STSDS Community Hub
Connecting Global Research in Spatio-Temporal Statistics & Data Science
Welcome to our international seminar series focusing on spatio-temporal statistics and data science. Our seminars bring together researchers and practitioners from around the world to share cutting-edge methodologies and real-world applications.
We’re a global forum for methods, theory, and applications that live in space and time—from geostatistics and point processes to machine learning for space-time data, forecasting, and high-performance workflows.
Our speakers span academia, industry, and government and highlight work on models, scalable inference, uncertainty quantification, causal questions on maps/graphs, computational statistics, extremes, and big data. Applications include climate and environment, public health, transportation and mobility, earth observation, economics, and more.
Subscribe to the mailing list to get:
- Talk announcements and calendar-friendly reminders
- Slides/recordings when available
- Calls for speakers, mini-tutorials, and community news
Join us to learn, connect, and shape the next wave of spatio-temporal statistics and data science!
Upcoming Events
Register for the event here
Register for the event here
Register for the event here
Register for the event here
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Community Activities
Seminars & Workshops
Monthly expert presentations and hands-on sessions
Research Highlights
Showcase of breakthrough achievements in statistics
Performance Benchmarks
Industry-standard metrics and computational comparisons
Research Areas
Our seminar series covers a broad range of topics in spatial and spatio-temporal statistics:
Environmental Statistics
Statistical methods for environmental monitoring, climate analysis, ecological modeling, and environmental risk assessment.
Spatial Data Analysis
Geostatistical and point process methods, spatial regression, kriging, and statistical modeling of spatially correlated data.
Spatio-Temporal Modeling
Advanced methods for analyzing data that varies across both space and time, including dynamic spatial models.
Extreme Statistics
Models for rare and high-impact events, heavy-tailed modeling, spatial and space–time extremes, and dependence of compound/cascading events.
Computational Methods
Scalable inference and optimization, sparse linear algebra, multi- and mixed-precision techniques, software stacks, Efficient data formats
Visualization
Interactive maps and time-aware graphics, uncertainty visualization, animation of trajectories and fields
High-Performance Computing
Distributed and GPU-accelerated workflows, parallel simulation, big data analytics
Seminar Format
Each seminar lasts 60 minutes and follows a structured format to maximize learning and interaction:
Minutes of Presentation
Expert speaker presentation
Minutes of Discussion
Questions and answers
Platform
Webinar
Questions are collected in the chat during and after the seminar, with moderators facilitating the discussion period.
Organizers
- Mary Lai O. Salvaña (Assistant Professor, University of Connecticut, Storrs, CT, USA) - marylai.salvana@uconn.edu
- Jian Cao (Assistant Professor, University of Houston, Houston, TX, USA) - jcao21@central.uh.edu
- Jordan Richards (Assistant Professor, University of Edinburgh, Edinburgh, UK) - jordan.richards@ed.ac.uk
- Yan Song (Assistant Professor, University of British Columbia, Vancouver, BC, Canada) - yan.song@stat.ubc.ca
- Yan Gong (Incoming Lecturer, University of New South Wales, Sydney, NSW, Australia) - yangong@hsph.harvard.edu