Conference and Workshop Talks

• (Invited) "Advancing uncertainty quantification in environmental modeling using AI/ML," AGU Fall Meeting, Washington, DC (Dec. 9-13, 2024).

• (Invited) "Advancing predictive understanding of hydrological systems through explainable AI," AGU Fall Meeting, Washington, DC (Dec. 9-13, 2024).

• "AI foundation model for Earth system modeling," AGU Fall Meeting, Washington, DC (Dec. 2024).

• (Invited) "Improving coastal compound flooding prediction using AI/ML techniques," DOE-BER Urban IFL PI Meeting, Washington, DC (Oct. 28-29, 2024).

• (Invited) "Advancing weather and hydrological forecasting using AI/ML techniques," ORNL-Tennessee Valley Authority (TVA) Collaboration Workshop, Oak Ridge, TN (Sept. 12, 2024).

• (Invited) "Trustworthy AI to advance scientific discovery," Workforce Development for Teachers and Scientists Workshop, Oak Ridge, TN (Sept. 11, 2024).

• (Invited) "AI foundation model to advance Earth system predictability," Trillion Parameter Challenges Workshop, NASA’s Marshall Space Flight Center, Huntsville, AL (Sept. 4-5, 2024).

• (Invited) "Advancing hydrological and Earth system modeling using AI/ML," DOE-BER-EESM PI Meeting, Washington, DC (Aug. 6-9, 2024).

• (Invited) "Trustworthy AI to advance Earth system predictability," DOE-BER-EESM PI Meeting, Washington, DC (Aug. 6-9, 2024).

• (Invited) "Computational Earth Sciences at ORNL," CCSD Summer Series, Oak Ridge, TN (June 24, 2024).

• (Invited) "Machine learning methods to advance Climate Modeling and Analysis," 9th European Seminar on Computing (ESCO), Pilsen, Czech Republic (June 10-14, 2024).

• (Invited) "Advancing Earth system model calibration: A diffusion-based method," ICLR Tackling Climate Change with Machine Learning Workshop (May 11, 2024).

• (Invited) "Advancing Earth system modeling using AI/ML," Oak Ridge Postdoc Association, Oak Ridge, TN (Mar. 8, 2024).

• (Invited) "Machine learning in subsurface modeling: Addressing small and big data challenges with uncertainty quantification," AGU Fall Meeting, San Francisco, CA (Dec. 13, 2023).

• (Invited) "Machine learning methods for advancing Earth system predictability," SIAM MPE Colloquium (Nov. 30, 2023).

• (Invited) "Scale Climate AI model on AMD-powered Frontier with DeepSpeed for better weather and climate solutions," SC23, Denver, CO (Nov. 13-17, 2023).

• (Invited) "Physics-informed, explainable, trustworthy AI for advancing Earth system predictability," Workshop on AI Application in Earth System Sciences (Nov. 6, 2023).

• (Invited) "Explainable and trustworthy machine learning with applications in Earth and material sciences," ORNL-VU Collaborative Workshop, Oak Ridge, TN (Sept. 18, 2023).

• (Invited) "Machine learning techniques to advance Earth system predictability," Technical Meeting with Air Force (May 3, 2023).

• (Invited) "Uncertainty quantification of machine learning models in scientific and engineering applications," ORNL-PNNL Collaboration Workshop, Oak Ridge, TN (Oct. 25-26, 2022).

• "Uncertainty quantification of machine learning models to improve streamflow prediction in changing climate and environmental conditions," AGU Fall Meeting, San Francisco, CA (Dec. 2022).

• (Invited) "Machine learning for improving Earth system predictability," SIAM Conference on Mathematics of Planet Earth (MPE22) (July 2022).

• (Invited) "PI3NN-LSTM: Improving machine learning model prediction of streamflow in novel climate conditions," HydroML Workshop (May 2022).

• (Invited) "ML4UQ: Machine learning to enable efficient uncertainty quantification in climate models," SIAM Conference on Uncertainty Quantification (2022).

• (Invited) "Physics-informed, interpretable machine learning for terrestrial ecosystem predictions," 11th International Conference on Ecological Informatics (2021).

• (Invited) "A prediction interval method for uncertainty quantification of regression models," Ecological Society of America (ESA) Annual Meeting (Aug. 2021).

• (Invited) "Machine learning methods for improving terrestrial ecosystem predictions," Society for Industrial and Applied Mathematics (SIAM) Annual Meeting (2021).

• (Invited) "An efficient Bayesian machine learning method for advancing ecological forecasting," Ecological Society of America (ESA) Annual Meeting (Aug. 2020).

• (Invited) "An efficient Bayesian method for advancing the application of deep learning in Earth science," ICDM Conference, Beijing, China (Nov. 2019).

• (Invited) "Learning-based inversion-free model-data integration to advance ecosystem model prediction," ICDM Conference, Beijing, China (Nov. 2019).

• (Invited) "2nd workshop on quantifying and reducing uncertainty in Earth system model projections," University of Leeds, UK (2019).

• (Invited) "Efficient surrogate modeling methods to advance model-data integration," AGU Annual Meeting, Washington, DC (2018).

• (Invited) "Optimization of sensor networks for improving climate predictions," DOE-BER Headquarters, Germantown, Maryland (2018).

• (Invited) "A systematic Bayesian uncertainty quantification framework in environmental modeling," Jinan University, Guangzhou, China (2018).

• (Invited) "Efficient surrogate modeling methods for model-data integration," Geological Survey of Japan, Japan (2018).

• (Invited) "Efficient uncertainty quantification methods in groundwater contaminant risk assessment," Japan Geosciences Union Annual Meeting, Japan (2018).

• (Invited) "Application of reduced-order modeling techniques for uncertainty quantification," CSMD-CSED Cross-Divisional Seminar, Oak Ridge, TN (2018).

• (Invited) "Potential exascale applications for quantifying uncertainty in the land-atmosphere system," Exascale Application in Climate and Environmental Science Workshop (2018).

• (Invited) "Advance climate model development, prediction, and risk assessment using artificial intelligence," AI for Climate Sciences Seminar (2018).

• "An efficient Bayesian data-worth analysis using a multilevel Monte Carlo method," AGU Fall Meeting, New Orleans, LA (2017).

• "Quantum-behaved particle swarm optimization for parameter estimation in terrestrial ecosystem models," ESS PI Meeting, Washington, DC (2017).

• "Calibration of the Community Land Model (CLM4.5) using surrogate-based global optimization," AGU Fall Meeting, San Francisco, CA (2016).

• (Invited) "A systematic Bayesian framework for uncertainty quantification in environmental modeling," Earth System Modeling Workshop, Oak Ridge, TN (2015).

• "Multilevel Monte Carlo method with application to uncertainty quantification in oil reservoir simulation," AGU Fall Meeting, San Francisco, CA (2014).

• "Assessment of predictive performance of Bayesian model averaging in groundwater reactive transport models," SIAM Conference on Uncertainty Quantification, Savannah, GA (2014).

• "Maximum likelihood Bayesian model averaging of groundwater reactive transport models," SIAM SEAS Annual Meeting, Melbourne, FL (2014).

• "Integration of Markov chain Monte Carlo simulation into UCODE for Bayesian uncertainty analysis," Geological Society of America (GSA) Annual Meeting, Charlotte, NC (2012).

• "Quantification of predictive uncertainty in groundwater reactive transport modeling," DOE-BER PI Meeting, Washington, DC (2012).

• "Analysis of predictive uncertainty measures of regression and Bayesian," MODFLOW and More Meeting, Golden, CO (2011).

• "A controlled experiment for investigating prediction accuracy and prediction uncertainty in groundwater flow modeling," AGU Annual Meeting, San Francisco, CA (2010).

• Uncertainty Quantification In Earth System Modeling. Department of Mathematics at the Federal University of Parana (UFPR), Brazil virtually, June, 2021.  

• MODFLOW, with the GIS-based GUI FREEWAT and Calibration and Uncertainty Quantification Using UCODE. Colorado School of Mines, Golden, CO, 2017.

• Groundwater Model Calibration Using OSTRICH: with Presentation of Additional Capabilities Available using PEST, PEST++ and UCODE-2014. Colorado School of Mines, Golden, CO, 2015.