Daniel Schwyn
Papers
2
Total Citations
5
H-Index
2
About
Daniel Schwyn is a researcher focused on the intersection of machine learning, power management, and quality-of-service (QoS) in modern computing systems. His key research areas include reinforcement learning-based dynamic power management and platform-agnostic resource management for embedded and compute-aware systems. Schwyn’s major contributions center on developing intelligent, adaptive frameworks that balance energy efficiency with application performance. His 2022 paper, "Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM," demonstrates how reinforcement learning can proactively manage power in increasingly powerful embedded devices, while his 2021 work, "2QoSM: A Q-Learner QoS Manager for Application-Guided Power-Aware Systems," introduces a Q-learning-based manager that enables applications and hardware to share performance metrics for optimized resource allocation. Though his citation counts are currently modest—3 and 2 citations respectively—these works represent foundational steps toward more autonomous, efficient computing. Schwyn’s research is particularly notable for its practical approach to integrating machine learning into resource-constrained environments, offering a promising path for future energy-aware system design.
Research Focus
Key Achievements
Top Papers
- 1Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM3 citations · 2022
- 2