Salman Asif
Scholar

Salman Asif

Google Scholar ID: Dl0puDcAAAAJ
Associate Professor, ECE, University of California, Riverside
computational imagingcomputer visionmachine learningsignal processingcompressive sensing
Citations & Impact
All-time
Citations
2,501
 
H-index
29
 
i10-index
54
 
Publications
20
 
Co-authors
21
list available
Resume (English only)
Academic Achievements
  • Awarded NSF MEDIUM grant: 'Robustness to Distribution Shifts in Computational Imaging – Inference, Sampling, and Adaptation' (with Ulugbek Kamilov)
  • Received DARPA AIE award for DISCO: 'Discovery of Sparsity-Constrained Optimization Algorithms' (with Chinmay Hegde)
  • Awarded NSF SCH grant: 'Comprehensive Tissue Disease Diagnosis Using a Multimodal Robotic System' (collaborators include Jun Sheng, Chen Li, Sheng Xu, Mustafa Raoof)
  • Paper 'Gaussian is All You Need: A Unified Framework for Solving Inverse Problems via Diffusion Posterior Sampling' accepted to IEEE TCI
  • Paper 'VOccl3D: A Video Benchmark Dataset for 3D Human Pose and Shape Estimation under real Occlusions' accepted to ICCV 2025
  • Paper 'Targeted Unlearning with Single Layer Unlearning Gradient' accepted to ICML
  • Paper 'Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation' accepted to IEEE TPAMI
  • Paper 'Cross-Modal Safety Alignment: Is textual unlearning all you need?' accepted to EMNLP Findings
Background
  • Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Riverside (UCR)
  • Cooperating Faculty in the Department of Computer Science and Engineering
  • Research interests broadly lie in computational imaging, signal/image processing, computer vision, and machine learning
  • Designs algorithms to learn and exploit latent signal structures for fast and efficient information recovery
  • Current research focus includes physics-based and data-driven computational imaging, adversarial attacks and defenses for robust computer vision, multi-task and multi-modal machine learning, and wearable sensors for health monitoring and rehabilitation