Summary
Chuhan Feng is a software and machine learning engineer with an MS from Carnegie Mellon and over six years of industry experience building large-scale, low-latency data systems across Amazon, Verizon, and PwC. He blends deep ML R&D and MLOps expertise—designing petabyte ETL pipelines, distributed inference clusters, CI/CD automation, and explainability/drift-detection frameworks—while shipping production services that dramatically cut costs and incidents. At Amazon he optimized inference stacks and helped scale a global product-classification pipeline, and at Verizon he architected a self-service ML observability platform bridging black-box models to business metrics. Comfortable across systems, SDKs, and research prototypes, he pairs rigorous academic training in computational data science with hands-on experience operationalizing models for real-world stakeholders. An attention-to-detail engineer who once helped produce the most detailed Antarctica terrain map in research, he favors measurable impact: throughput, cost, and traceability.
11 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS Mathematics - Theoretical, Bachelor of Science - BS Mathematics - Theoretical at The Ohio State University College of Arts and Sciences
Master of Science - MS Computational Data Science - Analytics, Master of Science - MS Computational Data Science - Analytics at Carnegie Mellon University
Bachelor of Science - BS Computer Science & Engineering - Artificial Intelligence, Bachelor of Science - BS Computer Science & Engineering - Artificial Intelligence at The Ohio State University
Chinese, English