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Undergraduate Research Intern

Institute of Materials, EPFL (Prof. Michele Ceriotti)

Advanced atomistic ML models integrating long-range interactions; contributed to open-source educational code; improved PyTorch-based tools and CI/CD practices; presented a poster at DPG 2025; contributed to 2 preprints.

• Tools: PyTorch, ASE, LAMMPS, ipywidgets, Git, Bash

Research Intern (CUop [Company-University Cooperation] Program)

SpiderCore Inc.

Implemented graph neural networks for gene therapy design; developed chemically inspired self-supervised task achieving state-of-the-art performance.

• Tools: TensorFlow, RDKit

Research Intern

University of Illinois Urbana–Champaign

Comparative analysis of cellular membranes for joint drug design projects; refactored and optimized analysis methods (>30× speedup) enabling structural insights; co-authored a manuscript in preparation.

• Tools: MDAnalysis, scikit-learn

Undergraduate Research Intern

Department of Biological Sciences, KAIST

Designed potentially therapeutic antibody variants (Prof. Byung-Ha Oh)

• Tools: RFDiffusion, AlphaFold2, ProteinMPNN, Bash

Undergraduate Research Intern

Department of Chemistry, KAIST

Developed novel chemical reactions via combinatorial methods (Prof. Yoonsu Park).

scicode-widgets: Bringing Computational Experiments to the Classroom with Jupyter Widgets

arXiv:2507:05734

Learning Long-Range Representations with Equivariant Messages

arXiv:2507.19382

Exchange

École Polytechnique Fédérale de Lausanne (EPFL)

Exchange Program

• Dynamical Systems in Biology
• Methods in Drug Development (graduate)
• Structural Analysis

B.S.

KAIST (Korea Advanced Institute of Science and Technology)

Bio and Brain Engineering; double major in Chemistry

• Big Data and Machine Learning in Biotechnology
• Bio-Data Engineering
• Bio-Information Processing
• Bio-Data Structures
• Statistical ML
• Statistical Methods with Computing
• Computational Chemistry
• AI Chemistry
• ML for Molecules and Materials (graduate)

Silver Award (5th place) — 3rd POSTECH–UNIST–KAIST Data Science Competition

Developed forecasting models with cost-aware optimization to predict demand for electronic parts; placed 5th among 20+ teams.

Predicting Demand for Electronic Parts

Forecasting with cost-aware optimization to predict demand for electronic parts; 5th place (Silver Award) in competition.

• Nixtla

Analyzing Workplace Discrimination in Korea

Statistical analysis to uncover national trends in workplace discrimination; EDA, hypothesis testing, and clustering.

• scikit-learn
• pandas

Biomedical Information Systems for Future Healthcare

Designed biomedical and pharmacokinetics database integrating genomics and PK datasets; CLI for provider use cases.

• PostgreSQL
• psycopg2

Housing Price Prediction

Built predictive models for housing prices; EDA, feature engineering, and hyperparameter tuning; ranked 7/60 in class.

• scikit-learn
• pandas
• Optuna

Prediction of pKBHX on a Small Dataset

GCNN to predict hydrogen bond basicity (pKBHX) on ~350 molecules; preprocessing, regularization, hyperparameter tuning; ranked 4/13 (1st among undergrads).

• PyTorch
• RDKit

SNP Analysis for COVID‑19 Delta Variant Surge

Heuristic global sequence alignment in pure Python to detect SNPs; analyzed SNP patterns associated with the Delta surge in England; literature review for biological roles.

• Python