Mishal Fatima Minhas
Papers
1
Total Citations
6
H-Index
1
About
Mishal Fatima Minhas is a rising researcher at the forefront of neuromorphic computing and continual learning, two transformative fields shaping the future of efficient artificial intelligence. Her work addresses a critical bottleneck in modern AI: the unsustainable computational and memory demands of deep neural networks when learning continuously over time. In her highly cited 2025 paper, "Continual Learning With Neuromorphic Computing: Foundations, Methods, and Emerging Applications," she systematically bridges the gap between brain-inspired hardware and lifelong learning algorithms, offering a comprehensive roadmap for deploying energy-efficient, adaptive systems. This foundational survey has already garnered significant early attention, with six citations reflecting its immediate impact on the community. Minhas’s contributions are particularly notable for their interdisciplinary scope, synthesizing principles from neuroscience, hardware design, and machine learning to propose practical solutions for real-world deployment. Her work is poised to influence applications ranging from autonomous systems to edge computing, where low-power, continuously learning models are essential. As an emerging voice in this niche, Minhas is helping to steer the paradigm shift away from brute-force computation toward biologically plausible, sustainable AI—a mission that promises to reshape how intelligent systems learn and adapt in resource-constrained environments.
Research Focus
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Top Papers
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