Dr. Liang earned a Ph.D. in Civil Engineering and an M.S. in Computer Engineering from Purdue University. His work integrates parallel computing, AI/ML, and advanced optimization with physics-based numerical modeling for environmental systems. Proficient in Python, C/C++, Shell scripting, compiler tools, and AI tools, he develops efficient, robust, and scalable programs.
His experience spans groundwater and surface water modeling, contaminant transport, and data-driven decision support across academia, government, and industry. He has conducted projects in PFAS transport software and modeling, well-site optimization, and oil-spill tracking. He has also contributed to open-source geospatial software through enhancements in high-performance computing. He developed deep learning models for generating river bathymetry, calibrating groundwater models, and predicting water resources, among other applications. He delivers scientifically rigorous and operationally practical solutions.
Personal website: https://cyliang368.github.io/