cheeming1016

Directed design and deployment of production-grade AI systems using Python, PyTorch, and AWS, analyzing 12M records and improving clinical outcome prediction with real-world data by 23%. Architected scalable ML data ingestion and feature engineering pipelines with Apache Spark and Airflow, accelerating clinical data processing by 40% across oncology research and real-world evidence workflows. Implemented a model evaluation framework using MLflow and distributed experimentation pipelines, reducing validation time. Deployed ML inference services via Docker and Kubernetes to support AI-driven clinical decision-making. Established MLOps monitoring and model governance with MLflow, Prometheus, and Kubernetes, reducing drift incidents in production health analytics. Mentored 6 AI engineers, guiding ML pipeline development, model deployment, and MLOps workflows.

Designed predictive AI systems using Python, PyTorch, and XGBoost, analyzing 8M+ behavioral and transaction signals to improve fraud and risk detection accuracy by 22%. Built large-scale ML experimentation and hyperparameter optimization pipelines using Apache Spark and MLflow, increasing model performance by 19% across multiple ML models. Reduced deep learning training cycles by 40% through distributed training infrastructure using PyTorch and GPU clusters on AWS. Implemented experiment tracking and model validation workflows using MLflow and Airflow, integrating crossvalidation, performance scoring, and evaluation dashboards to shorten testing cycles by 55%. Deployed real-time machine learning inference services using Docker and Kubernetes, powering predictions for millions of platform users and reducing decision latency by 30%. Mentored 5 machine learning engineers, strengthening development practices across feature engineering, model deployment, and MLOps workflows. Integrated machine learning models into internal data analytics and operational decision platforms, enabling datadriven insights for operations and data science teams. Assisted senior engineers in prototyping early-stage machine learning models for digital mental health support systems.

- Developed scalable distributed machine learning pipelines using Apache Spark on Amazon Web Services, processing 100M+ mobility events to improve demand forecasting precision by 12% and reduce prediction error by 8%. - Applied advanced predictive modeling techniques using XGBoost and Scikit-learn, increasing demand prediction accuracy by 17%. - Analyzed behavioral and mental health datasets containing 400K+ anonymized user records to support predictive analytics and digital mental health AI research. - Implemented model training and experimentation pipelines using PyTorch, accelerating ML experimentation and model iteration cycles by 35%. - Conducted large-scale algorithm benchmarking and model evaluation to optimize route prediction and mobility demand forecasting models.