content(cv): sync site CV with 2026-06-23 master CV + redact sample sizes

Bring the CV pages up to date with the latest full CV and apply rule-9
redaction consistently across CV and project pages.

- _pages/cv.md, _pages/ko_cv.md: rewrite EN/KO CV — code-analysis agent
  benchmark (code graph 11,729/38,783, composite 0.977, ~17x cheaper),
  data-standardization robustness (5-fold CV, 1.48ms, suffix ablation,
  RAG holdout), FDA / RT-PCR / PCR-QC projects, Microsoft workshop, full
  Patents + Awards + Teaching sections, expanded Skills.
- _data/cv.yml: refresh dormant data file for repo consistency; drop the
  "~30 initial users" leak; align skills (MySQL, 95% CI/McNemar/Holm,
  KoSRoBERTa).
- _projects/1_ai_platform.md, _pages/ko_projects.md,
  _pages/ko_project_1_ai_platform.md: redact weakness sample sizes
  (151/101 query suites, 50-question set); ratios and metric counts kept.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: kmink3225

_data/cv.yml

@@ -56,10 +56,10 @@ cv:
           modeling for diagnostics. Technical lead / architect on a company-wide
           multi-agent platform.
         highlights:
-          - "Architected a domain-specific multi-agent RAG knowledge platform end-to-end and led it from a single-agent pilot to a company-wide initiative (~30 initial users, expanding company-wide)."
+          - "Architected a domain-specific multi-agent RAG knowledge platform (3 agents on shared Azure infra) end-to-end and led it from a single-agent pilot to a company-wide initiative, delivering 2 agents 2 months ahead of target."
           - "Designed a knowledge QnA chatbot (9 sub-agent Self-RAG/CRAG loop, token streaming, source citation): ~98% user satisfaction, 4.66s avg response, 96.9% citation rate, 5.0/5.0 factuality & reasoning on a 4-model LLM-as-judge evaluation."
-          - "Built a self-developed agent orchestration that benchmarked up to ~17x lower cost per query vs. a general-purpose CLI, validated with paired t-test / McNemar / bootstrap CI on a 6-metric composite."
-          - "Delivered an NLP-based data standardization system: validation time 8h -> 0.73s (99% reduction), metadata consistency 8.4% -> 98.7%; an 8-model classifier benchmark (14 classes, 95% CI, McNemar+Holm) selected KLUE-RoBERTa at 96.88% and proved a 671K-param BiLSTM statistically on par with a 110M model."
+          - "Built a self-developed agent orchestration grounding ~400K lines of Python (40K AST facts, an 11,729-node / 38,783-edge code graph) that won a 7-variant benchmark (composite 0.977) at up to ~17x lower cost per query vs. a general-purpose CLI, validated with paired t-test / McNemar / bootstrap CI on a 6-metric composite."
+          - "Delivered an NLP-based data standardization system: validation time 8h -> 0.73s (99% reduction), metadata consistency 8.4% -> 98.7%, completeness 29.6% -> 100%; an 8-model classifier benchmark (14 classes, 7,698 samples, 95% CI, McNemar+Holm) selected KLUE-RoBERTa at 96.88%, and 5-way robustness checks proved a 671K-param BiLSTM statistically on par with the 110M model at 1.48ms inference."
           - "Redesigned a hard-coded PCR signal baseline algorithm into a data-driven model, cutting the false-negative rate 0.47% -> 0.04% (91.49% improvement)."
           - "Automated diagnostic-equipment QC with a two-stage LSTM + 10 quality metrics over 61,248 signals, cutting QC time ~93% (~13x annual operating-cost reduction) — R&D President's Award."
           - "Established a model evaluation & MLOps baseline: LLM-as-judge auto-scoring + architecture A/B benchmarking (paired t-test, McNemar, Cohen's d, bootstrap CI) + metric logging; mentored 20+ engineers across IT/BT."
@@ -81,15 +81,15 @@ cv:
 
       - name: NLP / Deep Learning
         icon: fa-solid fa-brain
-        keywords: "KLUE-RoBERTa, KoBERT, ALBERT, BiLSTM/LSTM, Hugging Face Transformers, PyTorch, KiwiPiePy/KoNLPy"
+        keywords: "KLUE-RoBERTa, KoBERT, ALBERT, KoSRoBERTa, BiLSTM/LSTM, Hugging Face Transformers, PyTorch, KiwiPiePy/KoNLPy"
 
       - name: Machine Learning / Statistics
         icon: fa-solid fa-chart-line
-        keywords: "scikit-learn, HDBSCAN, regression/survival analysis, time series, causal inference (A/B test), experiment design"
+        keywords: "scikit-learn, HDBSCAN, regression/survival analysis, time series, causal inference (A/B test), experiment design, 95% CI, McNemar/Holm"
 
       - name: Data / Backend Engineering
         icon: fa-solid fa-server
-        keywords: "Python, R, SQL (PostgreSQL), SAS, FastAPI, Streamlit, Apache Airflow, Parquet, AST, Docker, Azure DevOps"
+        keywords: "Python, R, SQL (MySQL/PostgreSQL), SAS, FastAPI, Streamlit, Apache Airflow, Parquet, AST, Docker, Azure DevOps"
 
     Awards:
       - title: Patents filed (7 total; first inventor on 4)

_pages/cv.md

@@ -16,56 +16,100 @@ toc:
 
 ## Summary
 
-AI Engineer / Data Scientist with 7+ years of experience architecting and building enterprise AI platforms (RAG, LLM agents, NLP) end-to-end, backed by statistically rigorous evaluation. Specialties: LLM agents, RAG/Graph RAG, deep learning / NLP, machine learning, experiment design, and statistical analysis.
+AI Engineer / Data Scientist with 7+ years of experience, architecting and building enterprise AI platforms (RAG, LLM agents, NLP) end-to-end. I built an enterprise **AI-agent knowledge platform** from the architecture up (**~98% user satisfaction**) and delivered a **data standardization system** (validation time cut **99%**) that is now expanding into a company-wide multi-agent platform under my technical lead. My self-built agent orchestration benchmarked **up to ~18× lower cost** than general-purpose models; I also cut diagnostic-equipment QC operating cost **~13×/yr**, alongside statistically rigorous model evaluation and experiment design. I have led multidisciplinary teams (up to ~20) and filed **7 patents (first inventor on 4)**.
+
+**Specialties:** LLM agents, RAG system design/implementation, NLP / deep learning, machine learning, experiment design, statistical analysis, diagnostic algorithms.
 
 ## Experience
 
 ### Seegene — Data Scientist / AI Engineer
 
-*2020.12 – Present · South Korea*
+*Diagnosis IT General Research Institute · Data Science / Core Dev Team · 2020.12 – Present · South Korea*
 
-Technical lead / architect on a company-wide multi-agent platform; architecting enterprise AI agent / RAG platforms and the statistical evaluation systems behind them. Previously led ML and statistical modeling for diagnostics.
+**Enterprise AI-Agent Knowledge Platform** — Technical Lead / AI Architect, 2025.11 – Present
 
-- Architected a domain-specific **multi-agent RAG knowledge platform** end-to-end and led it from a single-agent pilot to a company-wide initiative.
-- Designed a **knowledge QnA chatbot** (9 sub-agent **Self-RAG/CRAG** loop, token streaming, source citation): ~98% user satisfaction, 4.66s avg response, 96.9% citation rate, 100% system success; 5.0/5.0 factuality & reasoning on a 4-model LLM-as-judge evaluation.
-- Built a **self-developed agent orchestration** that benchmarked **up to ~17× lower cost per query** vs. a general-purpose CLI (paired t-test / McNemar / bootstrap CI on a 6-metric composite).
-- Delivered an **NLP-based data standardization system**: validation time **8h → 0.73s (99%↓)**, metadata consistency 8.4% → 98.7%; an 8-model classifier benchmark (14 classes, 95% CI, McNemar+Holm) selected KLUE-RoBERTa at 96.88% and proved a 671K-param BiLSTM statistically on par with a 110M model.
-- Redesigned a hard-coded PCR signal baseline algorithm into a **data-driven model**, cutting the false-negative rate **0.47% → 0.04% (91.49%↓)**.
-- Automated diagnostic-equipment QC with a **two-stage LSTM + 10 metrics** over 61,248 signals, cutting QC time ~93% (≈13× annual operating-cost reduction) — R&D President's Award.
-- Established a model evaluation & MLOps baseline (LLM-as-judge auto-scoring + architecture A/B benchmarking + metric logging); mentored 20+ engineers across IT/BT.
+- Led end-to-end architecture of a domain-specific **multi-agent RAG platform** for company-wide data assetization (deployed to working-level staff, expanding company-wide) — three agents (knowledge QnA, data standardization, sequence-recommendation code analysis) on shared Azure infrastructure; scaled a single-agent plan into a multi-agent flagship and delivered two agents **2 months ahead of target**.
+- **Knowledge QnA chatbot** — 9 sub-agent **Self-RAG/CRAG** loop with token streaming and source citation over a Parent-Child + hybrid-search (BM25 + vector) RAG pipeline; passed all 10 metrics (**4.66s** avg response, 96.9% citation rate, 100% system success, 95.6% retrieval success), **5.0/5.0** factuality & reasoning (gpt-4.1) on a 4-model LLM-as-judge eval, **~98%** user satisfaction.
+- **Data-standardization assistant agent** — Rule + ALBERT classifier + RAG **hybrid engine** (LangGraph Reflexion loop) auto-recommending three metadata types; passed all 10 metrics, **90.4%** satisfaction, 3.75s avg response, 0% fallback.
+- **Sequence-recommendation code-analysis agent** — grounded ~400K lines of Python (32 repos, 1,453 files) into **40K AST facts**, a code graph (**11,729 nodes / 38,783 edges**), and a 42K search index; benchmarked three architectures (raw general-purpose CLI vs. metadata+skill harness vs. self-built orchestration; 7 variants) on a 6-metric composite + statistical tests — the harness beat the general CLI on answer usefulness (cross-validated by blind practitioner review), and the **self-built orchestration won overall** (GPT-5.4-mini composite **0.977**, 11.6s, $0.076/query), **~17× cheaper** than the costliest variant; nearing production.
+- **Evaluation & MLOps baseline** — LLM-as-judge auto-scoring (factuality / reasoning / out-of-scope / multi-turn) + architecture A/B benchmarking (paired t-test, McNemar, Cohen's d, bootstrap CI) + metric logging; ran **32%** below projected cloud operating cost, with a self-built harness strategy hedging vendor lock-in.
+- Drove **two Microsoft workshops**, persuading an **MS architect and 7 engineers** to adopt the self-built orchestration over a general-purpose Copilot CLI.
 
-### Columbia University Irving Medical Center — Taub Institute · Statistical Research Assistant
+**NLP-Based Data Standardization System** — Technical Lead (mentored 20+ across IT/BT), 2024.10 – 2025.09
+
+- Defined the metadata-inconsistency problem and led an **NLP + Rule + RAG** standardization system end-to-end; after a successful pilot it went **company-wide** and seeded the follow-on AI-agent platform.
+- Outcomes (user survey + ops): validation time **8h → 0.73s (99%↓)**, cross-team inquiries **70 → 4/mo (94.3%↓)**, metadata consistency **8.4% → 98.7%**, completeness **29.6% → 100%**.
+- **8-model classifier benchmark** (KLUE-RoBERTa, XLM, KoBERT, ALBERT, mBERT, BiLSTM, DistilKoBERT, e5; 14 classes, 7,698 samples, stratified, 95% CI, McNemar+Holm over 28 pairs) → **KLUE-RoBERTa 96.88%** (top-5 transformers statistically tied).
+- **Robustness / 5-way cross-validation** — 5-fold CV showed a **671K-param BiLSTM statistically on par with the 110M KLUE** (96.18%±0.41% vs. 96.35%, p=0.73) at **1.48ms inference** (vs. 12.49ms); suffix ablation (−51%p), a RAG holdout (rejected synthetic-overfit), and a noise floor diagnosed the accuracy ceiling as a data limit.
+- **Training-data engineering** — curated 9,168 items from three sources (LLM, rules, RAG) → label normalization, 29 conflicts resolved, 1,466 deduplicated → 7,698; built dictionaries of 582 standard terms and 147 domain mappings.
+- **Rule-based naming-standardization engine** (14 rules + physical-name / abbreviation generation); synonym clustering (ko-sroberta-multitask + HDBSCAN, 2,048 → 569 clusters); **pytest (60), GitHub Actions CI, Docker** for reproducible ML.
+
+**Time-Series PCR Signal Baseline-Correction Optimization** — Project PM (DS 3, DE 1), 2024.01 – 2024.09
+
+- Redesigned a hard-coded legacy baseline algorithm into a **mixed-basis data-driven model**, cutting the false-negative rate **0.47% → 0.04% (91.49%↓)**; refactored Matlab → low-level Python with real-time lightweight regression, ranking 1st of 5 competing algorithms on white-noise residual fit.
+
+**FDA-Submission Diagnostic-Algorithm Safety Statistical Analysis** — Project PM (16, multidisciplinary), 2023.05 – 2023.12
+
+- Designed and automated the **statistical V&V pipeline** for FDA software validation, cutting validation time **6 months → 3 weeks (87.5%↓)** at 99.2% statistical confidence; implemented C++-port statistical tests (2-way RM-ANOVA, McNemar, Breslow-Day, Cochran-Mantel-Haenszel), an in-house **Switch Model** ablation, and an Airflow → R + Quarto pipeline generating a 200-page V&V report.
+
+**RT-PCR Diagnostic-Algorithm Reverse Engineering & Statistical Modeling** — Data Scientist (team of 6), 2021.10 – 2023.04
 
-*2018.12 – 2020.05 · New York, US*
+- Reverse-engineered an undocumented legacy Matlab algorithm (10+ stages, 50+ empirical parameters) to **80%** logic/dependency coverage with a C++-port spec; designed an RT-PCR-kinetics logistic-sigmoid composite with joint normal estimation to remove systematic bias.
 
-Large-scale multi-omics analysis for Alzheimer's disease biomarker discovery.
+**PCR-Equipment QC Protocol Design & Performance Grading (A+/A/B/F)** — Project PM (11, multidisciplinary), 2020.12 – 2021.09
 
-- Integrated genomic, metabolomic, and clinical data to surface **13 key biomarkers (p<0.01)** from ~3,000 metabolites; uncovered a confounder missed for eight months.
-- Handled a high-dimensional, small-sample regime (~3,000 variables, far more features than samples); compared 10+ ML algorithms and selected sPLS (84% accuracy with interpretability); built 20-year onset-risk models with Cox hazard / GEE.
+- Automated manual Excel QC with a **two-stage LSTM + 10 quality metrics** grading system: QC time **~400h → 28h per 100 units (93%↓)**, **~13× annual operating-cost reduction**; over 2,201 units and **61,248 signals**, 94.5% pass/fail and 82.7% grade accuracy, with PCA/t-SNE/DBSCAN anomaly detection and an R Shiny dashboard — **R&D President's Award**, **2 first-inventor patents**.
+
+### Columbia University Irving Medical Center — Taub Institute · Statistical Research Assistant
+
+*Research on Alzheimer's Disease and the Aging Brain · 2018.12 – 2020.05 · New York, US*
+
+- Integrated genomic, metabolomic, and clinical data to surface **13 key biomarkers (p<0.01)** from ~3,000 metabolites, resolving a confounder missed for eight months.
+- Worked a high-dimensional, small-sample regime (146 samples × 3,000 variables); compared 10+ ML algorithms and chose **sPLS (84% accuracy with interpretability)**; built 20-year onset-risk models with Cox hazard and family-based GEE — research-competition top 3, Chair's Award, full-time neurosurgery offer.
 
 ## Education
 
-- **M.S. Biostatistics**, Columbia University (2017–2019) — Chair's Award
+- **M.S. Biostatistics**, Columbia University (2017–2019) — Chair's Award (annual graduation research competition)
 - **B.A. Mathematics**, Baruch College, CUNY (2015–2017)
 - **B.S. Biochemistry**, Kangwon National University (2006–2012) — Valedictorian, Dean's Award
 
 ## Skills
 
 - **LLM Agent / GenAI** — RAG, Agentic RAG, Graph RAG, Self-RAG/CRAG, LangChain, LangGraph, Azure OpenAI, Azure AI Search, OpenAI/Claude API, Prompt Engineering
-- **NLP / Deep Learning** — KLUE-RoBERTa, KoBERT, ALBERT, BiLSTM/LSTM, Hugging Face Transformers, PyTorch, KiwiPiePy/KoNLPy
-- **ML / Statistics** — scikit-learn, HDBSCAN, regression/survival analysis, time series, causal inference (A/B test), experiment design
-- **Data / Backend Engineering** — Python, R, SQL (PostgreSQL), SAS, FastAPI, Streamlit, Apache Airflow, Parquet, AST, Docker, Azure DevOps
-
-## Awards & Patents
-
-- **7 patents filed** (first inventor on 4) — Ct-based customized treatment; medical-platform subscription system; diagnostic-equipment noise-test automation; medical-equipment noise-level algorithm (first inventor); molecular-diagnostics prediction model, etc. (co-inventor). Seegene, 2021–2022.
-- **President's Award** (R&D), Seegene, 2021
-- **Chair's Award** — Graduation Practicum Research Competition, Columbia Biostatistics, 2019
-
-## Certificates
-
-- Microsoft Azure — DP-203 (Data Engineering), DP-100 (Data Science), DP-300 (Database), 2025
-- SAS Certified Base Programmer, 2018
+- **NLP / Deep Learning** — KLUE-RoBERTa, KoBERT, ALBERT, KoSRoBERTa, BiLSTM, LSTM, Hugging Face Transformers, PyTorch, KiwiPiePy/KoNLPy
+- **ML / Statistics** — scikit-learn, HDBSCAN, regression/survival analysis, time series, causal inference (A/B test), experiment design, 95% CI, McNemar/Holm
+- **Data / Backend Engineering** — Python, R, SQL (MySQL/PostgreSQL), SAS, FastAPI, Streamlit, Apache Airflow, Parquet, AST
+- **Visualization / Docs** — Plotly, Matplotlib, Seaborn, R Shiny, ggplot2, Quarto, Jupyter, R Markdown
+- **Cloud / DevOps** — Azure, Azure DevOps, Docker, Git/GitHub, Conda
+
+## Patents (filed)
+
+- **(First inventor)** Customized treatment method based on repeatedly-measured Ct values, Seegene (2022)
+- **(First inventor)** Subscription system for a medical platform, Seegene (2022)
+- **(First inventor)** Noise-test automation system for diagnostic equipment, Seegene (2021)
+- **(First inventor)** Noise-level measurement algorithm for medical equipment, Seegene (2021)
+- **(Co-inventor)** Prediction model for molecular diagnostics, Seegene (2022)
+- **(Co-inventor)** Negative certificate for molecular diagnostics, Seegene (2022)
+- **(Co-inventor)** Molecular-diagnostics system for community groups, Seegene (2022)
+
+## Awards & Certifications
+
+- **President's Award (R&D)** — noise-test automation system, Seegene (2021)
+- **Chair's Award** — Graduation Practicum Research Competition, Columbia Biostatistics (2019)
+- **Job Offer** — Taub Institute, Columbia University Irving Medical Center (2019)
+- **Dean's Award** — valedictorian, Kangwon National University (2012)
+- **Microsoft Azure certification training** — DP-203 (Data Engineering), DP-100 (Data Science), DP-300 (Database) (2025)
+- **SAS Certified Base Programmer** (2018), **SIT TESOL Instruction Certification** (2014)
+- **Completion** — EN62304 Medical Device SW Life Cycle (SGS, 2021), HIPAA (CUIMC, 2020)
+- **Stipends** — $1,000 Mathematical Kinetic Modeling, CUNY (2015); $5,000 Medical Convergence Capstone Design, KNU (2012); full academic-excellence scholarship, KNU (2010–2011)
+
+## Teaching & Mentoring
+
+- **Mentor**, Seegene — AI Engineering (2024–2025), Data Standardization (2025), Statistical Analysis (2023–2024), Intro to Statistical Learning (2022)
+- **Teaching Assistant**, Columbia University — Probability Theory (graduate, 2019)
+- **Teaching Assistant**, CUNY — Calculus 1–3, Precalculus, Statistics (undergraduate, 2015–2016)
+- **Private Tutor** — Calculus 1–2 (New York, 2021), GRE Math, TOEFL iBT (New York, 2014–2020)
+- **Trainee Instructor** — SIT TESOL teaching, Rennert (2014)
 
 ## Languages