Bachelor of Science in Applied Mathematics, Kongju National University (Feb 14, 2025)

Yamaguchi University (Faculty of Science): non-degree visiting student, April 2023–March 2024; completed 33 credits in analysis, algebra, computer science, statistics, modeling, and simulation.

Interests include reinforcement learning (RL) and AI based on fundamentals in Markov Decision Processes, optimization, and programming.

Selected coursework: Deep Learning; Advanced Calculus I–II; Applied Linear Algebra I–II; Probability and Statistics; Data Structures and Algorithms; Programming for Data Science; Artificial Intelligence and Mathematics; R Programming.

Experience with: RL Algorithms (PPO, DQN, A2C, REINFORCE), Planning/Control (MPC), Time Series Forecasting, Image Classification, NLP (Basic concepts to Transformer)

• Designed the MySQL schema and REST APIs; set up CI/CD with automated deployments, health checks, and monitoring.
• Prototyped a simple recommendation and glucose-forecast model using minimal inputs; explored safe personalization under sparse data.
• Selected for the Asan Nanum Foundation “Doers” Program — Pre-Startup Track (Jeong Joo-Young Startup Competition).

• Led development of ML learning modules and reference implementations (k-NN, logistic regression, decision trees) with tests and documentation from scratch.

• Produced animated explainers with Manim(3blue1brown math tool); set code review standards for reproducibility.

• Supported curriculum design, demo pipelines, and contributor workflows.

• Participated in the KIAS workshop titled “Machine learning integrated Fourier transform for enhanced signal analysis,” exploring ML-driven spectral methods.

• Studied and conducted experiments on the double descent phenomenon, analyzing weights dynamics across model capacity and sample regimes.

• Bachelor of Science in Applied Mathematics; Graduation: February 14, 2025; 130 credits; GPA: 3.81/4.5 (93%).

• Core coursework: Advanced Calculus I/II, Applied Linear Algebra I/II, Applied Differential Equations, Probability Theory, Probability and Statistics, Discrete Mathematics, Artificial Intelligence and Mathematics, Deep Learning, R Programming, Data Structures and Algorithms, Modeling and Simulation.

• Faculty of Science; Non-degree exchange; Apr 2023–Mar 2024; 33 credits.

• Studied analysis, algebra, probability and statistics, data structures, programming for data science (RL lecture), modeling and simulation, graph theory.

• Python (NumPy, Pandas, SciPy) for data analysis and prototyping
• C++ for performance-focused tasks
• C for basic systems programming
• R (tidyverse) for statistics and plots

• PyTorch

• TensorFlow/Keras

• scikit-learn

• PyTorch Lightning

• Jupyter Notebook

• RapidMiner/tableau

• MATLAB

• RStudio

• Relevant coursework: Data Structures and Algorithms; Programming for Data Science; Applied Data Science Practice; Seminar in Multimedia Processing; Computer Programming II; Deep Learning

• Git

• Notion

• Asana

• Familiar with PPO, SAC, A2C, and DQN

• Understand MDPs, policy/value methods, reward design, and exploration basics

• Used PyTorch, Gym/Gymnasium

• Track experiments with Weights & Biases, MLflow, and TensorBoard
• Hyperparameter tuning with Optuna
• Export and run models with ONNX and TorchScript

• Calculus

• Linear Algebra

• Convex Optimization

• Statistics

• Probability Theory

• Graph Theory

• Multivariate Analysis

• Modeling and Simulation

• Group, Ring Theory

• Completed a one-month intensive on the end-to-end deep learning workflow. From data analysis and cleaning to preprocessing, model training, and evaluation.

• Implemented ResNet and VGG variants and set up preprocessing (normalization, resizing) with basic augmentation.

• Built advanced architecture of VGG, improving accuracy.

• Built a PyTorch training and evaluation pipeline with adjustable hyperparameters and run tracking.

• Compared XGBoost, CatBoost, and regularized linear models; selected the best-performing approach on validation.

• Set up a time-series workflow with rolling cross-validation and leakage-aware feature engineering.

• Built a sequence model combining 1D CNN, BiLSTM, attention, and MLP for trajectory data.
• Applied attention-based sequence learning to capture temporal patterns in trajectories.