high sea levels could rise?
  • Non-profit Organizations (ex. Carbon Tracker)—How can we convince the finance industry that carbon plants are not profitable?

CV

Research Interests

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

Education

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)

Employment

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)

Honors & Awards

Al-Kindi Statistics Research Student Award, King Abdullah University of Science and Technology (KAUST), Jeddah, Saudi Arabia (2021)

PROFESSIONAL ASSOCIATIONS

American Statistical Association Member
New England Statistical Society Member
Ateneo Innovation Center Research Fellow

SERVICE & OUTREACH

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

Research

Publications

Publications

  1. Salvaña, M. L. O., & Genton, M. G. (2020). Nonstationary cross-covariance functions for multivariate spatio-temporal random fields. Spatial Statistics, 37, 100411. https://doi.org/10.1016/j.spasta.2020.100411.
    PDF
  2. Salvaña, M. L. O., & Genton, M. G. (2021). Lagrangian spatio-temporal nonstationary covariance functions. Book Chapter in Advances in Contemporary Statistics and Econometrics, Festschrift for Prof. C. Thomas-Agnan, 427-447. https://link.springer.com/chapter/10.1007/978-3-030-73249-3_22.
    PDF
  3. Salvaña, M. L. O., Abdulah, S., Huang, H., Ltaief, H., Sun, Y., Genton, M. G., & Keyes, D. E. (2021). High performance multivariate spatial modeling for geostatistical data on manycore systems. IEEE Transactions on Parallel and Distributed Systems, 32(11), 2719-2733. https://ieeexplore.ieee.org/document/9397281.
    PDF
  4. Salvaña, M. L. O., Abdulah, S., Ltaief, H., Sun, Y., Genton, M. G., & Keyes, D. E. (2022). Parallel space-time likelihood optimization for air pollution prediction on large-scale systems. PASC '22: Proceedings of the Platform for Advanced Scientific Computing Conference, 17, 1-11. https://dl.acm.org/doi/pdf/10.1145/3539781.3539800.
    PDF
  5. Salvaña, M. L. O., Lenzi, A., & Genton, M. G. (2022). Spatio-temporal cross-covariance functions under the Lagrangian framework with multiple advections. Journal of the American Statistical Association, 118, 2746-2761. https://doi.org/10.1080/01621459.2022.2078330.
    PDF

Preprints

Teaching

STAT 3375Q - Introduction to Mathematical Statistics I

University of Connecticut, Undergraduate Course, Spring 2024

Course Description

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.

Class Times and Locations
  • Lectures: Monday and Wednesday, 4:40PM-5:55PM in YNG 327, Storrs Campus
    Instructor: Mary Lai Salvaña (marylai.salvana@uconn.edu)
    Office Hours: Monday and Wednesday, 3:30PM-4:30PM in AUST 330, Storrs Campus
  • Discussion Section: Friday, 1:25PM-2:15PM in AUST 344, Storrs Campus
    TA: Banani Bera (banani.bera@uconn.edu)
Course Materials

Syllabus Schedule

Announcements

Slides for Lecture 12 is up!

Solutions to Quiz 3 Review Exercises is up!

Slides for Lecture 11 is up!

Events

Exciting Contents soon

News

Exciting Contents soon