Maryam Mehri Dehnavi
Papers
1
Total Citations
24
H-Index
1
About
Maryam Mehri Dehnavi is a leading researcher at the intersection of high-performance computing, machine learning, and neuroscience. Her work focuses on developing efficient algorithms and systems for large-scale scientific simulations and brain-inspired computing. She is best known for her contributions to spiking neural networks (SNNs) and neuromorphic computing, where she bridges the gap between biological plausibility and computational efficiency. Her highly cited paper, "From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?" (2023, 24 citations), offers a transdisciplinary review that connects neuroscience, AI, and robotics, highlighting the challenges and opportunities in designing energy-efficient, brain-like systems. Dehnavi’s research has significant implications for autonomous systems, real-time robotics, and next-generation AI hardware. Her work is recognized for its impact on both theoretical foundations and practical implementations, making her a key figure in advancing neuromorphic engineering and embodied cognition.
Research Focus
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Top Papers
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