Ruijing Wang

About

Human-AI Design Lab practitioner who productionized a wearable-health anomaly detection system by evolving a standalone autoencoder into a hybrid autoencoder + GPT-based approach, backed by PySpark ETL and MLOps on AWS SageMaker/MLflow. Also has applied LLM troubleshooting experience (fine-tuned FLAN-T5 summarization) and partnered with BI teams to run A/B tests and improve retention via feature stores and experimentation.

Key Strengths

• Took an anomaly detection prototype to production for wearable health data
• Built scalable PySpark ETL pipelines for multivariate time-series ingestion and alignment
• Implemented MLOps workflows using AWS SageMaker and MLflow
• Reduced production latency by redesigning pipelines for real-time/batch processing
• Diagnosed LLM workflow issues using layered triage (infra health to logic/alignment)
• Resolved fine-tuned FLAN-T5 summarization inconsistencies via trace-based debugging plus prompt/vocab/fallback changes
• Effective technical communication via architecture-focused demos and trade-off discussions
• Enabled product experimentation by building feature stores supporting A/B testing and retention modeling
• Delivered measurable impact: larger milestone rewards increased user retention

Experience

Education

Awards

• Fabrycky-Blanchard Award (2024) - School of Systems and Enterprises, Stevens Institute of Technology
• Third-class Scholarship (2014) - Hebei University of Technology
• Third-class Scholarship (2017) - Hebei University of Technology

Skills

Languages