TIES working groups
The TIES Early Career Committee coordinates three working groups that bring together researchers interested in statistical and computational methods for environmental science. The groups are designed to foster collaboration across the Society, support early-career researchers, and develop practical resources, software, benchmarks, and publications for the broader environmetrics community.
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TIES Early Career Committee chairs |
Members |
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Joshua North Josh is a Career Track Research Scientist at Lawrence Berkeley National Laboratory. |
Likun Zhang Likun is an Assistant Professor in the Department of Statistics at the University of Missouri. |
Aman Bhullar |
The 2026 working groups are organized around three complementary themes:
Please sign up for one of the working groups here.
Working Group 1: High-Performance Statistical Computing (HPSC) for Environmental Sciences
Lead: Mary Lai Salvana, University of Connecticut
Proposal link here for more details
This group will examine how high-performance statistical computing can enable modern environmental data analysis at scales that exceed conventional statistical workflows. Its activities will focus on parallel and distributed algorithms, GPU-accelerated computing, scalable MCMC and optimization, and mixed-precision numerical linear algebra for spatial and spatio-temporal models. A central goal is to develop practical guidance on precision--accuracy trade-offs, together with open-source R and Python implementations, reproducible benchmarks, and a joint review or roadmap paper for the environmetrics community.
Mary Lai Salvana Assistant Professor in the Department of Statistics at the University of Connecticut (UConn) |
Mary Lai Salvana is an Assistant Professor in the Department of Statistics at the University of Connecticut (UConn). Prior to joining UConn, she was a Postdoctoral Fellow in the Department of Mathematics at the University of Houston. She received her Ph.D. in Statistics from the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. She obtained her BS and MS degrees in Applied Mathematics from Ateneo de Manila University, Philippines, in 2015 and 2016, respectively. Her research interests include extreme and catastrophic events, risks, disasters, spatial and spatio-temporal statistics, environmental statistics, computational statistics, large-scale data science, and high-performance computing. |
Working Group 2: Suitable Extreme Value Approaches for Weather/Hazard — Identifying Best Practices
Lead: Kate Saunders, Monash University
Proposal link here for more details
This group will develop practical and accessible guidance for applying extreme value methods in climate and meteorological sciences. Motivated by the gap between modern EVT methodology and operational practice, the group will identify where updated guidance is needed for fitting, validating, and interpreting models of weather and climate extremes. Planned outputs include application-specific guidance across major environmental variables, shared data case studies for benchmarking new methods, tutorial resources, and software guidance that help connect statistical method developers with practitioners responsible for weather, hazard, and climate-risk communication.
Kate Saunders Senior Lecturer in Econometrics and Business Statistics at Monash University, Australia |
Kate Saunders is a Senior Lecturer in Econometrics and Business Statistics at Monash University. Her primary focus is on modelling climate extremes and understanding how the probability of extreme events might be influenced by natural variability and climate change. Other interests include statistical post-processing of meteorological forecasts, quality control of meteorological data, compound event risk, and natural hazard analytics. Kate's research improves our understanding of extreme events and helps support informed decisions about weather- and climate-related risk. |
Working Group 3: Generative Models for Environmental Spatial Data: A Statistician's Perspective
Lead: Paul Wiemann, Ohio State University
Proposal link here for more details
This group will bring a statistical perspective to generative models for spatial, temporal, and spatio-temporal environmental data. Rather than focusing only on visually realistic synthetic fields or state-of-the-art machine-learning benchmarks, the group will assess when generative approaches such as diffusion models, normalizing flows, generative adversarial networks, and variational autoencoders are statistically valid, useful, and practical. The group will compare these methods with established statistical baselines using criteria such as uncertainty quantification, calibration, computational cost, data requirements, ease of implementation, and failure modes, with the aim of producing a publishable practical guide for environmental statisticians.
Aman Bhullar Post-Doctoral Fellow at Agriculture and Agri-Food Canada |
Aman is a researcher who recently earned a Ph.D. from the University of Guelph for developing a novel framework to advance agricultural land suitability assessments across Canada. Currently, Aman is a Post-Doctoral Fellow at Agriculture and Agri-Food Canada, researching how quantum computing can be used for agricultural remote sensing to better monitor crops from afar. This research is driven by a commitment to food security, specifically focusing on projecting how climate change will impact future yields to better support sustainable agricultural management and adaptive farming practices. |
The TIES Early Career Committee
