We encourage students to submit papers on their research in environmental science. More information is available on the ASA website.
Information on the 2026 competition can be found here.
2025
Winners:
Shuren He, Texas A&M University - "GS-BART: Bayesian Additive Regression Trees with Graph-split Decision Rules for Generalized Spatial Nonparametric Regressions"
Kevin Collins, North Carolin State University - "Bayesian Model-based Decomposition Reveals Spatially Varying Temporal Shifts in Streamflow Profiles across North Temperate US Rivers"
Honorable Mentions:
Elliot Maceda, North Carolin State University - "A Variational Neural Bayes Framework for Inference on Intractable Posterior Distributions"
Xuanjie Shao, King Abdullah University of Science and Technology - "Deep Compositional Spatial Models for Nonstationary Extremal Dependence"
Sweta Rai, Colorado School of Mines - "Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks"
2024
Winners:
Jacob Johnson, Brigham Young University - "Fusing climate data products using a spatially varying autoencoder"
Julia Walchessen, Carnegie Mellon University - "Neural likelihood surfaces for spatial processes with computationally intensive or intractable likelihoods"
Honorable Mention:
Lucas Godoy, University of Connecticut - "From point to polygon: A unified framework for modeling spatial dependence,"
Michael Schwob, The University of Texas at Austin - "Composite Dyadic Models for Spatio-Temporal Data"
Troy Wixon, Colorado State University - "Attribution of seasonal wildfire risk to changes in climate: A statistical extremes approach"
2023
Winners:
Bora Jin, Duke University - “Spatial predictions on physically constrained domains: Applications to Arctic sea salinity data”
Claire Heffernan. Johns Hopkins University - “Did the COVID-19 lockdowns improve air quality? Machine-learning based robust estimation of effects of policy interventions on air pollution”
Eva Murphy, Clemson University - “Joint modeling of wind speed and wind direction through a conditional approach”
Myungsoo Yoo, University of Missouri - “A Bayesian Spatio-Temporal Level Set Dynamic Model and Application to Fire Front Propagation”
Honorable Mention:
Matthew Bonas, University of Notre Dame - “Calibration of Spatio-Temporal Forecasts from Citizen Science Urban Air Pollution Data with Sparse Recurrent Neural Networks”
2022
Winners:
Bora Jin, Duke University, "Bag of DAGs: Flexible & Scalable Modeling of Spatiotemporal Dependence"
Becky Tang, Duke University, "Species interactions and movement: modeling environmental effects on community dynamics"
Honorable Mention:
Joshua North, University of Missouri, "DEtection: A Bayesian Approach to Data-Driven Discovery of Nonlinear Dynamic Equations"
Lynsie Warr, Brigham Young University, "Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models"
Mengchen Wang, University of Illinois at Urbana-Champaign, "Bayesian Changepoint Estimation for Spatially Indexed Functional Time Series"
2021
Winners:
Saumya Bhatnagar (University of Cincinnati) "Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression"
Jingjie Zhang (Texas A&M University) "Multi-scale Vecchia approximations of Gaussian processes"
Honorable Mention:
Lauren Hoskovec (Colorado State University) "Infinite Hidden Markov Model for Multiple Multivariate Time Series with Missing Data"
Jun Tang (University of Iowa) "Space-Time Covariance Models on Networks with An Application on Streams"
Christopher Geoga (Rutgers University) "Flexible nonstationary spatio-temporal modeling of high-frequency monitoring data"
2020
Winners:
Suman Majumder (North Carolina State University) Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire PM2.5 Concentration Forecasting
Wanfang Chen (KAUST) Assessing the Risk of Disruption of Wind Turbine Operations in Saudi Arabia Using Bayesian Spatial Extremes
Honorable Mention:
Nathan Wikle (Pennsylvania State University) Mechanistic Models for Spatial Data from Ornstein-Uhlenbeck Processes
Ben Seiyon Lee (Pennsylvania State University) PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models
Ghulam Qadir (KAUST) Flexible Modeling of Variable Asymmetries in Cross-Covariance Functions for Multivariate Random Fields
2019
Marcin Jurek (TAMU): Multi-resolution filters for massive spatio-temporal data
Xiao Wu (Harvard): Causal Inference in Air Pollution Epidemiology Using Generalized Propensity Score Matching
Yuan Yan (KAUST): Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid
Jeremiah Liu – Harvard - An Adaptive Ensemble of Spatiotemporal Processes with Calibrated Predictive Uncertainty: A Bayesian Nonparametric Approach