I lead applied AI at a group holding company's AI center, across manufacturing, logistics, bio/healthcare, retail and media. LLM/RAG and agentic systems, mathematical optimization, causal inference and forecasting — owned end to end, from problem definition to deployment and validation.
Three recent builds at CJ AI Center, each patented and in production. Every one started as a problem nobody had framed yet.
OCR with coordinate grounding, RAG over an 11,000-item per-category violation knowledge store, an independent verification unit and retrain-free policy evolution. Flags risky copy with its judgment basis and on-screen location.
Parallel summarization, evidence-highlighting and interview-question agents with an independent self-correcting verification layer. Core models internalized on an open GPT-OSS base; ~73,000 applications across three cycles.
Causal discovery and inference over fragmented process data to find actionable operating conditions, plus an in-silico simulator process experts use to test hypotheses before touching the line. Some conditions became plant standards.
Methodological depth from a statistics Ph.D., applied by hand: each capability below is listed with the production or research work it was used in.
LangGraph workflows, hybrid search and re-ranking, verification agents, prompt and context engineering, self-hosted model internalization, FM evaluation.
MILP, metaheuristics, reinforcement learning, discrete-event simulation, routing, bottleneck and inventory models, constraint modeling.
TFT, PatchTST, DeepAR, Prophet, tree ensembles; causal discovery (PC, PCMCI, Bayesian networks), PSM/IPTW/G-formula, A/B platforms, survival models.
Protein embeddings (ESM2, gLM), structure and clustering tools, microbiome modeling, biostatistics; GAN/diffusion/copula synthetic data with privacy-utility evaluation.
Each demo re-implements the decision logic of a project and lets you change the data and watch the model work. No company data, identifiers or layouts are used; demo pages are in Korean.
Which boxes share a cart and in what order to walk: a lexicographic zone → aisle → travel objective, aisle sweep and ordering DP, real-time cart selection and batch trip planning.
SKU-to-station allocation with affinity scores and a multi-round knapsack DP, then injection sequencing by discrete-event simulation of the ring conveyor and Tabu Search.
MIP-based ordering and stock policy under capacity and transport cost.
The group holding company's central AI organization, taking on cross-affiliate problems that individual companies cannot solve alone. I set technical direction, review architectures, mentor data scientists and work directly with affiliate executives and domain experts.
Vector DB, LangGraph workflow, hybrid search and re-ranking, guardrails for a microbiome-health advisor.
Workload allocation, task priority and bottleneck response, plus an LLM-agent AI Floor Supervisor in operation; multi-center rollout.
ESM2/gLM embeddings, AlphaFold/ESMFold, MMseqs2/Foldseek clustering; vector search over ~6M sequences in 1–2 s.
Order-box mapping, cart allocation, routing and bottleneck waiting reformulated as one problem, with standardized inputs for multi-center rollout.
30+ scheduling rules as MILP constraints with tractability work; occupancy prediction by site and time slot.
Re-scoped a seasonal forecast into year-round store/category forecasting for 300+ stores (TFT, PatchTST, DeepAR) with a Streamlit dashboard.
Daily sales forecasting and a traffic-light system that flags monthly-target risk about a month early.
Cross-affiliate data connected with LLM-based persona estimation and an insight dashboard.
MILP, search, RL and simulation evaluated; demand-based inventory policy proposed.
Customer retention, customer value, experimentation and synthetic data on telecom-scale data across mobile, IPTV and subscription services.
WGAN, CTGAN, CTAB-GAN, TabFair GAN, TTS-GAN, TABDDPM, MTcopula; certified anonymized IPTV, delivery-app and mobility data through the KISA program.
~400 variables; PC, PCMCI, Bayesian networks, PSM, IPTW, G-formula.
Metric definition, sample size, randomization, testing, Bayesian and bandit extensions for IPTV and mobile TV.
TFT + survival CLV; Parallel Louvain household detection; RFM-based engagement; Bass diffusion and Prophet revenue models.
Disease-exacerbation prediction under heavy class imbalance, clinical-trial statistical analysis plans, and early-phase dose-finding methodology (Bayesian logistic regression, CRM, graphical test procedures).
I hold a Ph.D. in Statistics from Yonsei University, where my dissertation developed biosimilarity assessment methodology for time-to-event endpoints. That training — survival analysis, hypothesis testing, experimental design — is still the backbone of how I evaluate every model I ship.
My core strength is redefining a business problem into an analyzable one: translating implicit domain knowledge, operational constraints and business rules into objective functions, evaluation metrics and production-ready systems. I own projects end to end, from problem definition and data pipelines through deployment and performance validation, and I prefer systems that business teams can keep using without me.
Seoul, Korea · Open to applied AI, optimization and data science leadership roles.