Deepak Sridhar
PhD Student, Computer Vision, Generative Modeling
San Diego, California
I am a fifth year PhD candidate at UC San Diego specializing in Computer Vision, advised by Prof. Nuno Vasconcelos. My research focuses on multimodal foundation models, with particular interests in multimodal generation, editing, and understanding, as well as efficient personalization methods. My PhD thesis focuses on “Efficient Adaptation of Foundation Models” for fine-grained tasks. My work centers on the question: how do we get more capability out of multimodal foundation models with less training and compute?
During my PhD, I have developed an efficient diffusion model framework (project page, NeurIPS’24), explored efficient prompting techniques (project page ECCV’24, project page NeurIPS’26) and worked on efficient video reasoning (project page, CVPRF’26). Prior to my PhD, I have also worked on fundamental problems in computer vision such as efficient image classification, object detection, action recognition and localization.
Outside of Research, I love to go on hiking and exploring the nature. I also love to participate in outdoor adventures.
Canadian Citizen, TN-eligible
news
| Sep 24, 2026 | Our Paper “Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation” is accepted to NeurIPS’26! This paper was an outcome of Qualcomm Innovation Fellowship 2025 Project! |
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| Jun 18, 2026 | Our Paper “MED-DSLC: Multi-Expert-Domain Classification via Domain Supervision and Logit Calibration” accepted to ECCV’26! |
| Feb 20, 2026 | My Qualcomm Internship work “Video Reasoning Without Training” accepted to CVPR Findings 2026 |
| May 27, 2025 | Awarded the “Qualcomm Innovation Fellowship 2025” for our proposal on Meta-Prompting! |
| Sep 29, 2024 | Gave an oral presentation of my PromptSliders paper at the Unlearning and Model Editing Workshop in ECCV’24 |