Presentations
73 entries.
V.V. Vesselinov, T. Kliphuis, Machine Learning Estimates of Geothermal and Critical Mineral Prospectivity of the Great Basin, Stanford Geothermal Workshop, 2026. Link
T. Kliphuis, V.V. Vesselinov, GAIA: Cloud Framework for Geospatial Artificial Intelligence Analyses, HydroML 2025, University of California, Irvine, 2025. Link
V.V. Vesselinov, R. Yang, A. Markowitz, T. Kliphuis, GeoDAWN & GeoFLIGHT to GeoTGo: From complex data to defensible decisions related to geothermal prospectivity, Stanford Geothermal Workshop, 2025. Link
R. Yang, A. Markowitz, T. Kliphuis, R. Abuamoud, S. Ben Ayed, V.V. Vesselinov, UrbanAI Transforming Urban Energy Planning Using Artificial Intelligence, Machine Learning in Solid Earth Geoscience, 2025. Link
V.V. Vesselinov, H. Jasperson, T. Kliphuis, Laying the groundwork for a greener future: ML for characterizing and managing geologic reservoirs, 2024. PDF
T. Kliphuis, H. Jasperson, V.V. Vesselinov, Mapping geothermal resources using AI/ML, New Mexico Geological Society Annual Spring Meeting, 2024. Link
T. Kliphuis, M. Bluehouse, V.V. Vesselinov, GeoTGO: Equitable and Inclusive Tool for Community-Based Geothermal, Stanford Geothermal Workshop, 2023. PDF
V.V. Vesselinov, T. Kliphuis, ChemML: Understanding groundwater flow and contaminant transport using machine learning, American Geophysical Union, San Juan, Puerto Rico, 2022. PDF
M. Ahmmed, V.V. Vesselinov, Machine learning and a process model to better characterize hidden geothermal resources, Geothermal Rising Conference (GRC), 2022. Link
M. Ahmmed, V.V. Vesselinov, Machine Learning for Small, Uncertain, and Sparse Data Sets, AGU Fall Meeting, 2022. PDF
V.V. Vesselinov, T. Kliphuis, Novel machine learning methods and tools for geothermal and geochemical problems, AGU Fall Meeting, 2022. PDF
V.V. Vesselinov, T. Kliphuis, Physics-Informed Machine Learning of Geothermal, Geomechanical, Geochemical Process, AGU Fall Meeting, 2022. PDF
W. Fleming, V.V. Vesselinov, A. Goodbody, 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, AGU Fall Meeting, 2022. PDF
E. Jafarov, H. Genet, V.V. Vesselinov, V. Briones, R. Rutter, B. Rogers, S. Natali, Toward Automated Data-Model Calibration for the Arctic Terrestrial Ecosystem Model, AGU Fall Meeting, 2022. PDF
V.V. Vesselinov, et al., GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources, Department of Energy, Geothermal Office, 2021. PDF
V.V. Vesselinov, et al., GeoThermalCloud: Fusion of Big Data and Multi-Physics Models, JuliaCon, Boston, MA, 2021. PDF
V.V. Vesselinov, et al., Hidden geothermal signatures of the southwest New Mexico, World Geothermal Congress, Reykjavik, Iceland, 2021
V.V. Vesselinov, et al., Machine Learning to Characterize the State of Stress and its Influence on Geothermal Production, Geothermal Rising Conference, 2021
V.V. Vesselinov, et al., ML4Geo: Machine Learning for Geosciences, JuliaCon, Boston, MA, 2021. PDF
V.V. Vesselinov, et al., SmartTensors: Unsupervised Machine Learning, JuliaCon, Boston, MA, 2021. PDF
V.V. Vesselinov, et al., Unsupervised and Physics-Informed Machine learning in Geosciences, Baylor University, Texas, 2021. PDF
V.V. Vesselinov, et al., Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning, Stanford Geothermal Workshop, 2020. PDF
V.V. Vesselinov, et al., Machine learning for geothermal resource analysis and exploration, XXIII International Conference on Computational Methods in Water Resources (CMWR), Stanford, CA, 2020. PDF
V.V. Vesselinov, Predicting oil and gas production from unconventional tight-rock reservoirs using machine learning, XXIII International Conference on Computational Methods in Water Resources (CMWR), 2020. PDF
M. Mudunuru, V.V. Vesselinov, et al., Site-Scale and Regional-Scale Modeling for Geothermal Resource Analysis and Exploration, Geothermal Workshop, Stanford, CA, 2020. PDF
V.V. Vesselinov, Unsupervised and Physics-Informed Machine Learning Analyses for Characterization of Energy Production from Unconventional Reservoirs, Machine Learning in Oil & Gas Conference, 2020. PDF
V.V. Vesselinov, Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data, Bureau of Economic Geology, University of Austin, Texas, 2020. PDF
V.V. Vesselinov, Machine learning analyses for characterization of oil, gas and water production from unconventional tight-rock reservoirs, AGU Fall Meeting, 2019. PDF
V.V. Vesselinov, Machine Learning Analyses of Climate Data and Models, 11th World Congress of European Water Resources Association (EWRA), Madrid, Spain, 2019. PDF
V.V. Vesselinov, Novel Unsupervised Machine Learning Methods for Data Analytics and Model Diagnostics, Machine Learning in Solid Earth Geoscience, Santa Fe, 2019. PDF
V.V. Vesselinov, Physics-Informed Machine Learning Methods for Data Analytics and Model Diagnostics, M3 NASA DRIVE Workshop, Los Alamos, 2019. PDF
V.V. Vesselinov, Unsupervised and Physics-Informed Machine Learning of Big Data, Workshop: Applications of Big Data and High-Performance Computing in Earth Sciences, AGU Fall Meeting, San Francisco, CA (invited), 2019. PDF
V.V. Vesselinov, Unsupervised Machine Learning Methods for Feature Extraction, New Mexico Big Data & Analytics Summit, 2019. PDF
V.V. Vesselinov, Unsupervised Machine Learning: Nonnegative Matrix Tensor Decompositions, MIT, Boston, MA, 2019. PDF
V.V. Vesselinov, Novel Machine Learning Methods for Extraction of Features Characterizing Complex Datasets and Models, Recent Advances in Machine Learning and Computational Methods for Geoscience, Institute for Mathematics and its Applications, University of Minnesota, 2018. PDF
V.V. Vesselinov, Novel Machine Learning Methods for Extraction of Features Characterizing Datasets and Models, AGU Fall meeting, 2018. PDF
V.V. Vesselinov, D. O'Malley, B. Alexandrov, Novel Robust Machine Learning Methods for Identification and Extraction of Unknown Features in Complex Real-world Data Sets, Society for Industrial and Applied Mathematics (SIAM) Uncertainty Quantification, Garden Grove, CA (invited), 2018
V.V. Vesselinov, M. Mudunuru, S. Karra, D. O'Malley, B. Alexandrov, Unsupervised Machine Learning Based on Non-negative Tensor Factorization for Analysis of Field Data and Simulation Outputs, Computational Methods in Water Resources (CMWR), Saint-Malo, France, 2018. PDF
V.V. Vesselinov, M. Mudunuru, S. Karra, D. O'Malley, B. Alexandrov, Unsupervised Machine Learning based on Nonnegative Matrix/Tensor Factorization, World Congress on Computational Mechanics (WCCM) (invited), 2018
V.V. Vesselinov, D. O'Malley, B. Alexandrov, Unsupervised Machine Learning Based on Tensor Factorization, International Society for Porous Media (INTERPORE), 2018. PDF
V.V. Vesselinov, D. O'Malley, D. Katzman, Decision Analyses for Groundwater Remediation, Waste Management Symposium, 2017. PDF
Y. Lin, V.V. Vesselinov, D. O'Malley, B. Wohlberg, Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting, SIAM Conference on Computational Science and Engineering, 2017. PDF
D. O'Malley, V.V. Vesselinov, B. Alexandrov, L. Alexandrov, Nonnegative/binary matrix factorization with a D-Wave quantum annealer, DOE LANL Presentation, 2017. PDF
D. O'Malley, V.V. Vesselinov, Quo vadis: Hydrologic inverse analyses using high-performance computing and a D-Wave quantum annealer, AGU Fall Meeting, 2017. PDF
V.V. Vesselinov, D. O'Malley, B. Alexandrov, Uncertainty quantification and experimental design based on unsupervised machine learning identification of contaminant sources and groundwater types using hydrogeochemical data, AGU Fall Meeting, 2017. PDF
J. He, S. Hansen, V.V. Vesselinov, Analysis of Hydrologic Time Series Reconstruction Uncertainty due to Inverse Model Inadequacy, AGU Fall Meeting, 2016. PDF
X. Zhang, V.V. Vesselinov, Bi-Level Decision Making for Supporting Energy and Water Nexus, AGU Fall Meeting, 2016. PDF
D. O'Malley, V.V. Vesselinov, Groundwater Remediation using Bayesian Information-Gap Decision Theory, AGU Fall Meeting, 2016. PDF
Y. Lin, V.V. Vesselinov, D. O'Malley, B. Wohlberg, Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting, AGU Fall Meeting, 2016. PDF
Z. Lu, V.V. Vesselinov, H. Lei, Identifying Aquifer Heterogeneities using the Level Set Method, AGU Fall Meeting, 2016. PDF
V.V. Vesselinov, D. O'Malley, Model Analyses of Complex Systems Behavior using MADS, AGU Fall Meeting, 2016. PDF
S. Hansen, C. Haslauer, O. Cirpka, V.V. Vesselinov, Prediction of Breakthrough Curves for Conservative and Reactive Transport, AGU Fall Meeting, 2016. PDF
V.V. Vesselinov, D. O'Malley, B. Alexandrov, Reduced Order Models for Decision Analysis and Upscaling of Aquifer Heterogeneity, AGU Fall Meeting (invited), 2016. PDF
V.V. Vesselinov, D. O'Malley, D. Katzman, ZEM: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making, Waste Management Symposium, 2016. PDF
V.V. Vesselinov, D. O'Malley, D. Katzman, Model-Assisted Decision Analyses Related to a Chromium Plume at Los Alamos National Laboratory, Waste Management Symposium, 2015. PDF
J. Bakarji, D. O'Malley, V.V. Vesselinov, A Social Dynamics Dependent Water Supply Well Contamination Model, DOE LANL Presentation, 2014. PDF
D. O'Malley, V.V. Vesselinov, Bayesian Information-Gap Decision Analysis Applied to a Geologic CO2 Sequestration Problem, AGU Fall Meeting, 2014. PDF
V.V. Vesselinov, B. Alexandrov, Model-free Source Identification, AGU Fall Meeting, 2014. PDF
J. Cushman, V.V. Vesselinov, D. O'Malley, Random dispersion coefficients and Tsallis entropy, AGU Fall Meeting, 2014. PDF
V.V. Vesselinov, D. Katzman, D. Broxton, K. Birdsell, S. Reneau, D. Vaniman, P. Longmire, J. Fabryka-Martin, J. Heikoop, M. Ding, D. Hickmott, E. Jacobs, T. Goering, D. Harp, P. Mishra, Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory (LANL), Waste Management Symposium, 2013. PDF
D. O'Malley, V.V. Vesselinov, What Matters When and Where For Anomalous Dispersion/Diffusion, AGU Fall Meeting, 2013. PDF
V.V. Vesselinov, et al., AGNI: Coupling Model Analysis Tools and High-Performance Subsurface Flow and Transport Simulators for Risk and Performance Assessments, XIX International Conference on Computational Methods in Water Resources (CMWR 2012), 2012. PDF
V.V. Vesselinov, D. Harp, D. Katzman, Model-driven decision support for monitoring network design based on analysis of data and model uncertainties: methods and applications, AGU Fall meeting (invited), 2012. PDF
L. Leif Zinn-Bjorkman, V.V. Vesselinov, Numerical Optimization using the Levenberg-Marquardt Algorithm, DOE LANL Presentations, 2011. PDF
D. Harp, V.V. Vesselinov, Recent developments in MADS algorithms: ABAGUS and Squads, DOE LANL Presentations, 2011. PDF
V.V. Vesselinov, et al., Environmental Management Modeling Activities at Los Alamos National Laboratory (LANL), Department of Energy Technical Exchange Meeting, Performance Assessment Community of Practice, Hanford, 2010. PDF
V.V. Vesselinov, D. Harp, R. Koch, K. Birdsell, K. Katzman, Tomographic inverse estimation of aquifer properties based on pressure variations caused by transient water-supply pumping, AGU Meeting, 2008. PDF
V.V. Vesselinov, Uncertainties in Transient Capture-Zone Estimates, Conference on Computational Methods in Water Resources (CMWR), Copenhagen, Denmark, 2006. PDF
OMalley Vesselinov Alexandrov 2018 Unsupervised Machine Learning Based on Tensor Factorization interpore18 (2018) PDF
vesselinov LA UR 12 22187 agni cmwr 20120614 PDF
vesselinov wmsym20130304splitanimations LA UR 13 21534 PDF