homotopySalman Asif
 |
M. Salman Asif Email: sasif@ucr.edu Office: 4126 Multidisciplinary Research Building (MRB) |
I am an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Riverside.
Research interests. My research interests broadly lie in the areas of computational imaging, signal/image processing, computer vision, and machine learning. I design algorithms to learn and exploit latent signal structures for fast and efficient information recovery.
Current research focus is on computational imaging (physics-based and data-driven approaches), adversarial attacks and defenses for robust computer vision, multi-task and multi-modal machine learning, and wearable sensors for health monitoring and rehabilitation.
Positions available
Prospective PhD students: I am looking for new Ph.D. students to join my group for projects related to computational imaging and multi-modal machine learning.
A successful candidate should have solid understanding of Linear Algebra, programming (Matlab and Python), basics of Convex Optimization, and interest in tinkering with hardware prototypes (no special skills needed, but should be able to assemble a lego set with or without instructions).
Important: If you send me an email, please include your CV/resume and a brief statement of your research interests. I try to respond to as many emails as I can, but please do not be offended if you do not receive a response.
UCR offers PhD admissions in Fall, Winter, and Spring quarters. Please look at Graduate admissions page for details.
Undergraduate students: If you are at UCR and want to work on computational imaging, machine learning, or computer vision, get in touch.
News and updates
January 2023: Two journal papers on coded illuminations and lensless imaging. Congratulations to Yucheng!
January 2023: One journal paper on resolution analysis of masks-based lensless cameras.
September 2022: Two papers accepted to NeurIPS 2022. Congratulations to lead students Zikui, Abhishek, and all the collaborators!
Blackbox Attacks via Surrogate Ensemble Search
TLDR: Optimizing a weighted loss function over surrogate ensemble provides highly successful and query efficient blackbox (targeted and untargeted) attacks.GAMA: Generative Adversarial Multi-Object Scene Attacks
TLDR: A joint vision/language model (such as CLIP) provides image and text embeddings. The generative model learns to generate perturbed images that are close to image embedding and far from the text embedding of the clean images.
August 2022: One paper on Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks accepted to WACV 2023 (Round 1).
July 2022: Our paper on Incremental Task Learning with Incremental Rank Updates accepted to ECCV 2022. Congratulations to Rakib, Ken, Boyu and the collaborators!
June 2022: One paper on Physics-Guided Neural-Network-Based Inverse Design of a Photonic-Plasmonic Nanodevice for Superfocusing accepted to ACS Applied Materials & Interfaces (joint work with Ming Liu group).
May 2022: One paper on Low-Light Color Imaging Via Cross-Camera Synthesisaccepted to IEEE JSTSP (joint work with Zhan Ma group).
March 2022: One paper accepted to CVPR 2022. Congratulations to Zikui and all the collaborators!
Zero-Query Transfer Attacks on Context-Aware Object Detectors (joint work with Zikui Cai, Shantanu Rane, Alejandro Brito, Srikanth Krishnamurthy, Chengyu Song, and Amit Roy-Chowdhury)
Research interests
My research interests broadly lie in the areas of computational imaging, signal/image processing, computer vision, and machine learning. I design algorithms to learn and exploit latent signal structures for fast and efficient information recovery. Recently, I have been working on some projects in computational imaging, adversarial computer vision, multi-modal machine learning, and soft, wearable robotics.
Computational imaging
Machine learning
Computer vision
Compressive sensing
Signal and image processing
Convex optimization and numerical methods