Christopher Settles

Christopher Settles is a Machine Learning Engineer at Uber, where he has been employed since February 2021. He transitioned from a software engineering role to focus on machine learning applications, specifically within the Risk team. His work primarily involves developing machine learning infrastructure to combat fraud and abuse on Uber's platform.13

Education and Early Career

Settles graduated from the University of Illinois Urbana-Champaign in December 2020, where he completed both his undergraduate and graduate studies in Computer Science. His graduate research included building infrastructure for privacy-preserving machine learning models, which garnered funding for new NSF project grants.24

Before joining Uber, Settles gained experience through various internships, including a role at Google, where he implemented static analysis tools, and a previous internship at Uber focused on developing data pipelines for fraud detection.12

Professional Contributions

At Uber, Settles has made significant contributions by designing a Change Data Capture (CDC) system for the Risk Feature Store, which supports high-volume write operations while optimizing storage and processing costs. He has also worked on productionizing machine learning models that have prevented substantial financial losses due to fraud.13 His expertise extends to utilizing technologies such as SQL, Kafka, and various machine learning frameworks like XGBoost and PyTorch.

Personal Interests

Outside of his professional work, Settles enjoys activities such as running and rowing, and he has participated in events like half marathons. He also expresses interests in technology-related hobbies such as game development.24

Overall, Christopher Settles exemplifies a blend of technical skill and practical application in the field of machine learning and software engineering, particularly within the context of risk management at Uber.