Machine Learning · Optimization · Data Analytics Engineer

안녕하세요, 이효연입니다. 저는 엔드투엔드 데이터 및 머신러닝 시스템, 의사결정, 최적화, 견고하고 신뢰할 수 있는 머신러닝, 그리고 양적 연구에 관심이 있습니다.

Technical Skills

Work Experience

• AI model optimization at large-scale manufacturing sites. On real DRAM mass-production lines, deployed ML optimization frameworks on high-volume test streams and achieved record-high yields for new products. Engineered Bit Line Sense Amplifier (BLSA) optimization with Python and C/C++; built a cloud edge-computing–based platform that performs real-time optimization and automation across multi-device testing environments. Afterward, I directly set up the ML optimization system for all incoming DRAM product requests.
• Big-data & platform engineering. Designed and operated a TB-scale parallel processing framework using Dask, PyArrow, and AWS Glue to handle production logs. Fully automated anomaly detection via Airflow pipelines. Authored seven reusable automation tools that reduced repetitive tasks by ~90%, with results validated by team and division awards.
• Independent R&D (Algorithms · Causal/RL). Performed defect-cause inference using ML data analytics, hybrid ML tuning for process parameters, and robust reinforcement learning. Results include an IEEM-accepted paper and presentation, two patents, and CEO/Best Paper awards.
• Leadership · Communication · Education. Served as Culture Agent for a 50-member team and taught internal ML seminars (won Best Seminar Award). Consistently connected seminar/research insights to independent decision-making on new algorithm development projects.

Research Experience

• Statistics Consultant, Korea University Statistics Consulting Center (2019–2021). Analyzed chronic kidney disease progression using survival models on clinical time-series data. Identified early dementia biomarkers via structural equation modeling and cluster analysis. Evaluated impact of teaching methods through factor analysis and regression in child education study.
• Research Assistant, National Statistical Research Center (2018–2019). Performed Bayesian change-point survival analysis to evaluate vaccine effectiveness and durability. Developed statistical learning methodologies for personalized medical treatment recommendations. Conducted semi-parametric survival analysis for dependent censoring and dynamic treatment regimes.

(More details on CV.)

Program Projects

Project details and demos are organized on the Projects page.

Company Projects

Company-internal projects; details are omitted due to confidentiality.

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