A sophomore at KAIST, aspiring to solve real-world problems through Data Science & Machine Learning.

• Organized and led a data labeling team to build a high-quality annotated dataset for training the cooking-robot segmentation model.

• Developed a segmentation model for cooking robots to accurately identify safety-critical objects (pots, ingredients, oil, and human operators) in real-time.

• Designed a custom evaluation metric to prevent real-world hazards by penalizing consecutive misclassifications of high-risk classes.

• Optimized the model based on the custom metric, achieving ≥95% safety-compliant frames and limiting maximum consecutive errors to under 10 frames (<2 seconds).

• Department Representative of ISE (과대표)

• VOK (KAIST Broadcasting System), Executive Director of General Affairs (총무)

• VOK (KAIST Broadcasting System), Announcer

• Freshman Representative Speaker, KAIST Entrance Ceremony

• President, Student Council

• Graduated on the Domestic Track

• Python, C, C++, Java

• PyTorch, TensorFlow, OpenCV, CUDA, NumPy, Pandas, Seaborn

• Jetson Nano, Arduino,  ESP32

• Korean (Native), English (Fluent, iBT TOEFL 108)

• Developed a machine learning pipeline using Finite Element Method (FEM) simulation and design specification data to predict tire manufacturing defects and optimize test production strategies.

• Engineered custom features, including Target-Aware Correlation Drop and Mean-Difference Flags (MDFlag), and extracted structural statistics (e.g., pressure gradients) from complex 2D FEM data to isolate defect signals.

• Optimized predictive stability on a highly imbalanced dataset (Defect ratio ~15%) by implementing a weighted ensemble model combining TabPFN (neural network for tabular data) and Random Forest.

• Designed a profit-maximizing decision algorithm based on break-even analysis (Good: +100, Defect: -2000) to strictly penalize false negatives, strategically selecting the top 200 optimal tires to maximize Total Net Profit.

• Addressed the high initial volatility and risk of standalone reinforcement learning models by implementing an ensemble technique that computes a weighted average of MVO and PPO asset distribution weights.

• Conducted backtesting using 10 years of S&P 500 market data, achieving a cumulative return that outperformed the standalone Markowitz portfolio by 168% and the PPO-only portfolio by 336%.

• Demonstrated superior risk-adjusted performance and robust risk management by maximizing both the Sharpe and Sortino ratios compared to traditional baseline models.

• Engineered a hands-free, glasses-shaped wearable device designed to assist visually impaired individuals by conveying conversational emotions through a custom-coded vibration sensor.

• Developed deep learning-based multimodal emotion recognition algorithms, achieving 98.55% accuracy in Facial Expression Recognition (FER) using the Xception model and 97.25% in Speech Emotion Recognition (SER) via Conv1D and LSTM.

• Implemented an ESP32 microcontroller for real-time hardware activation via Bluetooth Low Energy (BLE) communication, controlled by a custom-built Flutter mobile application.

• Designed an emotion integration logic that categorizes extracted emotions into positive, negative, and neutral states, cross-validating facial and audio inputs to ensure accurate and refined vibration feedback.