Huijo Kim
I am a machine learning practitioner and former founder working across predictive modeling, computer vision, MLOps, and autonomous systems. After studying mechanical engineering, I worked in the electric vehicle development sector at Hyundai Motor Group, contributing to large-scale, safety-critical automotive systems.
I later founded and scaled an agtech startup from zero to a six-figure ARR business. This experience shaped my focus on building technology that delivers measurable, real-world value rather than chasing technical hype. After exiting, I transitioned into the e-commerce domain, applying machine learning to large-scale experimentation and operational optimization.
My background includes graduate research in robotics, published work in applied machine learning, and hands-on experience deploying end-to-end ML systems. I am particularly interested in explainability-driven optimization, agent-based workflows, and cross-disciplinary system design. I believe polymath practitioners—those who can bridge domains—will be especially valuable in the era of AI.
Session
Agent-Based Hyperparameter Optimization for Gradient Boosted Trees
Hyperparameter optimization for gradient boosted tree models is a repetitive yet cognitively demanding task. Practitioners must combine statistical intuition with detailed, library-specific knowledge—often buried across hundreds of pages of documentation for tools such as XGBoost, LightGBM, or CatBoost. As models and configurations grow in complexity, traditional approaches like grid search, random search, or even Bayesian optimization struggle to incorporate semantic understanding of model behavior.
Using XGBoost as a concrete case study, I demonstrate how RAG-powered agents, orchestrated in a structured workflow, can analyze model behavior via SHAP values, diagnose failure modes (e.g. overfitting, feature dominance, interaction leakage), and propose targeted hyperparameter adjustments grounded in both theory and library-specific constraints.
The system combines open-source tools including XGBoost, SHAP, Optuna, and LangGraph/LangChain, where agents specialize in tasks such as model diagnostics, documentation-aware parameter reasoning, and experiment orchestration. Rather than replacing existing optimization frameworks, agents operate on top of them, injecting domain knowledge and interpretability signals into the optimization loop.