– Machine learning force fields for simple and complex fluids
– Static and dynamic properties of soft matters via multi-scale molecular dynamic approach
– Electronic and phononic properties in nano-structrued materials using DFT, Boltzmann Transport Equation (BTE) and molecular dynamics (MD).
– Modeling and design of chemical reactors via multi-scale approach: DFT, micro-kinetics, machine learning, and CFD analysis
– Applications of molecular dynamic techniques to chemical engineering problems
Education
– B.S. in Chemical Engineering, Seoul National University, Seoul, Korea, 1996
– M.S. in Chemical Engineering, Seoul National University, Seoul, Korea, 1998
– Ph.D. in Chemical Engineering, University of California, Santa Barbara, 2003–2007
Professional Experience
– Research Scientist & Process Engineer, SK Innovation, Korea (1998 ~ 2003)
– Postdoctoral Fellow(Humboldt), Max Planck Institute for Polymer Research, Germany (2008 – 2010)
– Assistant/Associate Professor, Department of Chemical and Biomolecular Engineering, Sogang University (2010 ~ 2015)
– Professor, Department of Chemical and Biological Engineering, Seoul National University (2020 ~ Present)
– Program Director of Sustainable Technology, School of Transdisciplinary Innovations, Seoul National University (2024-present)
Honors and Awards (Selected)
– Humboldt Fellowship for Postdoctoral Research (Humboldt Foundation), 2009 – 2010
– Emerging Academic Award (Korea Society of Rheology), 2017
– Shinyang Engineering Academic Award – Academic Field (Seoul National University), 2021
– Lee Byung-ho Outstanding Lecture Award (Seoul National University), 2023
– Outstanding Paper Award in Science and Technology (The Korea Federation of Science and Technology Societies), 2023
Research Interests
– Machine learning force fields for simple and complex fluids
– Static and dynamic properties of soft matters via multi-scale molecular dynamic approach
– Electronic and phononic properties in nano-structrued materials using DFT, Boltzmann Transport Equation (BTE) and molecular dynamics (MD).
– Modeling and design of chemical reactors via multi-scale approach: DFT, micro-kinetics, machine learning, and CFD analysis
– Applications of molecular dynamic techniques to chemical engineering problems
