Sadia Anjum Tumpa
Papers
1
Total Citations
3
H-Index
1
About
Sadia Anjum Tumpa is a rising researcher at the intersection of neuromorphic computing and embedded computer vision, with a focus on enabling efficient perception for autonomous systems. Her most cited work introduces a novel hybrid architecture that synergistically combines spiking neural networks (SNNs) and artificial neural networks (ANNs) for monocular depth estimation in resource-constrained environments. This approach leverages the temporal precision of event-based cameras and the robustness of frame-based data, achieving state-of-the-art performance on multimodal depth estimation tasks while maintaining low power consumption—a critical requirement for real-time applications in autonomous driving, robotics, and augmented reality. With 3 citations already for her 2024 paper, Tumpa’s contributions are gaining traction in the neuromorphic computing community. Her work addresses a fundamental challenge: bridging the gap between biological plausibility and practical deployment, offering a scalable solution for embedded systems that must operate reliably in dynamic, low-latency scenarios. As an early-career researcher, Tumpa is establishing herself as a key voice in the push toward energy-efficient, event-driven vision systems.
Research Focus
Key Achievements
Top Papers
- 1