Presentations & Lectures

• Machine Learning Estimates of Geothermal and Critical Mineral Prospectivity of the Great Basin (2026) — V.V. Vesselinov, T. Kliphuis — Stanford Geothermal Workshop DOI
• GAIA: Cloud Framework for Geospatial Artificial Intelligence Analyses (2025) — T. Kliphuis, V.V. Vesselinov — HydroML 2025, University of California, Irvine DOI
• GeoDAWN & GeoFLIGHT to GeoTGo: From complex data to defensible decisions related to geothermal prospectivity (2025) — V.V. Vesselinov et al. — Stanford Geothermal Workshop DOI
• UrbanAI Transforming Urban Energy Planning Using Artificial Intelligence (2025) — R. Yang et al. — Machine Learning in Solid Earth Geoscience DOI
• When Earth Meets AI (2025) — V.V. Vesselinov, T. Kliphuis — RealmIQ El Sailon Link
• Laying the groundwork for a greener future: ML for characterizing and managing geologic reservoirs (2024) — V.V. Vesselinov, H. Jasperson, T. Kliphuis Link
• Mapping geothermal resources using AI/ML (2024) — T. Kliphuis, H. Jasperson, V.V. Vesselinov — New Mexico Geological Society Annual Spring Meeting DOI
• GeoTGO: Equitable and Inclusive Tool for Community-Based Geothermal (2023) — T. Kliphuis, M. Bluehouse, V.V. Vesselinov — Stanford Geothermal Workshop Link
• ChemML: Understanding groundwater flow and contaminant transport using machine learning (2022) — V.V. Vesselinov, T. Kliphuis — American Geophysical Union, San Juan, Puerto Rico Link
• Machine learning and a process model to better characterize hidden geothermal resources (2022) — M. Ahmmed, V.V. Vesselinov — Geothermal Rising Conference (GRC) Link
• Machine Learning for Small, Uncertain, and Sparse Data Sets (2022) — M. Ahmmed, V.V. Vesselinov — AGU Fall Meeting Link
• Novel machine learning methods and tools for geothermal and geochemical problems (2022) — V.V. Vesselinov, T. Kliphuis — AGU Fall Meeting Link
• Physics-Informed Machine Learning of Geothermal, Geomechanical, Geochemical Process (2022) — V.V. Vesselinov, T. Kliphuis — AGU Fall Meeting Link
• Practical Glass-Box Machine Learning for Seasonal Water Supply Forecasting, with Applications to the Owyhee and Yellowstone Rivers: Regression Using Climate Indices Derived from SNOTEL Data Using Nonnegative Matrix Factorization with k-Means Clustering (2022) — W. Fleming, V.V. Vesselinov, A. Goodbody — AGU Fall Meeting Link
• Toward Automated Data-Model Calibration for the Arctic Terrestrial Ecosystem Model (2022) — E. Jafarov et al. — AGU Fall Meeting Link
• GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources (2021) — V.V. Vesselinov, et al. — Department of Energy, Geothermal Office Link
• GeoThermalCloud: Fusion of Big Data and Multi-Physics Models (2021) — V.V. Vesselinov, et al. — JuliaCon, Boston, MA Link
• Hidden geothermal signatures of the southwest New Mexico (2021) — V.V. Vesselinov, et al. — World Geothermal Congress, Reykjavik, Iceland
• Machine Learning to Characterize the State of Stress and its Influence on Geothermal Production (2021) — V.V. Vesselinov, et al. — Geothermal Rising Conference
• ML4Geo: Machine Learning for Geosciences (2021) — V.V. Vesselinov, et al. — JuliaCon, Boston, MA Link
• SmartTensors: Unsupervised Machine Learning (2021) — V.V. Vesselinov, et al. — JuliaCon, Boston, MA Link
• Unsupervised and Physics-Informed Machine learning in Geosciences (2021) — V.V. Vesselinov, et al. — Baylor University, Texas Link
• Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning (2020) — V.V. Vesselinov, et al. — Stanford Geothermal Workshop Link
• Machine learning for geothermal resource analysis and exploration (2020) — V.V. Vesselinov, et al. — XXIII International Conference on Computational Methods in Water Resources (CMWR), Stanford, CA Link
• Predicting oil and gas production from unconventional tight-rock reservoirs using machine learning (2020) — V.V. Vesselinov — XXIII International Conference on Computational Methods in Water Resources (CMWR) Link
• Site-Scale and Regional-Scale Modeling for Geothermal Resource Analysis and Exploration (2020) — M. Mudunuru, V.V. Vesselinov, et al. — Geothermal Workshop, Stanford, CA Link
• Unsupervised and Physics-Informed Machine Learning Analyses for Characterization of Energy Production from Unconventional Reservoirs (2020) — V.V. Vesselinov — Machine Learning in Oil & Gas Conference Link
• Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data (2020) — V.V. Vesselinov — Bureau of Economic Geology, University of Austin, Texas Link
• Machine learning analyses for characterization of oil, gas and water production from unconventional tight-rock reservoirs (2019) — V.V. Vesselinov — AGU Fall Meeting Link
• Machine Learning Analyses of Climate Data and Models (2019) — V.V. Vesselinov — 11th World Congress of European Water Resources Association (EWRA), Madrid, Spain Link
• Novel Unsupervised Machine Learning Methods for Data Analytics and Model Diagnostics (2019) — V.V. Vesselinov — Machine Learning in Solid Earth Geoscience, Santa Fe Link
• Physics-Informed Machine Learning Methods for Data Analytics and Model Diagnostics (2019) — V.V. Vesselinov — M3 NASA DRIVE Workshop, Los Alamos Link
• Unsupervised and Physics-Informed Machine Learning of Big Data (2019) — V.V. Vesselinov — Workshop: Applications of Big Data and High-Performance Computing in Earth Sciences, AGU Fall Meeting, San Francisco, CA (invited) Link
• Unsupervised Machine Learning Methods for Feature Extraction (2019) — V.V. Vesselinov — New Mexico Big Data & Analytics Summit Link
• Unsupervised Machine Learning: Nonnegative Matrix Tensor Decompositions (2019) — V.V. Vesselinov — MIT, Boston, MA Link
• Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models (2018) — V.V. Vesselinov — Recent Advances in Machine Learning and Computational Methods for Geoscience, Institute for Mathematics and its Applications, University of Minnesota DOI
• Novel Machine Learning Methods for Extraction of Features Characterizing Datasets and Models (2018) — V.V. Vesselinov — AGU Fall meeting Link
• Novel Robust Machine Learning Methods for Identification and Extraction of Unknown Features in Complex Real-world Data Sets (2018) — V.V. Vesselinov, D. O'Malley, B. Alexandrov — Society for Industrial and Applied Mathematics (SIAM) Uncertainty Quantification, Garden Grove, CA (invited)
• Unsupervised Machine Learning Based on Non-negative Tensor Factorization for Analysis of Field Data and Simulation Outputs (2018) — V.V. Vesselinov et al. — Computational Methods in Water Resources (CMWR), Saint-Malo, France DOI
• Unsupervised Machine Learning based on Nonnegative Matrix/Tensor Factorization (2018) — V.V. Vesselinov et al. — World Congress on Computational Mechanics (WCCM) (invited)
• Unsupervised Machine Learning Based on Tensor Factorization (2018) — V.V. Vesselinov, D. O'Malley, B. Alexandrov — International Society for Porous Media (INTERPORE) DOI
• Decision Analyses for Groundwater Remediation (2017) — V.V. Vesselinov, D. O'Malley, D. Katzman — Waste Management Symposium DOI
• Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting (2017) — Y. Lin et al. — SIAM Conference on Computational Science and Engineering Link
• Nonnegative/binary matrix factorization with a D-Wave quantum annealer (2017) — D. O'Malley et al. — DOE LANL Presentation Link
• Quo vadis: Hydrologic inverse analyses using high-performance computing and a D-Wave quantum annealer (2017) — D. O'Malley, V.V. Vesselinov — AGU Fall Meeting DOI
• Uncertainty quantification and experimental design based on unsupervised machine learning identification of contaminant sources and groundwater types using hydrogeochemical data (2017) — V.V. Vesselinov, D. O'Malley, B. Alexandrov — AGU Fall Meeting Link
• Analysis of Hydrologic Time Series Reconstruction Uncertainty due to Inverse Model Inadequacy (2016) — J. He, S. Hansen, V.V. Vesselinov — AGU Fall Meeting Link
• Bi-Level Decision Making for Supporting Energy and Water Nexus (2016) — X. Zhang, V.V. Vesselinov — AGU Fall Meeting Link
• Groundwater Remediation using Bayesian Information-Gap Decision Theory (2016) — D. O'Malley, V.V. Vesselinov — AGU Fall Meeting Link
• Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting (2016) — Y. Lin et al. — AGU Fall Meeting Link
• Identifying Aquifer Heterogeneities using the Level Set Method (2016) — Z. Lu, V.V. Vesselinov, H. Lei — AGU Fall Meeting Link
• Model Analyses of Complex Systems Behavior using MADS (2016) — V.V. Vesselinov, D. O'Malley — AGU Fall Meeting DOI
• Prediction of Breakthrough Curves for Conservative and Reactive Transport (2016) — S. Hansen et al. — AGU Fall Meeting Link
• Reduced Order Models for Decision Analysis and Upscaling of Aquifer Heterogeneity (2016) — V.V. Vesselinov, D. O'Malley, B. Alexandrov — AGU Fall Meeting (invited) Link
• ZEM: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making (2016) — V.V. Vesselinov, D. O'Malley, D. Katzman — Waste Management Symposium DOI
• Model-Assisted Decision Analyses Related to a Chromium Plume at Los Alamos National Laboratory (2015) — V.V. Vesselinov, D. O'Malley, D. Katzman — Waste Management Symposium DOI
• A Social Dynamics Dependent Water Supply Well Contamination Model (2014) — J. Bakarji, D. O'Malley, V.V. Vesselinov — DOE LANL Presentation DOI
• Bayesian Information-Gap Decision Analysis Applied to a Geologic CO2 Sequestration Problem (2014) — D. O'Malley, V.V. Vesselinov — AGU Fall Meeting Link
• Model-free Source Identification (2014) — V.V. Vesselinov, B. Alexandrov — AGU Fall Meeting Link
• Random dispersion coefficients and Tsallis entropy (2014) — J. Cushman, V.V. Vesselinov, D. O'Malley — AGU Fall Meeting Link
• Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory (LANL) (2013) — V.V. Vesselinov et al. — Waste Management Symposium DOI
• What Matters When and Where For Anomalous Dispersion/Diffusion (2013) — D. O'Malley, V.V. Vesselinov — AGU Fall Meeting Link
• AGNI: Coupling Model Analysis Tools and High-Performance Subsurface Flow and Transport Simulators for Risk and Performance Assessments (2012) — V.V. Vesselinov, et al. — XIX International Conference on Computational Methods in Water Resources (CMWR 2012) Link
• MADS & ASCEM (2012) — V.V. Vesselinov — AGU Fall Meeting Link
• Model-driven decision support for monitoring network design based on analysis of data and model uncertainties: methods and applications (2012) — V.V. Vesselinov, D. Harp, D. Katzman — AGU Fall meeting (invited) DOI
• Numerical Optimization using the Levenberg-Marquardt Algorithm (2011) — L. Leif Zinn-Bjorkman, V.V. Vesselinov — DOE LANL Presentations DOI
• Recent developments in MADS algorithms: ABAGUS and Squads (2011) — D. Harp, V.V. Vesselinov — DOE LANL Presentations DOI
• Environmental Management Modeling Activities at Los Alamos National Laboratory (LANL) (2010) — V.V. Vesselinov, et al. — Department of Energy Technical Exchange Meeting, Performance Assessment Community of Practice, Hanford DOI
• Tomographic inverse estimation of aquifer properties based on pressure variations caused by transient water-supply pumping (2008) — V.V. Vesselinov et al. — AGU Meeting DOI
• Uncertainties in Transient Capture-Zone Estimates (2006) — V.V. Vesselinov — Conference on Computational Methods in Water Resources (CMWR), Copenhagen, Denmark Link