Introduction

JONGHYUN HARRY LEEAssociate Professor

One can benefit from applied mathematics statistics, and computer programming to understand underlying natural phenomena and perform relevant projects successfully.

JONGHYUN HARRY LEEAssociate Professor

Jonghyun Harry Lee
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One can benefit from applied mathematics statistics, and computer programming to understand underlying natural phenomena and perform relevant projects successfully

Dr. Lee joined the WRRC “ohana” (family) and the department of CEE in 2017 as part of the Hawaii EPSCoR ‘Ike Wai project “Securing Hawai‘i’s Water Future.” A civil and environmental engineer, Harry hopes to integrate his separate research topics on groundwater, surface hydrology, and nearshore environments to develop a holistic model supporting integrated management of water resources from “Mauka to Makai” (mountain to ocean) in Hawaii.

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EDUCATION 

  • BS, Seoul National University 
  • MS, Colorado State University 
  • PhD, Stanford University 

INTERESTS 

  • Numerical modeling of variable density water flow 
  • Contaminant transport in coastal areas 
  • Uncertainty quantification in water resources modeling 
  • Physics-guided machine learning for water resources management 

CURRENT PROJECTS 

  • Geothermal energy recovery operation 
  • Carbon CO2 sequestration 

 

 

RESEARCH PROJECTS

Nearshore and Riverine Environment Characterization for Military Vehicle Mobility Assessment

Savannah River Bathymetry estimation using surface flow velocity
Nearshore and riverine bathymetry estimation using physics model-based data assimilation and machine learning

CSSI Elements: ALE-AMR Framework and the Pisale Codebase

pde graphic
This project will provide access to software for modeling with PDEs and also apply the code for simulations of complex groundwater flow processes in the Hawaiian islands characterized by highly heterogeneous volcanic rocks and dynamic interaction between freshwater and seawater.

DOE FE NETL Science-Informed Machine Learning for Accelerating Real Time Decisions in Subsurface Applications (SMART) Initiative Phase 1

Machine-learning based data assimilation for CO2 storage sites.
Machine-learning based data assimilation for CO2 storage sites.

Deep Learning Applications for Image Reconstruction and Analysis in Earth Sciences

Development of machine learning techniques for 3D digital rock reconstruction.
Development of machine learning techniques for 3D digital rock reconstruction.

‘Ike Wai: Securing Hawai‘i’s Water Future

the extend of modeling area (left) and its estimated hydraulic conductivity distribution (right)
Numerical modeling techniques for density-driven flow in coastal aquifers and its application to West Hawai‘i Area Aquifers in island of Hawai‘i.