Neda Shahidi

The University of Texas at Austin, University of Tehran

Papers

Total Citations

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About

Neda Shahidi’s research lies at the intersection of robotics, autonomous systems, and hardware acceleration for artificial intelligence. Her most influential work tackles the fundamental challenge of path planning for mobile robots, introducing a memetic algorithm that combines evolutionary search with local refinement. Her novel solution representation proved highly efficient for generating optimal, collision-free paths, earning 25 citations and establishing a foundation for adaptive robotic navigation. Shahidi also contributed to the future of autonomous transportation through her work on batch reservations for intersection management, a concept that enables multiple autonomous vehicles to coordinate seamlessly without traffic signals, anticipating the widespread adoption of self-driving cars. Beyond software algorithms, she explored dedicated hardware for neural networks, designing the Neural Networks Stream Processor (NnSP) to exploit parallelism and locality for time-critical applications. This embedded architecture demonstrated how specialized processors could accelerate neural computation in real-world, latency-sensitive environments. Shahidi’s work bridges algorithmic innovation with practical hardware implementation, offering valuable insights for researchers developing intelligent, autonomous systems that must operate efficiently under real-world constraints.