CV
Curriculum vitae of Jaehyung Lee — PhD student in Materials Science & Engineering at Johns Hopkins University. Use the button above to download the PDF.
Contact Information
| Name | Jaehyung Lee |
| Professional Title | PhD Student, Materials Science & Engineering |
| jlee859@jh.edu |
Professional Summary
PhD student in Materials Science & Engineering at Johns Hopkins University working on machine learning and high-performance computing for accelerated materials discovery — spanning DFT, ML interatomic potentials, graph neural networks, and agentic AI.
Education
Research Experience
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2025 - present Baltimore, MD
Doctoral Researcher
Johns Hopkins University — Choudhary Lab
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2024 - 2024 New York, NY
Graduate Research Assistant
Columbia University — Urban Lab
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2020 - 2023 University Park, PA
Undergraduate Research Assistant
Pennsylvania State University — Janik & Alexopoulos Labs
Industry & Service
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2022 - 2022 Reading, PA
Advanced Quality Engineer Intern
EnerSys
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2018 - 2020 Camp Humphreys, South Korea
Sergeant (E-5)
U.S. Forces Korea (KATUSA)
Teaching
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2026 - 2026 Baltimore, MD
Teaching Assistant — Applied Quantum Computing (EN.520.677)
Johns Hopkins University
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2026 - 2026 Baltimore, MD
Teaching Assistant — Physical Chemistry of Materials I (EN.510.312)
Johns Hopkins University
Leadership
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2024 - 2024 New York, NY
MS Ambassador
Columbia University, Dept. of Chemical Engineering
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2020 - 2023 University Park, PA
Vice President
AIChE, Penn State Chapter
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2020 - 2023 University Park, PA
Development Committee Chair
Engineers Without Borders, Penn State
Publications
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2026 AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org
The Journal of Physical Chemistry Letters
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2026 Lessons Learned from the 2025 Agentic AI for Science Hackathon
ChemRxiv (under review, Mach. Learn.: Sci. Technol.)
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2025 The search for high-entropy fuel-cell catalysts using disorder descriptors
Nano Futures 9(4), 045001
Selected Presentations
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2026 SlaKoNet DB: A Cross-Domain Tight-Binding Database of Electronic Structure (poster)
Electronic Structure Workshop (ESW 2026), University of Wisconsin–Madison
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2026 SlaKoNet DB: A Cross-Domain Tight-Binding Database of Electronic Structure (poster)
Artificial Intelligence for Materials Science (AIMS) 2026, NIST, Gaithersburg, MD
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2026 Agentic AI for Materials Design with AGAPI (talk)
MRS Spring Meeting, Honolulu, HI
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2026 BatteryMat: A Machine Learning Accelerated DFT Framework for Screening Battery Cathode Materials (poster)
NIST Quantum Matters in Materials Science (QMMS) Workshop, Gaithersburg, MD
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2024 Analysis on Cathode Materials for Lithium-ion Batteries Using Machine Learning Potentials (talk)
Société de Chimie Industrielle Palladium Medal Ceremony, New York, NY
Skills
First-Principles & Atomistic Methods: DFT (VASP), DFT+U, NEB ion-migration barriers, convex-hull / phase stability, supercell modeling
Machine Learning for Materials: ML interatomic potentials (ALIGNN-FF), graph neural networks (ALIGNN), tight-binding NN (SlakoNet), PyTorch, DGL
Programming & Tooling: Python, Bash, JARVIS-Tools, ASE, SLURM, REST APIs, LLM tool-calling / agents
High-Performance Computing: Multi-GPU training & inference, GPU scaling, distributed workflows
Languages
English : Native speaker
Korean : Native speaker