Teaching & Education

Bridging Theory and Practice in Computational Sciences

Empowering the next generation of computational scientists and engineers

Teaching Philosophy

Hands-On Learning

Learning by doing with real computational problems.

Theory-Practice Bridge

Connecting theory with practice for deeper understanding.

Collaborative Environment

Fostering collaborative learning and peer support.

Teaching Assistant Experience

Computational Materials and Data Science
Seoul National University • Fall 2024 • Graduate Level

ML applications in materials science: GNNs, active learning, PyTorch implementations.

Machine Learning Materials Informatics PyTorch Graph Neural Networks
Physical Chemistry Laboratory
POSTECH • Fall 2023 • Undergraduate Level

Lab supervision: kinetics, thermodynamics, spectroscopy.

Experimental Design Data Analysis Kinetics Thermodynamics
Molecular Simulation
POSTECH • Fall 2021 • Graduate Level

Computational methods: MD, Monte Carlo, DFT calculations.

Molecular Dynamics Monte Carlo DFT VASP

Teachable Topics & Expertise

Computational Chemistry

DFT, electronic structure, catalysis applications.

DFT VASP ASE

Machine Learning for Scientists

Neural networks, GNNs, chemistry applications.

PyTorch GNNs Active Learning

Thermodynamics & Kinetics

Chemical kinetics, statistical mechanics.

Reaction Engineering Kinetic Modeling Statistical Mechanics

Catalysis Fundamentals

Surface science, catalyst design.

Surface Science Reaction Mechanisms Catalyst Design

Scientific Programming

Python, data analysis, research workflows.

Python NumPy Matplotlib

Materials Informatics

Data-driven materials discovery, high-throughput screening, materials databases, and computational materials design.

Materials Discovery High-throughput Data Mining

My Teaching Approach

Interactive Learning

  • Combine lectures with hands-on coding sessions
  • Use real research problems as learning examples
  • Encourage questions and collaborative problem-solving
  • Provide immediate feedback on computational results

Practical Skills Development

  • Focus on transferable computational skills
  • Emphasize code readability and documentation
  • Teach debugging and troubleshooting techniques
  • Connect theory to real-world applications

Student-Centered Learning

  • Adapt teaching methods to different learning styles
  • Provide multiple paths to understanding concepts
  • Offer additional support for struggling students
  • Encourage independent exploration and creativity

Industry Relevance

  • Include current industry trends and challenges
  • Discuss career paths in computational sciences
  • Invite guest speakers from academia and industry
  • Emphasize skills valued by employers

Educational Resources & Materials

Computational Modeling Tutorials

Step-by-step guides for setting up and running DFT calculations, molecular dynamics simulations, and machine learning models for materials science applications.

Python Scripts for Scientists

Collection of well-documented Python scripts for data analysis, visualization, and computational workflows commonly used in chemistry and materials research.

Machine Learning Examples

Practical examples of applying machine learning techniques to chemical and materials problems, including complete code implementations and explanations.

Research Best Practices

Guidelines for reproducible computational research, including code organization, documentation standards, and version control for scientific projects.

View Course Materials on GitHub

Commitment to Student Success

My goal is to inspire students to see computational science not just as a tool, but as a powerful way to understand and solve real-world problems. I strive to create an environment where curiosity thrives and students develop both technical skills and scientific intuition.
- Teaching Philosophy