Harry Lee
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Presentations

  1. D. Patel, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve (2023 Dec. 16) “Improved Black-box Variational Inference for High-dimensional Bayesian Inversion involving Black-box Simulators”, NeurIPS 2023 workshop: Deep Learning and Inverse Problems, New Orleans, LA

  2. D. Patel, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve (2023 Dec. 13), “Overcoming Challenges of Practical Bayesian Inference: Black-box Forward Models meet Black-box Variational Inference”, AGU Fall Meeting 2023, San Francisco, CA

  3. P. Casillas, S. Dutta, M. Loveland, J. Lee, M. Farthing, C. Dawson (2023 Dec. 13), “Latent Space Neural Operators for Hydrodynamic Modeling”, AGU Fall Meeting 2023, San Francisco, CA

  4. D. Patel, J. Lee, T. Hesser, M.W. Farthing, P.K. Kitanidis, E.F. Darve (2023 Oct. 23), “Overcoming challenges of practical Bayesian inference: black box forward models meet blackbox variational inference”, Advancec in Computational Mechanics (ACM 2023), Austin, TX

  5. J. Bao, J. Lee, H. Yoon, L. Pyrak-Nolte (2023 Jun. 27) “Subsurface Characterization using Bayesian Deep Generative Prior-based Inverse Modeling for Utah FORGE Enhanced Geothermal System”, ARMA 57th US Rock Mechanics/Geomechanics Symposium, Atlanta, GA

  6. J. Lee, T. Hesser, M. W. Farthing, A. S. Bak, K. DeVore (2022 Dec. 9) “Scalable real-time data assimilation with various data types for accurate spatiotemporal nearshore bathymetry estimation”, ICCE 2022, Sydney, Australia

  7. H. Yoon, J. Lee (2022 Oct. 26) “Deep learning-based data assimilation in the latent space for real-time forecasting of geologic carbon storage”, 16th Greenhouse Gas Control Technologies (GHGT-16), Lyon France

  8. J. Lee (2022 Sep. 19), “Subsurface Characterization using Generative Models”, 49th International Association of Hydrologists (IAH) Congress, Wuhan, China (Virtual)

  9. J. Lee (2022 Sep. 16), “Machine Learning in Civil and Environmental Engineering”, CEE Department Graudate Seminar, Honolulu, HI

  10. E. F. Darve, D. Patel, P. K. Kitanidis, J. Lee (2022 Aug. 18) “Multifidelity Monte-Carlo Sampling in Mechanics”, USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP), Arlington, VA

  11. J. Lee (2022 Aug. 11), “Uncertainty Quantification using Generative Models for Geoscience Applications”, KSIAM-MINDS-NIMS International Conference on Machine Learning and PDEs (ICMLP), Seoul, Korea (Hybrid)

  12. D. Patel, J. Lee, M. Forghani, M.W. Farthing, T. Hesser, P.K. Kitanidis, E.F. Darve (2022 Aug. 1), “Multi-fidelity Hamiltonian Monte Carlo with Deep Learning-based Surrogate”, WCCM-APCOM 2022, Yokohama, Japan (Virtual)

  13. Y. Seo, J. Lee, A. Koniges, A. Fisher (2022 Jul. 12), “Development of the PISALE Codebase for Simulating Flow and Transport in Large-scale Coastal Aquifer”, ICCFD11, Maui, HI.

  14. J. Lee (2022 Jun. 25), “Subsurface Characterization using Generative Models”, ARMA 56th US Rock Mechanics Geomechanics Symposium Workshop, Santa Fe, NM (hybrid).

  15. D. Patel, J. Lee, M. Forghani, M. Farthing, T. Hesser, P. K. Kitandis, E. F. Darve (2022 Jul. 26), “A Multi-Fidelity Hamiltonian Monte Carlo Method with Deep Learning-Based Surrogate for Subsurface Flow”, CMWR 2022, Gdansk, Poland (hybrid).

  16. D. Patel, J. Lee, M. Forghani, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve (2022 Apr. 13), “Multi-Fidelity Hamiltonian Monte Carlo Method with Deep Learning-Based Surrogate”, SIAM UQ 2022 (Virtual)

  17. W. Reese, A. K. Saibaba, J. Lee (2022, Apr. 4), “Bayesian Level Set Approach for Inverse Problems with Piecewise Constant Reconstructions”, 17th Copper Mountain Conference On Iterative Methods (Virtual)

  18. H. Yoon, J. Lee (2022 Mar. 31) “Physics-based Deep Learning Driven CO2 Flow Modeling and Data Assimilation for Real-Time Forecasting”, AAPG-CCUG, Houston, TX

  19. J. Lee, K. DeVore, M. Farthing, T. Hesser (2022 Mar. 3) “Bathymetry Blending using cBathy and Parametric Beach Tool”, AGU OSM 2022 (virtual)

  20. T. Hesser, M. Farthing, J. Lee, K. Devore, K. Brodie (2022, Mar 3), “Single Flight Littoral Bathymetry Estimation from Infrared Imagery”, AGU OSM 2022 (virtual)

  21. D. Patel, M. Forghani, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve (2021, Nov 6), “Efficient Stochastic Subsurface Inversion using Deep Generative Modeling and Multi-fidelity Importance Sampling”, AGU Fall Meeting 2021 (virtual)

  22. X. Kang, A. Kokkinaki, X. Shi, J. Wu, H. Yoon, J. Lee, P. K. Kitanidis (2021, Nov 11), “Integration of Deep Learning-based Inversion and Upscaled Mass-transfer Model for DNAPL Mass-discharge Prediction and Uncertainty Assessment”, RemPlex 2021 (virtual)

  23. D. Patel, J. Lee, M. Forghani, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve (2021, Nov 6), “Multi-Fidelity Hamiltonian Monte Carlo Method with Deep Learning-based Surrogate”, AAAI 2021 Fall Symposium on Science-guided AI (virtual)

  24. M. Forghani, Y. Qian, J. Lee, M. W. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve (2021, Sep 26), “Deep learning application to large-scale bathymetry estimation”, MMLDT-CSET 2021, San Diego, CA (hybrid)

  25. J. Lee (2021, Sep. 22), “Combining ML with Physics Information in Water Resources Engineering”, UHM WRRC seminar, Honolulu, HI, USA. (virtual)

  26. J. Lee (2021 Oct 10), “ML-based scalable data assimilation with hydrological applications”, 2nd Workshop on Knowledge Guided Machine Learning. (virtual)

  27. J. Lee, M. Forghani, Y. Qian, M. Farthing, T. Hesser P. K. Kitanidis, E. F. Darve (2021, July 19), “Large-Scale Bathymetry Prediction using Deep Learning Approaches”, SIAM Annual Meeting (AN21). (virtual)

  28. W. Reese, A. Saibaba, J. Lee (2021, Mar 5), “Bayesian Level Set Approaches for Inverse Problems with Piecewise Constant Reconstructions”, SIAM Conference on Computational Science and Engineering (CSE21). (virtual)

  29. E. F. Darve, Y. Qian, P. K. Kitanidis, M. Farthing, T. Hesser, J. Lee (2021, Mar. 4), “Data Assimilation and Inverse Modeling using Variational Supervised-Encoders”, SIAM Conference on Computational Science and Engineering (CSE21). (virtual)

  30. Y. Qian, M. Forghani, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve, (2021, Mar 4), “Bayesian Inference of Nearshore Bathymetry using Deep Learning Techniques”, SIAM Conference on Computational Science and Engineering (CSE21). (virtual)

  31. M. Forghani, Y. Qian, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve, (2021, Mar. 3), “Deep Learning for Large-Scale Riverine Surface Flow Velocity Estimation”, SIAM Conference on Computational Science and Engineering (CSE21). (virtual)

  32. Y. Qian, J. Lee, M. Forghani, M. W. Farthing, T. Hesser, P. K. Kitanidis and E. F. Darve, “Deep Learning Based Spatial Interpolation Methods for Nearshore Bathymetry with Sparse Measurements”, 23rd CMWR conference, Stanford, CA, December 17, 2020

  33. X. Kang, A. Kokkinaki, X. Shi, J. Lee, P. K. Kitanidis, “Contaminant source characterization: sequential and joint geostatistical inversion and the benefit from a physical based prior”, 23rd CMWR conference, Stanford, CA, December 14, 2020

  34. M. Forghani, J. Lee, Y. Qian, M. W. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve, “Application of deep learning to large scale riverine bathymetry and surface flow velocity estimation”, 23rd CMWR conference, Stanford, CA, December 14, 2020

  35. M. Forghani, Y. Qian, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve, “Deep learning application to fast estimation of riverine surface flow velocity”, AGU Fall Meeting, San Francisco, CA, December 1-17, 2020.

  36. Y. Qian, M. Forghani, J. Lee, M. W. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve, “Deep Bayesian techniques to nearshore bathymetry with sparse measurements”, AGU Fall Meeting, San Francisco, CA, December 1-17, 2020

  37. X. Kang, A. Kokkinaki, C. Power, X. Shi, P. K. Kitanidis, J. Lee, K. Wu, “Improved monitoring of dense non-aqueous phase liquid (DNAPL) source zone remediation through hydrogeophysical inversion using variational autoencoder and Ensemble Kalman filter”, AGU Fall Meeting, San Francisco, CA, December 8, 2020

  38. M. Forghani, Y. Qian, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve, “Fast solver of the shallow water equations with application to estimation of the riverine surface flow velocity”, APS Fall meeting, Chicago, IL, November 23, 2020

  39. J. Lee, E. F. Darve, P. K. Kitanidis, M. W. Farthing, T. Hesser, “Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences”, CEUR-WS (http:ceur-ws.orgVol-2587), Stanford, CA, March 23-25, 2020

  40. H. Ghorbanidehno, Y. Qian, J. Lee, M. W. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve, M. Forghani, “Deep learning techniques for nearshore and riverine bathymetry estimation using water-surface observations”, AGU Ocean Sciences Meeting 2020, San Diego, CA, February 20, 2020

  41. J. Lee, M. Farthing, T. Hesser, K. L. Brodie, H. Ghorbanidehno, M. P. Geheran, B. L. Bruder, E. F. Darve, P. K. Kitanidis, “Rapid wave model-based nearshore bathymetry inversion with UAS measurements”, AGU Ocean Sciences Meeting 2020, February 21, 2020

  42. J. Lee, M. Farthing, T. Hesser, K. L. Brodie, H. Ghorbanidehno, M. P. Geheran, B. L. Bruder, E. F. Darve, P. K. Kitanidis, “Rapid wave model-based nearshore bathymetry inversion with UAS measurements”, AGU Fall Meeting 2019, December 9, 2019

  43. J. Lee, “ML-based Nano-scale Carbonate Rock Reconstruction and Associated Microfluidics Design for EOR and CO2 Sequestration”, SEG 2019 Post-Convection Workshop, September 20, 2019

  44. N. Grobbe, J. Lee, R. Wang, C. Chen, E. Darve, “Fast and scalable joint subsurface inversion using hydrological-geophysical datasets with a parallel black-box Fast Multipole Method”, SIAM GS 19, Houston, TX, March 12, 2019

  45. H. Ghorbanidehno, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve, “Robust Parameter Estimation Using Inverse Modeling and Advanced Machine Learning Techniques”, SIAM GS 19, Houston, TX, March 11, 2019

  46. S. Lee and J. Lee, “A Locally Conservative Enriched Galerkin for Coupling Flow and Transport”, SIAM GS 19, Houston, TX, March 11, 2019

  47. J. Lee, “High-dimensional EM imaging in geoscience and engineering accelerated by a black-box Fast Multipole Method”, CEMC and UHM Site IAB meeting, Hawaii, HI, September 26, 2018

  48. J. Lee, UMH CEE 691 Seminar: “Fast linear algebra and its applications in engineering”, Septebmer 26, 2018

  49. H. Ghorbanidehno, J. Lee, M. Farthing, T. Hesser, P. K. Kitanidis, E. F. Darve, “Efficient Bathymetry Estimation in the Presence of Model and Observation Uncertainties”, SIAM Conference on Uncertainty Quantification, Garden Grove, CA, April 17, 2018

  50. J. Lee, H. Ghorbanidehno, M. Farthing, T. Hesser, M. Geheran, E. F. Darve and P. K. Kitanidis, “Fast and scalable bathymetry estimation for nearshore and river hydrodynamic modeling”, SIAM Conference on Mathematical and Computational Issues in the Geosciences, Erlangen, Germany, September 13, 2017

  51. J. Lee, “Scalable inverse modeling and data assimilation methods in earth sciences”, Inria@SiliconValley workshop session on Level-set methods, parallel computing and uncertainty quantification: Techniques and Application at CITRIS and the Banatao Institute, Berkeley, June 9, 2017

  52. J. Lee, M. Rolle, and P. K. Kitanidis, “How can we make Fickian dispersion models useful in practice”, AGU Fall Meeting 2016, San Francisco, CA, December 14, 2016

  53. J. Lee, “Scalable inverse modeling with an application to saline aquifer characterization”, US Army Corps of Engineer's Engineering Research Development Center Coastal and Hydraulics Laboratory, Vicksburg, Mississippi, November 10, 2016

  54. A. Kokkinaki, J. Lee, P. K. Kitanidis, B. E. Sleep, H. Yoon, C. J. Werth, and A. J. Valocchi, “DNAPL Modeling With Permeabilities Obtained By Geostatistical Inverse Modeling: How Much Detail Is Enough?”, Computational Methods in Water Resources, University of Toronto, Canada, June 24, 2016

  55. J. Lee, H. Yoon, T. Dewers and P. K. Kitanidis, “Multiscale Imaging of Carbonate Rocks and 3D Stochastic Reconstruction for Digital Rock Physics, SIAM Conference on Imaging Science”, Albuquerque, New Mexico, May 26, 2016

  56. J. Lee, “Scalable Subsuface Modeling and Characterization for Aquifer Recharge and Recovery”, Departmental Seminar, Technical University of Denmark (DTU), Copenhagen, Denmark, April 14, 2016

  57. J. Lee, and P. K. Kitanidis, “Fast Large-Scale and Joint Subsurface Inversion Using Principal Component Geostatistical Approach”, SIAM Conference on Uncertainty Quantification, EPFL, Lausanne, Switzerland, April 7, 2016

  58. J. Lee, “Integrated Hydro-geophysical Characterization for Managed Aquifer Recharge and Recovery Sites”, International Workshop on Aquifer Storage and Recovery in Saline Aquifer, Korea Institute of Science and Technology (KIST), Seoul, Korea, Mar 22, 2016

  59. J. Lee, “Fast and Scalable Subsurface Inverse Modeling using Principal Component Geostatistical Approach”, Sandia National Laboratories, Albuquerque, New Mexico, Mar 2, 2016

  60. J. Regnery, Z. Drumheller, J. Lee, J. Drewes, K. Smits, T. H. Illangasekare, and J.E. McCray, “Understanding trace organic chemical attenuation during groundwater recharge by means of a 2D synthetic aquifer”, NGWA Conference - Hydrology and Water Quality in the Southwest, Albuquerque, New Mexico, Feb 23-24, 2016

  61. P. K. Kitanidis and J. Lee, “Three-dimensional ERT Imaging by the Geostatistical Approach”, AGU Fall Meeting 2015, San Francisco, USA, December 2015

  62. J. Lee, H. Yoon and P. K. Kitanidis, “Fast Large-Scale and Joint Subsurface Inversion Using Principal Component Geostatistical Approach”, SIAM Conference on Mathematical and Computational Issues in the Geosciences, Stanford, CA, USA, June 29, 2015

  63. J. Lee and P. K. Kitanidis, “Matrix-free geostatistical inversion with an application in large-scale hydraulic tomography”, SIAM Confererce on Uncertainty Quantification, Savannah, GA, April, 2014

  64. J. Lee, J. Regnery, Z. W. Drumheller, P. Schulte, K. M. Smits, T. H. Illangasekare and P. K. Kitanidis, “FEFLOW Integration to a Sensor-based Control System for Managed Aquifer Recharge”, Groundwater Modeling Workshop : State-of-the-art Groundwater Modelling For Water Resources Management And Well-field Operation, Denver, Colorado, USA, May 8 2014

  65. X. Liu, Q. Zhou, J. T. Birkholzer and J. Lee, “Geostatistical Reduced-Order Models in Inverse Problems”, SIAM Conference on Uncertainty Quantification, Savannah, Georgia, USA, April 2014

  66. J. Lee and P. K. Kitanidis, “Bayeisan Total Variation Inversion for Discrete Geologic Structure Identification”, AGU Fall Meeting 2012, San Francisco, California, Dec 2012

  67. J. Lee, P. K. Kitanidis and X. Liu, “Bayesian Subsurface Imaging using Total Variation Prior”, Stanford Chapter of SIAM, Stanford, USA, Mar 8 2012

  68. P. K. Kitanidis, J. Parker, U. Kim, X. Liu, and J. Lee, “Context-specific Quantification of Uncertainty, Value of Information, and Total-cost Optimization of DNAPL Site Remediation”, SERDP Annual Meeting, Washington D.C., USA, Nov 30 - Dec 2 2010

  69. X. Liu, J. Lee, P. K. Kitanidis, J. Parker, and U. Kim, “Context-Specific Measures of Uncertainty in Groundwater Remediation”, AGU Fall Meeting 2010, San Francisco, California, USA, Dec 2010

  70. J. Lee and D. Bau, “Optimization Approaches for the Management of Groundwater Supply Systems under Parameter Uncertainty”, AGU Hydrology Days, Colorado State University, USA, March 29 2009