Match tier
Confirmed
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-10-05
R
Reeshad Khan
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Research Assistant
1 h-index
5 pubs
6 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Reeshad Khan's research focuses on the application of machine learning and advanced computational techniques to address challenges in medical imaging and autonomous perception. His recent work includes developing methods for unsupervised denoising of magnetic resonance images (MRIs) using diffusion and Bayesian risk approaches, as well as adaptive extensions of unbiased risk estimators. Khan has also investigated efficient deep neural networks for autonomous perception and task-optimal sensor co-design for robust autonomous-driving segmentation. His publications explore areas such as data scarcity in deep learning for MRIs and cross-modal knowledge distillation for multi-task perception and planning.
Metrics
- h-index: 1
- Publications: 5
- Citations: 6
Positions
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Research Assistant 2021–presentUniversity of Arkansas at Fayetteville Computer Science ORCID
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
Ukash Nakarmi
University of Arkansas at Fayetteville
3 shared publications
In database
- 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
- Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising
John M. Gauch
University of Arkansas at Fayetteville
2 shared publications
In database
- 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
John Gauch
University of Arkansas at Fayetteville
2 shared publications
In database
- Beyond Bayer: Task-Optimal Sensor Co-Design for Robust Autonomous-Driving Segmentation
- Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising
Ibsa Jalata
University of Arkansas at Fayetteville
1 shared publication
In database
- 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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