Corey Nolet

Corey Nolet is a Principal Engineer at NVIDIA, specializing in machine learning and data science. He is a prominent member of the RAPIDS ML team, where he focuses on developing and scaling machine learning algorithms designed to handle large data loads efficiently. His work emphasizes high-performance and distributed computing, particularly in the context of graph analytics and GPU acceleration.145

Education and Background

Corey is currently a PhD candidate at the University of Maryland, Baltimore County, in the Department of Computer Science and Electrical Engineering. His academic research includes areas like machine learning, data mining, and distributed systems.12

Professional Experience

• NVIDIA: As a senior data scientist and software architect, he has contributed significantly to the RAPIDS project, which aims to accelerate data science workflows using GPU technology. His expertise includes exploratory data analysis (EDA), recommendation systems, and representation learning.34
• Publications: Corey has authored several research papers focusing on machine learning advancements, including topics such as secure aggregation in federated learning and GPU-accelerated genomic analysis.1

Skills and Certifications

Corey holds certifications in Machine Learning and Probabilistic Graphical Models, showcasing his proficiency in advanced analytical techniques. He is also Scrum Certified, indicating his capability in agile project management methodologies.24

Online Presence

He maintains an active online presence through platforms like GitHub and LinkedIn, where he shares insights on data science and machine learning practices. His GitHub profile highlights his contributions to libraries for vector search and clustering on GPUs.32