Alexandra Shanafield
Papers
1
Total Citations
21
H-Index
1
About
Alexandra Shanafield is a leading researcher at the intersection of neuromorphic computing and edge intelligence, with a primary focus on developing ultra-low-power artificial intelligence for real-world control systems. Her most-cited work, "Evolutionary vs imitation learning for neuromorphic control at the edge" (2021, 21 citations), makes a pivotal contribution by systematically comparing learning paradigms for neuromorphic hardware. This research addresses a critical challenge: determining the most effective training method—evolutionary algorithms versus imitation learning—for deploying spiking neural networks in resource-constrained environments like autonomous vehicles and robotics. By demonstrating the trade-offs between these approaches, Shanafield provides a practical roadmap for engineers seeking to implement efficient, brain-inspired control systems without relying on cloud computing. Her work is particularly notable for bridging theoretical neuromorphic principles with tangible edge applications, offering clear guidance on optimizing performance while minimizing energy consumption. With her research gaining traction in the growing field of green AI, Shanafield is helping to shape the future of autonomous systems that can operate intelligently and independently in power-limited settings.
Research Focus
Key Achievements
Top Papers
- 1Evolutionary vs imitation learning for neuromorphic control at the edge*21 citations · 2021