Groundwater simulation laboratory
Research Field
I am an associate professor of Earth and Environmental Sciences at National Chung Cheng University (CCU), Taiwan. With a hydrogeology and numerical modeling specialization, my academic journey has been dedicated to understanding complex subsurface processes, particularly in fractured geological media. My teaching portfolio includes courses such as statistics, linear algebra, fluid mechanics, and hydrogeology, tailored to students in Earth sciences. While I find value in education, my true passion lies in research — especially in integrating theory, simulation, and fieldwork to solve real-world environmental challenges. I actively collaborate with international institutions, mentor students across borders, and contribute to national science initiatives focusing on climate adaptation, energy transition, and environmental resilience.
Our laboratory at National Chung Cheng University is dedicated to subsurface hydrological modeling and environmental simulation, with a focus on groundwater flow and solute transport in fractured and porous media. We employ a wide range of advanced numerical tools, including open-source platforms such as TOUGH3, DFNWorks, and OpenGeoSys, as well as commercial software like FEFLOW and FracMan. These tools allow us to investigate complex flow and transport behaviors under varied geological settings.
Our research addresses critical environmental and energy-related challenges, including geological disposal of radioactive waste, geothermal energy development, carbon dioxide sequestration, and contaminant migration in fractured formations. Through interdisciplinary collaboration and rigorous simulation work, the lab provides a dynamic environment for tackling real-world subsurface problems and training the next generation of geoscientists.
My research focuses on the simulation and analysis of groundwater flow and solute transport in fractured rock environments, a central theme with wide-ranging applications. These include
- geological disposal of spent nuclear fuel
- geothermal energy exploration
- geological carbon sequestration
- contaminant remediation in fractured and porous media
- water resource management
I specialize in developing and applying advanced numerical models to investigate fluid flow behavior and its interactions with geological structures in complex subsurface systems.
In parallel, our group is actively developing machine learning and deep learning algorithms to address emerging challenges across multiple disciplines — including water resource management, CO₂ transport in fractured rocks, groundwater contamination, and geothermal exploration. This research direction aims to harness state-of-the-art AI techniques as surrogate models, offering efficient and scalable alternatives to conventional, computationally intensive simulators.
Beyond computational development, I emphasize interdisciplinary collaboration to deepen our understanding of fractured rock behavior and environmental systems. My long-term vision is to establish a globally connected research platform that brings together talented students and researchers to push the boundaries of modeling and predictive analysis in Earth and environmental sciences.
- College of Science Academic Excellence Award, National Chung Cheng University, 2010
- Distinguished Student Mentorship Award, National Chung Cheng University, 2012
- Outstanding Service Award for Faculty and Researchers with Administrative Duties, National Chung Cheng University, 2025
- Ph.D., 1999, Department of Civil and Environmental Engineering, University of California at Berkeley
- M.S., 1992, Department of Civil Engineering, National Chiao-Tung University
- B.S., 1990, Department of Civil Engineering, National Chiao-Tung University
Job Description
Research Focus & Internship Expectations
Interns will join an interdisciplinary team working on three key environmental applications:
- Geological CO2 Transport in Fractured Media
- Deep Geothermal Energy Exploration and Simulation
- Groundwater Contamination and Risk Prediction
The intern will select one of the above topics and develop a deep learning model designed to emulate the behavior of conventional numerical simulators (e.g., TOUGH3, FEFLOW, OpenGeoSys). While modern simulation tools provide accurate predictions, they are often computationally expensive and difficult to scale — especially when dealing with incomplete field data or large parameter uncertainties. This internship aims to bridge that gap by training AI models on physics-based synthetic datasets generated by trusted simulation software, offering a new path toward real-time environmental modeling.
Deliverables and collaboration:
- Interns will work closely with lab members and participate in weekly research meetings
- Each intern is required to submit a technical report summarizing methodology, findings, and model performance by the end of the internship
- Interns are also required to give a final presentation, accompanied by an internship progress report, to share their project outcomes and reflect on the learning experience
Preferred Intern Education Level
Undergraduate or graduate students in Earth science, computer science, or related fields
Skill sets or Qualities
- Proficiency in Python
- Prior experience in machine learning (deep learning frameworks such as PyTorch or TensorFlow preferred)
- Familiarity with handling large datasets
- A background in Earth science is a plus, but not required
- A strong interest in sustainability is essential