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.
- 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