Deepak Sridhar
PhD Student, Computer Vision, Generative Modeling
San Diego, California
I am a fourth year PhD student at UC San Diego specializing in Computer Vision, advised by Prof. Nuno Vasconcelos. My research focuses on multimodal generative AI, 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.
During my PhD, I have developed an efficient diffusion model framework (project page, NeurIPS’24), explored efficient prompting techniques (project page, ECCV’24) and worked on efficient video reasoning (project page). My work addresses challenges such as improving prompt compliance, controllability, modularity, and editing in multimodal generation in an efficient manner. 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.
news
| Feb 20, 2026 | My Qualcomm Internship work “Video Reasoning Without Training” accepted to CVPR Findings 2026 |
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| 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 |
| Sep 28, 2024 | Awarded “Outstanding Reviewer Award” at ECCV 2024! |
| Sep 25, 2024 | Paper on “Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis” accepted to NeurIPS’24! |