The Grainger College of Engineering
Civil & Environmental Engineering

Alexandre Tartakovsky

Alexandre Tartakovsky
Alexandre Tartakovsky
  • Professor
(217) 300-2249
3026 Civil Eng Hydrosystems Lab

Primary Research Area

  • Water Resources Engineering and Science

Research Areas

Education

  • M.Sc., Department of Mathematics and Mechanics, Kazan State University, Russia, Applied Mathematics/Fluid Mechanics (1994)
  • Ph.D., Department of Hydrology and Water Resources, The University of Arizona, Tucson, Hydrology (2002)

Academic Positions

  • Associate Professor, University of South Florida, School of Geosciences, Department of Mathematics and Statistics, Tampa, FL (2013-2014)
  • Professor, University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering, Urbana, IL (2019-present)

Other Professional Employment

  • Graduate Research Assistant/Associate, Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ. (1998-2002)
  • Post Doctoral Researcher, Idaho National Engineering and Environmental Laboratory, Subsurface Science Initiative, Idaho Falls, ID. (2002-2004)
  • Scientist, Pacific Northwest National Laboratory, Computational Science and Mathematics, Richland, WA. (2004-2014)
  • Associate Division Director for Computational Mathematics, Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, Richland, WA. (2014-2019)

Professional Societies

  • Society for Industrial and Applied Mathematicians (SIAM)
  • American Geophysical Union (AGU)

Service on University Committees

  • Dept representative to the Faculty Senate

Research Interests

  • Inference abd parameter estimation
  • Multiscale modeling
  • Uncertainty quantification
  • Physics-informed machine learning
  • Pore-scale multiphase flow and reactive transport
  • Subsurface flow and transport

Research Statement

Tartakovsky's research interests are at the intersection of flow and transport of contaminants, groundwater-surface water interactions and scientific machine learning.

Primary Research Area

  • Water Resources Engineering and Science

Research Areas

Chapters in Books

Monographs

Selected Articles in Journals

Articles in Conference Proceedings

Abstracts (in print or accepted)

  • M Reyna, AM Tartakovsky Physics-Informed Radial Basis Function Method for Transport Processes, AGU Fall Meeting Abstracts 2022, H35H-1207
  • AM Tartakovsky, YH Yeung, DA Barajas-Solano, Physics-informed Gaussian process regression model for parameter estimation in groundwater models, AGU Fall Meeting Abstracts 2022, U32A-08
  • M Aghababaei, TR Ginn, J McCallum, R Gonzalez-Pinzon, KC Carroll, ..., Hyporheic zone exchange and solute transport including memory functions for the river, hyporheic zone, and a boundary layer, AGU Fall Meeting Abstracts 2022, B22F-1503
  • C Shen, HV Gupta, T Bandai, D Kifer, AM Tartakovsky, D Feng, Differentiable modeling in Geosciences-Breaking down the imaginary barrier between machine learning and process-based modeling with differentiable modeling, AGU Fall Meeting Abstracts 2022, H45L-1535
  • J Burghardt, T Bao, A Tartakovsky, K Xu, E Darve, Autonomous Inversion of in Situ Deformation Measurement Data for Injection-Induced Stress Change, ARMA US Rock Mechanics/Geomechanics Symposium, ARMA-2023-0773
  • AM Tartakovsky, C Munoz Ono, JL McCreight, Y Wang, JD Hughes, ...,Karhunen-Loeve-Deep Neural Network Surrogate Models for Dynamic Simulations and History Matching, AGU Fall Meeting Abstracts 2023 (2017), H33Q-2017
  • DA Barajas-Solano, YH Yeung, AM Tartakovsky, Conditional parameterizations for low-dimensional surrogate modeling with application to high-dimensional problems in hydrogeology, AGU Fall Meeting Abstracts 2023 (2012), H33Q-2012
  • YH Yeung, DA Barajas-Solano, Y Zong, AM Tartakovsky, Conditional Karhunen-Loève model inversion algorithms for large-scale data assimilation AGU Fall Meeting Abstracts 2023 (2008), H33Q-2008
  • W Hongsheng, SA Hosseini, AM Tartakovsky, J Leng, M Fan, A Deep Learning-Based Workflow for Fast Prediction of 3D State Variables in Geological Carbon Storage: A Dimension Reduction Approach AGU23
  • AM Tartakovsky, Y Wang, Y Zong, Approximate Bayesian Deep Learning Surrogate Model for Parameter and State Estimation AGU24
  • J Zeng, Y Wang, AM Tartakovsky, DA Barajas-Solano, Solving High-dimensional Inverse Problems Using Amortized Likelihood-Free Inference and Karhunen–Loève Expansions AGU24
  • Tartakovsky, A.M. and Barajas-Solano, D.A., 2019, December. Model inversion via conditioned Karhunen-Loève expansions. In AGU Fall Meeting Abstracts (Vol. 2019, pp. H31K-1864).
  • Kordilla, J., Dentz, M. and Tartakovsky, A., 2020, May. Partitioning of preferential flows in fracture networks: Smoothed Particle Dynamics simulations and analytical modeling of infiltration dynamics. In EGU General Assembly Conference Abstracts (p. 22595).
  • He, Q. and Tartakovsky, A.M., 2020, December. Applying Convex Weighting Physics-Informed Neural Networks to Subsurface Modeling and Characterization Problems. In AGU Fall Meeting Abstracts (Vol. 2020, pp. H036-0003).
  • Kordilla, J., Dentz, M. and Tartakovsky, A.M., 2021, April. Flow partitioning in partially saturated fracture networks: Relation between dispersive properties and internal fracture geometry. In EGU General Assembly Conference Abstracts (pp. EGU21-12255).
  • Tartakovsky, A.M., Yeung, Y.H. and Barajas-Solano, D.A., 2021, December. Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems. In AGU Fall Meeting 2021. AGU.
  • Kohanpur, A.H., Tartakovsky, A.M., Saksena, S., Dey, S., Johnson, M., Yeghiazarian, L. and Riasi, M.S., 2021, December. Parametric Uncertainty Quantification in Urban Flooding Models. In AGU Fall Meeting 2021. AGU.

Invited Lectures

  • Mathematics of Digital Twins and Transfer Learning
  • Approximate Bayesian Deep Learning Surrogate Model for Parameter and State Estimation

Honors

  • George E. P. Smith Graduate Fellowship for AY2000-2001, University of Arizona. (2000)
  • 2001-2002 John and Margaret Harshbarger Doctoral Fellow in Hydrology and Water Resources, University of Arizona. (2001)
  • Outstanding Performance Award, Computational and Information Science Directorate, PNNL (2005)
  • Department of Energy (DOE) Office of Science Early Career Award in Science and Engineering. (2008)
  • Presidential Early Career Award for Scientists and Engineers. (2008)
  • U.S. Department of Energy (DOE) Office of Science Early Career Research Program Award. (2011)

Other Honors

  • National Academy of Sciences Kavli Frontiers of Science Alumni, 2016 German American Frontiers (2016)