Samuel received his B.S.E. in Electrical Engineering from Princeton University in 2019. His research interests include computer architecture, systems for machine learning, and hardware support for deep recommendation systems.

Samuel's publications

• DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference
• RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference
• Cross-Stack Workload Characterization of Deep Recommendation Systems
• RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance