Harry Lee - News

[10/15/2024] Our book chapter “Bayesian Inference in Geomechanics” published in Machine Learning in Geomechanics 2: Data‐Driven Modeling, Bayesian Inference, Physics‐ and Thermodynamics‐based Artificial Neural Networks and Reinforcement Learning

[9/3/2024] Our manuscript “CO2 Storage Site Characterization using Ensemble-based Approaches with Deep Generative Models” accepted in Journal of Petroleum Science and Engineering

[7/22/2024] WCCM-PANACM 2024 Deep neural operators for data-driven modeling of multiphysics coastal hydrodynamics

[7/8/2024] SIAM Annual Meeting 2024 Deep Neural Operators for Multiphysics Coastal Hydrodynamics

[6/26/2024] “Modeling upscaled mass discharge from complex DNAPL source zones using a Bayesian Neural Network: prediction accuracy, uncertainty quantification and source zone feature importance” published in Water Resources Research

[6/24/2024] ARMA 2024 Poster579 Enhanced Geothermal Site Characterization using Generative Adversarial Network and Ensemble Method

[5/29/2024] “Blending Bathymetry: Combination of image-derived parametric approximations and celerity data sets for nearshore bathymetry estimation” accepted in Coastal Engineering

[5/10/2024] “A first look at river discharge from SWOT satellite observations” posted on ESS Open Archive

[5/9/2024] “Multi-fidelity Hamiltonian Monte Carlo” posted on Arxiv

[5/1/2024] Lee is selected as NREL Faculty-Applied Clean Energy Sciences (FACES) Program fellow

[2/28/2024] SIAM UQ 2024 Deep Learning-Based Data Assimilation and Uncertainty Quantification with Generative Priors and Multimodal Sensing Data in Subsurface Systems

[2/22/2024] AGU OSM 2024 OT42B-06 Bathymetry estimation from satellite-based optical video using a data fusion approach

[12/13/2023] AGU Fall Meeting H31I-06 Overcoming Challenges of Practical Bayesian Inference: Black-box Forward Models meet Black-box Variational Inference

[12/13/2023] AGU Fall Meeting H31I-04 Latent Space Neural Operators for Hydrodynamic Modeling

[12/13/2023] AGU Fall Meeting Session H31I Scientific Machine Learning for Flow, Transport, and Coupled Processes

[12/13/2023] AGU Fall Meeting H33R-2027 Characterizing Carbon Storage System Using Patch Diffusion Model and Bayesian Inversion

[12/13/2023] AGU Fall Meeting H33R-2028 CO2 Sequestration Site Characterization using Deep Generative Prior-based Inverse Modeling Framework

[11/03/2023] Our conference paper “Improved Black-box Variational Inference for High-dimensional Bayesian Inversion involving Black-box Simulators” accepted in NeurIPS 2023 Workshop Deep Learning and Inverse Problems

[11/02/2023] “Blending Bathymetry: Combination of image-derived parametric approximations and celerity data sets for nearshore bathymetry estimation” posted on arXiv

[10/23/2023] ACM2023 Overcoming challenges of practical Bayesian inference: black box forward models meet blackbox variational inference

[10/02/2023] “Subsurface Characterization using Ensemble-based Approaches with Deep Generative Models” posted in arXiv

[09/11/2023] Lee is selected as a NSF-NASA EPSCoR Research Fellow

[09/04/2023] “Evolution of plume geometry, dilution and reactive mixing in porous media under highly transient flow fields at the surface water-groundwater interface” published in Journal of Contaminant Hydrology

[07/28/2023] SynS & ML Workshop @ ICML 2023 accepted paper “Coupling Self-Attention Generative Adversarial Network and Bayesian Inversion for Carbon Storage System”

[07/28/2023] SynS & ML Workshop @ ICML 2023 accepted paper “Estimation of Physical Coefficients for CO_2 Sequestration using Deep Generative Priors based Inverse Modeling Framework”

[06/27/2023] ARMA 57th US Rock Mechanics/Geomechanics Symposium “Subsurface Characterization using Bayesian Deep Generative Prior-based Inverse Modeling for Utah FORGE Enhanced Geothermal System”

[04/22/2023] Our paper “Improved methodology for deep aquifer characterization using hydrogeological, self-potential, and magnetotellurics data” posted in arXiv

[02/27/2023] Our paper “Using Deep Learning to Model the Groundwater Tracer Radon in Coastal Waters” accepted in Water Resources Research

[12/14/2022] AGU 2022 GC34C-02 - Natural gas fugitive leak detection and quantification using a continuous methane emission monitoring system and a simplified model

[12/13/2022] AGU 2022 Natural gas fugitive leak detection and quantification using a continuous methane emission monitoring system and a simplified model

[12/12/2022] AGU 2022 H16H Scientific Machine Learning for Flow, Transport, and Coupled Processes IV Oral

[12/12/2022] AGU 2022 H15H Scientific Machine Learning for Flow, Transport, and Coupled Processes III Oral

[12/12/2022] AGU 2022 H12Q Scientific Machine Learning for Flow, Transport, and Coupled Processes II Poster

[12/11/2022] AGU 2022 H11J Scientific Machine Learning for Flow, Transport, and Coupled Processes I Poster

[12/11/2022] AGU 2022 H11J-02 - Subsurface Characterization using Deep Generative Adversarial Networks in a Bayesian Inverse Modeling

[12/9/2022] ICCE 2022 Scalable real-time data assimilation with various data types for accurate spatiotemporal nearshore bathymetry estimation

[10/26/2022] GHGT-16 Deep learning-based data assimilation in the latent space for real-time forecasting of geologic carbon storage

[10/05/2022] Our paper “Integration of deep learning-based inversion and upscaled mass-transfer model for DNAPL mass-discharge estimation and uncertainty assessment” accepted in Water Resources Research

[10/04/2022] Our paper “Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry” accepted in Advances in Water Resources

[09/19/2022] 49th IAH Congress Topic 10 Session Invited Talk: Subsurface Characterization using Generative Models

[09/16/2022] CEE Department Graudate Seminar: Machine Learning in Civil and Environmental Engineering

[08/18/2022] USACM UQ-MLIP Multifidelity Monte-Carlo Sampling in Mechanics

[08/14/2022] Book Chapter: Machine Learning and Projection-Based Model Reduction in Hydrology and Geosciences in Knowledge-Guided Machine Learning

[08/11/2022] KSIAM-MINDS-NIMS ICMLP Uncertainty Quantification using Generative Models for Geoscience Applications

[08/01/2022] WCCM-APCOM 2022 Multi-fidelity Hamiltonian Monte Carlo with Deep Learning-based Surrogate

[07/12/2022] ICCFD11 Development of the PISALE Codebase for Simulating Flow and Transport in Large-scale Coastal Aquifer

[06/26/2022] ARMA 56th US Rock Mechanics Geomechanics Symposium Workshop: Subsurface Characterization using Generative Models

[06/21/2022] CMWR 2022 A Multi-Fidelity Hamiltonian Monte Carlo Method with Deep Learning-Based Surrogate for Subsurface Flow

[05/31/2022] InterPore 2022 CO2 Storage Site Characterization using Deep Generative Models

[04/13/2022] 2022 SIAM UQ Multi-Fidelity Hamiltonian Monte Carlo Method with Deep Learning-Based Surrogate

[04/04/2022] 17th Copper Mountain Conference on Interative Methods “Bayesian Level Set Approach for Inverse Problems with Piecewise Constant Reconstructions”

[03/31/2022] AAPG-CCUG Session 10 Physics-based Deep Learning Driven CO2 Flow Modeling and Data Assimilation for Real-Time Forecasting

[03/17/2022] New paper “Inference of young groundwater ages and modern groundwater proportions using chlorofluorocarbon and tritium/helium-3 tracers from West Hawai‘i Island” accepted in Journal of Hydrology

[03/07/2022] Harry Lee was selected as 2022 Google Cloud Research Innovator

[03/03/2022] AGU OSM 2022 CP12 Bathymetry Blending using cBathy and Parametric Beach Tool

[03/03/2022] AGU OSM 2022 CP08 Single Flight Littoral Bathymetry Estimation from Infrared Imagery

[03/02/2022] AGU OSM 2022 CP12 Sensitivity Analysis of a Parametric Model for Barred Equilibrium Beaches

[02/16/2022] New paper “Optimizing automated kriging to improve spatial interpolation of monthly rainfall over complex terrain” accepted in Journal of Hydrometorology

[02/01/2022] New arXiv manuscript on fast 3D Electrical Resistivity Tomography

[12/16/2021] AGU Fall Meeting H45I-1280 - Development of the PISALE Codebase for Coastal Aquifer Management

[12/15/2021] AGU Fall Meeting H35S-1254 - Deep learning-based estimation of riverine bathymetry using Variational Encoder Geostatistical Approaches (VEGAs)

[12/13/2021] AGU Fall Meeting H15O-1226 - Subsurface Characterization using Deep Learning Approaches

[12/13/2021] AGU Fall Meeting H15O-1217 - Geothermal Reservoir Characterization using Deep Learning Based Inversion Approach

[12/13/2021] AGU Fall Meeting H12E-06 Efficient Stochastic Subsurface Inversion using Deep Generative Modeling and Multi-fidelity Importance Sampling

[12/13/2021] AGU Fall Meeting H12E - Scientific Machine Learning Methods for Understanding Coupled Processes and Material Properties in Heterogeneous Porous Media Across Scales I

[12/13/2021] AGU Fall Meeting H150 - Scientific Machine Learning Methods for Understanding Coupled Processes and Material Properties in Heterogeneous Porous Media Across Scales II

[11/30/2021] Our new article “Bayesian Level Set Approach for Inverse Problems with Piecewise Constant Reconstructions” on arXiv

[11/24/2021] Our new article “Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry” on arXiv

[11/6/2021] “Multi-Fidelity Hamiltonian Monte Carlo Method with Deep Learning-based Surrogate” presented in AAAI 2021 Fall Symposium on Science-guided AI

[11/4-6/2021] AAAI 2021 Fall Symposium on Science-guided AI

[10/5/2021] Our paper “A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks” accepted in Nature Computational Science

[09/22/2021] Combining ML with Physics Information in Water Resources Engineering

[08/12/2021] Our paper “A density-dependent multi-species model to assess groundwater flow and nutrient transport in the coastal Keauhou aquifer, Hawaii, USA” accepted in Hydrogeology Journal

[08/10/2021] KGML2021 ML-based scalable data assimilation with hydrological applications

[08/09/2021] Our paper “Fast and Scalable Earth Texture Synthesis using Spatially Assembled Generative Adversarial Neural Network” accepted in Journal of Contaminant Hydrolgy

[07/22/2021] Our paper “Elastic Full-waveform Inversion using both the Multiparametric Approximate Hessian and the Discrete Cosine Transform” accepted in Transactions on Geoscience and Remote Sensing

[07/19/2021] SIAM AN21 Large-Scale Bathymetry Prediction using Deep Learning Approaches

[07/15/2021] Our paper “A non-Bayesian nonparametric model for characterization of basin-scale aquifers using groundwater level fluctuations” accepted in Journal of Hydrology

[05/27/2021] New arXiv paper “A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks”

[04/16/2021] Our paper “PBBFMM3D: a parallel black-box algorithm for kernel matrix-vector multiplication” accepted in Journal of Parallel and Distributed Computing 

[03/22-24/2021] AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences (AAAI-MLPS)

[03/05/2021] SIAM CSE21 Bayesian Level Set Approaches for Inverse Problems with Piecewise Constant Reconstructions

[03/04/2021] SIAM CSE21 Data Assimilation and Inverse Modeling using Variational Supervised-Encoders

[03/04/2021] SIAM CSE21 Bayesian Inference of Nearshore Bathymetry using Deep Learning Techniques

[03/03/2021] SIAM CSE21 Deep Learning for Large-Scale Riverine Surface Flow Velocity Estimation

[03/03/2021] SIAM CSE21 Physics-Guided Machine Learning and Data-Driven Methods in Computational Geoscience - Part I of II

[03/03/2021] SIAM CSE21 Physics-Guided Machine Learning and Data-Driven Methods in Computational Geoscience - Part II of II

[02/18/2021] Our paper “Microfluidic Investigation of Salinity-Induced Oil Recovery in Porous Media during Chemical Flooding” accepted in Energy & Fuels

[01/28/2021] Our paper “Application of deep learning to large scale riverine flow velocity estimation” accepted in Stochastic Environmental Research and Risk Assessment

[01/26/2021] Our paper “Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother” accepted in Water Resources Research

[12/17/2020] Our paper “Connectivity-informed Drainage Network Generation using Deep Convolution Generative Adversarial Networks” accepted in Scientific Reports

[12/14/2020] CMWR2020 Contaminant source characterization: sequential and joint geostatistical inversion and the benefit from a physical based prior

[12/14-17/2020] CMWR2020 Data-centric simulations and modeling

[12/16/2020] AGU2020 H196-0014 Development of the PISALE Codebase for coastal aquifer management

[12/15/2020] AGU2020 H031-0014 Hydraulic conductivity and dispersion upscaling for high-dimensional groundwater flow and transport (click to view our AGU eLightning poster)

[12/15/2020] AGU2020 OS041-11 Upscaling Submarine Groundwater Discharge Using Local Long-Term High-Resolution Radon Measurements and Deep Learning (click to view our AGU eLightning poster)

[12/14/2020] AGU2020 H150-07 Assessment of Modeling Uncertainties for Nutrient Transport Models of the Keauhou Aquifer, Hawai'i (click to view our AGU eLightning poster)

[12/14/2020] AGU2020 NS002-0007 Deep Aquifer Characterization with Magnetotellurics, Self-potential, and Hydrogeological Data Sets (click to view our AGU eLightning poster)

[12/14/2020] AGU2020 EP046-0006 Deep learning application to fast estimation of riverine surface velocity (click to view our AGU eLightning poster)

[12/09/2020] AGU2020 H076-10 Deep Bayesian Techniques to Nearshore Bathymetry with Sparse Measurements (click to view our AGU eLightning poster)

[12/09/2020] AGU2020 H076-01 Realtime forecasting of CO2 flow using variational autoencoder with ensemble-based data assimilation (click to view our AGU eLightning poster)

[12/09/2020] AGU2020 Session H076 Scientific Machine Learning for Flow, Transport, and Coupled Processes Across Temporal and Spatial Scales III eLightning

[12/08/2020] AGU2020 H052-10 Improved monitoring of dense non-aqueous phase liquid (DNAPL) source zone remediation through hydrogeophysical inversion using variational autoencoder and Ensemble Kalman filter (click to view our AGU eLightning poster)

[12/08/2020] AGU2020 Session H052 Scientific Machine Learning for Flow, Transport, and Coupled Processes Across Temporal and Spatial Scales II

[12/08/2020] AGU2020 Session H048 Scientific Machine Learning for Flow, Transport, and Coupled Processes Across Temporal and Spatial Scales I

[11/23/2020] 73th APS Division of Fluid Dynamics: Fast solver of the shallow water equations with application to estimation of the riverine surface flow velocity

[11/13-14/2020] AAAI 2020 Fall 2020 Symposium on Physics-guided AI to acclerate scientific discovery, November 13-14, 2020

[10/16/2020] UHM Engineering seminar “Recent developments in fast and scalable inverse modeling and data assimilation in hydrology”

[10/07/2020] Our paper “Bathymetric Inversion and Uncertainty Estimation from Synthetic Surf-Zone Imagery with Machine Learning” accepted in Remote Sensing

[08/21/2020] Call for Papers: AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences (AAAI-MLPS), March 22-24, 2021

[07/30/2020] Our paper “Deep learning technique for fast inference of large-scale riverine bathymetry” accepted in Advances in Water Resources

[07/15/2020] Our paper “Improved characterization of DNAPL source zones via sequential hydrogeophysical inversion of hydraulic-head, self-potential and partitioning-tracer data” accepted in Water Resources Research

[06/29/2020] Our paper “Recent developments in fast and scalable inverse modeling and data assimilation methods in hydrology” accepted in Journal of Hydrology

[06/24/2020] Assessment of Groundwater Ages Using Radiocarbon and Chlorofluorocarbons in West Hawai'i Aquifer Systems

[06/23/2020] Assessing Submarine Groundwater Discharge Dynamics Using Long-Term High-Resolution Radon Measurements and Machine Learning

[06/16/2020] Our paper “Connectivity-informed Drainage Network Generation using Deep Convolution Generative Adversarial Networks” uploaded in arXiv

[04/22/2020] SPIE Defense + Commercial Sensing 2020 Augmenting wave-kinematics algorithms with machine learning to enable rapid littoral mapping and surf-zone state characterization from imagery

[04/01/2020] AAAI-MLPS symposium proceedings published

[03/23-25/2020] AAAI 2020 Spring Symposium on Combining Machine Learning with Physics Sciences (AAAI-MLPS)

[02/25-27/2020] CODA 2020 Scalable spatio-temporal modeling using a fast multipole method for 3D tracer concetnration breakthrough data with magnetic resonace imaging

[02/20/2020] AGU Ocean Sciences 2020 Rapid wave model-based nearshore bathymetry inversion with UAS measurements

[02/20/2020] AGU Ocean Sciences 2020 OD44A-3484 Deep learning techniques for nearshore and riverine bathymetry estimation using water-surface observations

[02/06/2020] Fall 2019 Deep Learning course featured in UH News and Hawaii Data Science Institute

[12/20/2019] CEE696-007 Deep Learning final project presentation

[12/13/2019] AGU2019 T51C-12 Efficient density-driven flow and transport simulation with adaptive mesh refinement using Enriched Galerkin Approach for reliable coastal aquifer management (click to view our AGU eLightning poster!)

[12/13/2019] AGU2019 EP53C-07 Deep learning techniques for riverine bathymetry and flow velocity estimation (click to view our AGU eLightning poster)

[12/13/2019] AGU2019 H51I-1595 Fast and scalable digital rock reconstruction using Spatially Assembled Generative Adversarial Neural Networks (click to download our poster)

[12/13/2019] AGU2019 H53J-1889 Assessment of Hawaiian aquifer systems Using groundwater age tracers and numerical models, Big Island, Hawaii

[12/12/2019] AGU2019 EP43C-04 Applications of Deep Neural Network to near-shore bathymetry with sparse measurements

[12/10/2019] AGU2019 S53D-0485 Two dimensional acoustic full waveform inversion using Discrete Cosine Transform

[12/10/2019] AGU2019 NS23B-0848 Multi-parameter elastic full waveform inversion using Discrete Cosine Transform

[12/10/2019] AGU2019 H21L-1911 Development of a reliable hydraulic conductivity upscaling tool for high dimensional groundwater flow models (click to download our poster)

[12/09/2019] AGU2019 EP13B-05 Rapid wave model-based nearshore bathymetry inversion with UAS measurements (click to download our presentation slides)

[12/03/2019] Awarded $5,000 Google Cloud Platform Research Credits

[11/04/2019] Ike Wai Modeling team interview with South Korea's JIBS TV station for groundwater management in Hawaii and Jeju Island, South Korea

[10/21/2019] CEE691 Seminar “Deep Learning in Civil and Environmental Engineering and Earth Science”

[10/12/2019] Our paper on hydrogeochemical and microbiological effects of aquifer recharge operations was accepted in Chemosphere

[09/20/2019] SEG 2019 Post-Convection Workshop: ML-based Nano-scale Carbonate Rock Reconstruction and Associated Microfluidics Design for EOR and CO2 Sequestration

[09/17/2019] CEE 696-007 Deep Learning awarded $2,000 AWS Educate Credits.

[09/17/2019] UW-Madison Geoscience Department Seminar: Modeling groundwater flow in Hawai'i Island

[09/14-15/2019] AMS Special Session on Uncertainty Quantification Strategies for Physics Applications

[08/05-06/2019] NSF EPSCoR ‘Ike Wai Project Site Visit at UHM (annual report can be found in the project website)

[07/18/2019] ICSC2019 Pacific islands’ desalination aiming for zero-discharge: concepts, designs, and feasibility

[07/14-24/2019] 2019 SAMSI Industrial Math/Stat Modeling Workshop

[05/09/2019] CEE699-007 Deep learning in CEE and Earth Science will be offered in Fall 2019

[04/25/2019] KSEG Spring 2019 Acoustic full waveform inversion using discrete cosine transform

[03/12/2019] Session chair: SIAM GS 2019 minisymposium ‘‘Scalable Methods for Coupling Water Resources Modeling and Parameter Estimation’’ Part II

[03/11/2019] Session chair: SIAM GS 2019 minisymposium ‘‘Scalable Methods for Coupling Water Resources Modeling and Parameter Estimation’’ Part I

[03/13/2019] SIAM GS 2019 Fast and scalable joint subsurface inversion using hydrological-geophysical datasets with a parallel black-box Fast Multipole Method

[03/12/2019] SIAM GS 2019 Robust Parameter Estimation Using Inverse Modeling and Advanced Machine Learning Techniques

[03/11/2019] SIAM GS 2019 A Locally Conservative Enriched Galerkin for Coupling Flow and Transport

[03/06/2019] Our manuscript on parallel black-box Fast Multipole Method was uploaded in arXiv (arXiv:1903.02153)

[02/08/2019] Our paper on nearshore bathymetry estimation was accepted in Journal of Atmospheric and Oceanic Technology

[12/14/2018] AGU 2018: Characterizing pool-dominated DNAPL source zones using hydraulic tomography

[12/14/2018] AGU 2018: Nearshore Bathymetry Estimation using Batch Inversion from UAS Based Observations

[12/13/2018] AGU 2018: The Role of Hydrogeophysics in the ‘Ike Wai Project within a Multi-Disciplinary Approach to Understanding Groundwater in Hawai‘i

[12/12/2018] AGU 2018: 3D Nano-porous Chalk Rock Construction using Deep Convolutional Generative Adversarial Neural Networks

[12/12/2018] AGU 2018: Enriched Galerkin approach for density-driven flow in coastal aquifer

[12/11/2018] AGU 2018: Bathymetry estimation using deep learning techniques

[12/11/2018] AGU 2018: Drainage network generation using Deep Convolutional Generative Adversarial Networks

[12/10/2018] AGU 2018: Groundwater Modeling Challenges Under Limited Field Data in Hawai'i Aquifer Systems

[11/08/2018] CEMC and UHM Site IAB meeting: High-dimensional EM imaging in geoscience and engineering accelerated by a black-box Fast Multipole Method

[11/03/2018] Our paper on pyrite oxidation modeling and experiments was accepted in Applied Geochemistry

[10/24/2018] UHM Applied Math Seminar: A parallel black-box Fast Multipole Method and its applications in spatial interpolation and inverse modeling

[10/16/2018] Harry Lee becomes an associate editor in Journal of Hydrology

[10/09/2018] TOUGH Symposium 2018: Subsurface characterization for large-scale systems: an integrated Python-based inversion tool for TOUGH2

[09/26/2018] UMH CEE 691 Seminar: Fast linear algebra and its applications in engineering

[09/11/2018] IAH 2018 Presentation: FP-046 Machine-learning-based Fast Forecast of Future Reservoir Performance

[08/01/2018] Young-Ho joined our group!

[07/03/2018] As2018 Presentation: Towards imaging the spatial distribution of geochemical heterogeneities and arsenic sources

[06/21/2018] Harry Lee was selected as an Oak Ridge Institute for Science and Education (ORISE) Faculty Fellow, 2018/10-2019/09

[06/19/2018] Our 2017 aquifer control paper in GWMR was one of the journal's top 20 downloaded papers.

[06/06/2018] CMWR 2018 Presentation: S23-3 Bathymetric Inversion from indirect observations

[06/05/2018] CMWR 2018 Presentation: S24-2 Imaging the spatial distribution of geochemical heterogeneities with inverse reactive transport modeling: The example of pyrite oxidation

[05/31/2018] Our paper “A learning-based data-driven forecast approach for predicting future reservoir performance” was accepted in Advances in Water Resources

[05/18/2018] 110th KSMER Spring Conference Presentation: Fast Forecast of Future Reservoir Performance Using Machine Learning

[05/17/2018] InterPore 2018 Poster: GS2 Fast Forecast of Future Reservoir Performance Using Deep Learning

[05/16/2018] InterPore 2018 Presentation: MS3.08 Imaging the spatial distribution of geochemical heterogeneities in porous media: multidimensional flow-through experiments and inverse modeling

[05/15/2018] InterPore 2018 Poster: MS1.28 Fast large-scale joint inversion for deep aquifer characterization using pressure and heat tracer measurements

[05/14/2018] InterPore 2018 Poster: MS1.19 Enriched Galerkin approach for density-driven flow in unsaturated coastal aquifer

[04/20/2018] Our paper “Riverine bathymetry imaging with indirect observations” was accepted in Water Resources Research

[04/17/2018] SIAM UQ 2018 Presentation: MS40 Efficient Bathymetry Estimation in the Presence of Model and Observation Uncertainties

[04/13/2018] EGU 2018 Poster: EGU2018-10230 Pyrite oxidation in porous media: flow-through experiments and reactive transport modeling

[04/03/2018] NSF EPSCoR ‘Ike Wai Project Reserve Site Visit in DC (annual report can be found in the project website)

[03/19/2018] pyPCGA: python package for Principal Component Geostatistical Approach released in public (openbeta)

[03/08/2018] CODA 2018 Poster: Efficient graph model construction for nano-porous media

[03/01/2018] Dr. Sung Eun Kim joined our research group!

[01/02/2018] A new course on optimization in groundwater engineering using USGS FloPy and Python SciPy optimization package

[12/13/2017] AGU 2017 Poster: NG31A-0168 Efficient data assimilation algorithm for bathymetry application

[12/11/2017] AGU 2017 Poster: H11E-1222 Riverine Bathymetry Imaging with Indirect Observations

[10/01/2017] The Lee Research Group is offically launching! We are inviting applications for PhD postions! see here

[09/2017] Our paper on groundwater solute transport and dispersion was accepted in Journal of Contaminant Hydrology.

[09/13/2017] SIAM GS 2017 presentation: Fast and scalable bathymetry inversion in riverine and near-shore environments

[08/2017] Our paper on subsurface characterization using pressure and heat tracer was accepted in Transport in Porous Media.