Joon-Ku I.

Background

About Joon-Ku I.

I build machine learning systems that actually ship and get used in production. Over the past 10+ years, I've learned that the difference between a good model and one that creates business value isn't just technical elegance—it's understanding what problem you're solving and making it work despite real-world constraints.At Elevance Health, I've built 40+ production clinical prediction models covering 40 million members. My work includes maternity risk models supporting outreach to pregnant Medicaid members annually—predicting C-section risk, gestational diabetes, NICU needs, and identifying pregnancies early so care managers can ensure OBGYN visits and healthy deliveries. I also developed readmission risk models that help prioritize care management for recently discharged members, plus surgery prediction, cost forecasting, and ER utilization models. These systems have been running in production for 5 years using XGBoost and LightGBM, and I established the development processes and validation frameworks that let our team maintain quality at scale.Recently, I've expanded into GenAI and LLM systems. I built a text-to-SQL platform using PyAutoGen multi-agent framework that lets STARS campaign managers query healthcare data in plain English, plus call center automation tools processing 400K daily calls for real-time summarization and audit extraction. I also lead an 18-member cross-functional team (7 onshore, 11 offshore) and I'm known as the go-to person for troubleshooting production issues.What drives me is translating ambiguous business requirements into solutions that work—navigating compliance requirements, legacy systems, and enterprise constraints to deliver systems that care managers and business teams actually use. I'm equally comfortable building traditional ML pipelines or cutting-edge GenAI applications, as long as they solve real problems.Tech stack: Python, XGBoost, LightGBM, PyAutoGen, FastAPI, Snowflake, OpenAI API, LangChainDomains: Healthcare analytics, clinical prediction, cost forecasting, member risk stratificationKeywords: Machine Learning, Production ML Systems, Healthcare AI, Predictive Analytics, GenAI, NLP