Reeshad Khan
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Researcher
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Reeshad Khan's research focuses on the application of machine learning techniques to address complex problems in image reconstruction, perception, and restoration. His work investigates methods to improve deep learning-based magnetic resonance image reconstruction, particularly in scenarios with limited data, by systematically exploiting oversampling techniques. Khan also explores efficient multi-task learning for bird's-eye-view perception and planning, developing models like TinyBEV for enhanced performance. Additionally, his research addresses unsupervised denoising through a unified approach combining diffusion models and Bayesian risk, aiming to estimate noise and restore images effectively. Khan has published four papers and has an h-index of 1, with key collaborators including Ukash Nakarmi and John M. Gauch at the University of Arkansas at Fayetteville.
Metrics
- h-index: 1
- Publications: 5
- Citations: 6
Selected Publications
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Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising (2026)
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Efficient Deep Neural Networks for Autonomous Perception (2026)Journal of the Arkansas Academy of Science OpenAlex
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Beyond Bayer: Task-Optimal Sensor Co-Design for Robust Autonomous-Driving Segmentation (2026)arXiv (Cornell University) OpenAlex
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TinyBEV: Cross-Modal Knowledge Distillation for Efficient Multi-Task Bird's-Eye-View Perception and Planning (2025)
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From Noise Estimation to Restoration: A Unified Diffusion and Bayesian Risk Approach for Unsupervised Denoising (2025)
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Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction (2024)
Collaboration Network
Top Collaborators
- Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction
- From Noise Estimation to Restoration: A Unified Diffusion and Bayesian Risk Approach for Unsupervised Denoising
- From Noise Estimation to Restoration: A Unified Diffusion and Bayesian Risk Approach for Unsupervised Denoising
- TinyBEV: Cross-Modal Knowledge Distillation for Efficient Multi-Task Bird's-Eye-View Perception and Planning
- Learning From Oversampling: A Systematic Exploitation of Oversampling to Address Data Scarcity Issues in Deep Learning- Based Magnetic Resonance Image Reconstruction
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