Presentations & Lectures

• Kliphuis, T.L., Bluehouse, M., Vesselinov, V.V., Stanford Geothermal Workshop, Stanford, CA, February 6-8 (2023) PDF
• Vesselinov, V.V., Kliphuis, T.L., ChemML: Understanding groundwater flow and contaminant transport using machine learning, Frontiers in Hydrology Meeting, American Geophysical Union, San Juan, Puerto Rico, June 19-24 (2022) PDF
• Vesselinov, V.V., Kliphuis, T.L., Novel machine learning methods and tools for geothermal and geochemical problems, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16 (2022) PDF
• Vesselinov, V.V., Kliphuis, T.L., Physics-Informed Machine Learning of Geothermal, Geomechanical, Geochemical Process, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16 (2022) PDF
• Fleming, W.W., Vesselinov, V.V., Goodbody, A.G., 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, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16 (2022) PDF
• Jafarov, E.E., Genet. H., Vesselinov, V.V., Briones, V., Rutter, R., Rogers, B.M., Natali, S., Toward Automated Data-Model Calibration for the Arctic Terrestrial Ecosystem Model, American Geophysical Union, Fall Meeting, Chicago, IL, December 12-16 (2022) PDF
• Vesselinov, V.V., 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
• Vesselinov, V.V., et al., GeoThermalCloud: Fusion of Big Data and Multi-Physics Models, JuliaCon, Boston, MA, July 28-30 (2021) PDF
• Vesselinov, V.V., et al., Hidden geothermal signatures of the southwest New Mexico, World Geothermal Congress, Reykjavik, Iceland, May-Oct (2021)
• Vesselinov, V.V., et al., Machine Learning to Characterize the State of Stress and its Influence on Geothermal Production, Geothermal Rising Conference, San Diego, CA, October 3-6 (2021)
• Vesselinov, V.V., et al., ML4Geo: Machine Learning for Geosciences, JuliaCon, Boston, MA, July 28-30 (2021) PDF
• Vesselinov, V.V., et al., SmartTensors: Unsupervised Machine Learning, JuliaCon, Boston, MA, July 28-30 (2021) PDF
• Vesselinov, V.V., et al., Unsupervised and Physics-Informed Machine learning in Geosciences, Baylor University, Texas (2021) PDF
• Vesselinov, V.V., et al., Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning, Geothermal Workshop, Stanford, CA (2020) PDF
• Vesselinov, V.V., et al., Machine learning for geothermal resource analysis and exploration, XXIII International Conference on Computational Methods in Water Resources (CMWR), Stanford, CA, December 13-15 (2020) PDF
• Vesselinov, V.V., Predicting oil and gas production from unconventional tight-rock reservoirs using machine learning, XXIII International Conference on Computational Methods in Water Resources (CMWR), Stanford, CA, December 13-15 (2020) PDF
• Mudunuru, M.K, Vesselinov, V.V., et al., Site-Scale and Regional-Scale Modeling for Geothermal Resource Analysis and Exploration, Geothermal Workshop, Stanford, CA (2020) PDF
• Vesselinov, V.V., Unsupervised and Physics-Informed Machine Learning Analyses for Characterization of Energy Production from Unconventional Reservoirs, Machine Learning in Oil & Gas Conference (2020) PDF
• Vesselinov, V.V., Unsupervised and Physics-Informed Machine Learning of Big and Noisy Data, Bureau of Economic Geology, University of Austin, Texas (2020) PDF
• Vesselinov, V.V., Machine learning analyses for characterization of oil, gas and water production from unconventional tight-rock reservoirs, AGU Fall Meeting, San Francisco, CA (2019) PDF
• Vesselinov, V.V., Machine Learning Analyses of Climate Data and Models, 11th World Congress of European Water Resources Association (EWRA), Madrid, Spain (2019) PDF
• Vesselinov, V.V., Novel Unsupervised Machine Learning Methods for Data Analytics and Model Diagnostics, Machine Learning in Solid Earth Geoscience, Santa Fe (2019) PDF
• Vesselinov, V.V., Physics-Informed Machine Learning Methods for Data Analytics and Model Diagnostics, M3 NASA DRIVE Workshop, Los Alamos (2019) PDF
• Vesselinov, V.V., 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
• Vesselinov, V.V., Unsupervised Machine Learning Methods for Feature Extraction, New Mexico Big Data & Analytics Summit, nmbdas.com, Albuquerque (2019) PDF
• Vesselinov, V.V., Unsupervised Machine Learning: Nonnegative Matrix Tensor Decompositions, MIT, Boston, MA (2019) PDF
• Vesselinov, V.V., 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) DOI PDF
• Vesselinov, V.V., Novel Machine Learning Methods for Extraction of Features Characterizing Datasets and Models, AGU Fall meeting, Washington D.C. (2018) PDF
• Vesselinov, V.V., O'Malley, D., Alexandrov, B., 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)
• Vesselinov, V.V., Mudunuru. M., Karra, S., O'Malley, D., Alexandrov, Unsupervised Machine Learning Based on Non-negative Tensor Factorization for Analysis of Filed Data and Simulation Outputs, Computational Methods in Water Resources (CMWR), Saint-Malo, France (2018) DOI PDF
• Vesselinov, V.V., Mudunuru. M., Karra, S., O'Malley, D., Alexandrov, Unsupervised Machine Learning based on Nonnegative Matrix/Tensor Factorization, World Congress on Computational Mechanics (WCCM), New York, NY (invited) (2018)
• Vesselinov, V.V., O'Malley, D., Alexandrov, B., Unsupervised Machine Learning Based on Tensor Factorization, International Society for Porous Media (INTERPORE), New Orleans, LA (2018) DOI PDF
• Vesselinov, V.V., O'Malley, D., Katzman, D., Decision Analyses for Groundwater Remediation, Waste Management Symposium, Phoenix, AZ (2017) DOI PDF
• Lin, Y., Vesselinov, V.V., O'Malley, D., Wohlberg, B., Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting, SIAM Conference on Computational Science and Engineering, Atlanta, GA (2017) PDF
• O'Malley, D., Vesselinov, V.V., Alexandrov, B.S., Alexandrov, L.B., Nonnegative/binary matrix factorization with a D-Wave quantum annealer (2017) PDF
• O'Malley, D., Vesselinov, V.V., Quo vadis: Hydrologic inverse analyses using high-performance computing and a D-Wave quantum annealer, AGU Fall Meeting, New Orleans, LA (2017) DOI PDF
• Vesselinov, V.V., O'Malley, D., Alexandrov, B., Uncertainty quantification and experimental design based on unsupervised machine learning identification of contaminant sources and groundwater types using hydrogeochemical data, AGU Fall Meeting, New Orleans, LA (2017) PDF
• He, J., Hansen, S.K., Vesselinov, V.V., Analysis of Hydrologic Time Series Reconstruction Uncertainty due to Inverse Model Inadequacy, AGU Fall Meeting, San Francisco, CA (2016) PDF
• Zhang, X., Vesselinov, V.V., Bi-Level Decision Making for Supporting Energy and Water Nexus, AGU Fall Meeting, San Francisco, CA (2016) PDF
• Lin, Y., Vesselinov, V.V., O'Malley, D., Wohlberg, B., Hydraulic Inverse Modeling using Total-Variation Regularization with Relaxed Variable-Splitting, AGU Fall Meeting, San Francisco, CA (2016) PDF
• Lu, Z., Vesselinov, V.V., Lei, H., Identifying Aquifer Heterogeneities using the Level Set Method, AGU Fall Meeting, San Francisco, CA (2016) PDF
• Vesselinov, V.V., O'Malley, D., Model Analyses of Complex Systems Behavior using MADS, AGU Fall Meeting, San Francisco, CA (2016) DOI PDF
• Hansen, S.K., Haslauer, C.P., Cirpka, O.A., Vesselinov, V.V., Prediction of Breakthrough Curves for Conservative and Reactive Transport, AGU Fall Meeting, San Francisco, CA (2016) PDF
• Vesselinov, V.V., O'Malley, D., Alexandrov, B., Reduced Order Models for Decision Analysis and Upscaling of Aquifer Heterogeneity, AGU Fall Meeting, San Francisco, CA (invited) (2016) PDF
• Vesselinov, V.V., O'Malley, D., Katzman, D., ZEM: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making, Waste Management Symposium, Phoenix, AZ (2016) DOI PDF
• Vesselinov, V.V., O'Malley, D., Katzman, D., Model-Assisted Decision Analyses Related to a Chromium Plume at Los Alamos National Laboratory, Waste Management Symposium, Phoenix, AZ (2015) DOI PDF
• Bakarji, J., O'Malley, D., Vesselinov, V.V., A Social Dynamics Dependent Water Supply Well Contamination Model, LANL Postdoc Research Conference (2014) DOI PDF
• O'Malley, D., Vesselinov, V.V., Bayesian Information-Gap (BIG) Decision Analysis Applied to a Geologic CO2 Sequestration Problem, AGU Fall Meeting, San Francisco, CA (2014) PDF
• Vesselinov, V.V., Alexandrov, B.A, Model-free Source Identification, AGU Fall Meeting, San Francisco, CA (2014) PDF
• Cushman, J.H., Vesselinov, V.V., O'Malley, D., Random dispersion coefficients and Tsallis entropy, AGU Fall Meeting, San Francisco, CA (2014) PDF
• Vesselinov, V.V., Katzman, D., Broxton, D., Birdsell, K., Reneau, S., Vaniman, D., Longmire, P., Fabryka-Martin, J., Heikoop, J., Ding, M., Hickmott, D., Jacobs, E., Goering, T., Harp, D., Mishra, P., Data and Model-Driven Decision Support for Environmental Management of a Chromium Plume at Los Alamos National Laboratory (LANL), Session 109: Environmental Restoration Challenges: Alternative Approaches for Achieving End State, Waste Management Symposium, Phoenix, AZ, February 28 (2013) DOI PDF
• Vesselinov, V.V., 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), University of Illinois at Urbana-Champaign, June 17-22 (2012) PDF
• Vesselinov, V.V., Harp, D., Katzman, D., Model-driven decision support for monitoring network design based on analysis of data and model uncertainties: methods and applications, H32F: Uncertainty Quantification and Parameter Estimation: Impacts on Risk and Decision Making, AGU Fall meeting, San Francisco, December 3-7 (invited) (2012) DOI PDF
• Leif Zinn-Bjorkman, L., Numerical Optimization using the Levenberg-Marquardt Algorithm, EES-16 Seminar Series, LA-UR-11-12010 (2011) DOI PDF
• Harp, D., Vesselinov, V.V., Recent developments in MADS algorithms: ABAGUS and Squads, EES-16 Seminar Series, LA-UR-11-11957 (2011) DOI PDF
• Vesselinov, V.V., et al., Environmental Management Modeling Activities at Los Alamos National Laboratory (LANL), Department of Energy Technical Exchange Meeting, Performance Assessment Community of Practice, Hanford, April 13-14 (2010) DOI PDF
• Vesselinov, V.V., Harp, D., Koch, R., Birdsell, K., Katzman, K., Tomographic inverse estimation of aquifer properties based on pressure variations caused by transient water-supply pumping, AGU Meeting, San Francisco, CA, December 15-19 (2008) DOI PDF
• Vesselinov, V.V., Uncertainties in Transient Capture-Zone Estimates, CMWR 2006 XVI International Conference on Computational Methods in Water Resources, Copenhagen, Denmark, June 18-22 (2006) DOI PDF