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
Leads: Mary Lai Salvana, University of Connecticut; Likun Zhang, University of Missouri
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
Leads: Kate Saunders, Monash University; Jonathan Koh, ETH Zurich; Arnab Hazra, IIT Kanpur
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. |
Jonathan Koh Scientist/Lecturer (Oberassistent) at the Seminar for Statistics, ETH Zurich |
Jon is a scientist and lecturer at the Seminar for Statistics at ETH Zurich since February 2025. Before joining ETH, Jon was a postdoctoral researcher at the University of Bern, where he worked with Prof. Johanna Ziegel and Prof. Olivia Martius. He received his PhD in Statistics from EPFL in January 2022 under the supervision of Prof. Anthony Davison. His research develops new statistical tools for modeling environmental extremes, with broad interests at the intersection of spatial extreme-value theory, machine learning, forecast evaluation, and environmental statistics. |
Arnab Hazra Assistant Professor in Statistics and Data Science at the Indian Institute of Technology Kanpur |
Arnab is an Assistant Professor at the Indian Institute of Technology Kanpur, where he leads the Spatial Statistics Research Group. His research spans spatial statistics, extreme value analysis, heavy-tailed processes, hierarchical Bayesian modeling, high-dimensional data analysis, environmental and ecological statistics, Bayesian nonparametrics, robust inference, goodness-of-fit testing, coral reef applications, and brain imaging. He received his Ph.D. in Statistics from North Carolina State University in 2018 under the supervision of Brian J. Reich and Ana-Maria Staicu. |
Working Group 3: Generative Models for Environmental Spatial Data: A Statistician's Perspective
Leads: Aman Bhullar, Agriculture and Agri-Food Canada; Paul Wiemann, Ohio State University; Joshua North, Lawrence Berkeley National Laboratory; Won Chang,
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. |
Paul Wiemann Assistant Professor in Statistics at the Ohio State Unversity |
Paul is a statistician whose research focuses on Bayesian statistics, computational statistics, variational approximation, MCMC, shrinkage priors, and spatial statistics. He joined the Department of Statistics in 2024 after serving as a postdoctoral researcher at the University of Wisconsin and, before that, with the Chair in Statistics at the University of Göttingen in Germany. |
Won Chang Associate Professor in Statistics at Seoul National University |
Before joining Seoul National University in 2024, Won was a faculty member at the University of Cincinnati, where he served as Assistant Professor and later Associate Professor. He received his Ph.D. in Statistics from Pennsylvania State University in 2014 under the supervision of Murali Haran and Klaus Keller. His research focuses on uncertainty quantification for computer model experiments using deep learning and Gaussian processes, as well as spatial data analysis with applications in atmospheric science, hydrology, genetics, and related fields. |
The TIES Early Career Committee
