About me

I am a final year PhD student in computer science at The University of Washington, where I work under Hannaneh Hajishirzi and Ali Farhadi and collaborate closely with Ranjay Krishna. My research spans multimodal foundation and world models, video understanding, long-context modeling, scalable post-training, and large-scale multimodal data systems. Most recently, I was a research intern on the AI Frontiers team at Microsoft Research, working on active vision. Before that, I was a part-time student researcher on the PRIOR team at Ai2, where I led long-context post-training of Molmo2, and a machine learning research intern at Apple. Prior to UW, I received my B.Sc. in computer engineering from Sharif University of Technology. I publish under my full name Mohammadreza, and go by Reza among my friends.

I am on the industry job market! If you think my work aligns with your interests, please reach out at mrsalehi@cs.washington.edu.

News

Honors and Awards

  • 06/2026 Best paper award finalist at CVPR 2026 for Molmo2 (oral).
  • 06/2026 Best paper award at the KnowledgeMR workshop (2nd Workshop on Knowledge-Intensive Multimodal Reasoning @ CVPR 2026) for VideoNet.
  • 06/2025 Best paper honorable mention at CVPR 2025 for Molmo and PixMo.
  • 06/2024 $10k GCP credit award through the Gemini Academic Program.
  • 09/2023 Microsoft Accelerate Foundation Models Research Grant ($20k worth of Azure and OpenAI credit).
  • 06/2023 First place at the AI Tinkerers Gen. AI hackathon (100+ participants) with Sherlock AI, an LLM assistant for tribal knowledge, featured on Cohere's blog.
  • 05/2023 First place at the fixie.ai hackathon with an LLM-powered email assistant.
  • 07/2020 Ranked 3rd in the computer engineering class of 2020 (~110 students) at Sharif University of Technology.
  • 07/2016 Ranked 9th out of 160K participants in the Iranian University Entrance Exam (Konkour).
  • 09/2015 Silver medal in the Iranian National Mathematical Olympiad.

Publications

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MolmoPoint: Better Pointing for VLMs with Grounding Tokens

C. Clark*, Y. Yang*, J.S. Park*, Z. Ma, J. Zhang, R. Tripathi, M. Salehi, S. Lee, T. Anderson, W. Han, R. Krishna

ECCV'26
arXiv
TL;DR ▶

Instead of generating coordinates as text, MolmoPoint points by selecting the visual tokens that contain the target concept via special grounding tokens, followed by finer-grained subpatch and location tokens. It achieves state-of-the-art image pointing and improves video pointing and tracking.

Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding

C. Clark*, J. Zhang*, Z. Ma*, J.S. Park*, M. Salehi*, R. Tripathi*, S. Lee*, et al. (*core contributors)

CVPR'26 Oral Best Paper Award Finalist
arXiv / Code / Docs
TL;DR ▶

An open family of video-language models with strong video understanding and point-driven grounding. I led long-context post-training, scaling to 512+ frames and 65k+ token contexts with context parallelism; our 4B variant outperforms Qwen3-VL 8B on average across long-video benchmarks.

Domain-specific Action Recognition in the VLM Era

T. Yadav, M. Salehi, J.S. Park, H. Hajishirzi, Y. Choi, A. Farhadi, R. Tripathi, R. Krishna

CVPR'26 Highlight Best Paper Award @ KnowledgeMR Workshop
arXiv / Website
TL;DR ▶

VideoNet: a benchmark of 1,000+ domain-specific actions across dozens of domains that tests video-language models with few-shot in-context examples. Fine-tuning Molmo2-4B on our ~500k video QA training set surpasses all open-weight 8B models on the benchmark.

One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory

C. Zheng, J. Zhang, M. Salehi, Z. Gao, V. Iyengar, N. Kobori, Q. Kong, R. Krishna

ICCV'25
arXiv / Code
TL;DR ▶

TrajViT tokenizes videos into panoptic sub-object trajectories instead of space-time patches, cutting tokens by up to 10x while outperforming ViT3D on retrieval and as a VideoLLM encoder.

REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations

P. Sushko, A. Bharadwaj, Z. Lim, V. Ilin, B. Caffee, D. Chen, M. Salehi, C. Hsieh, R. Krishna

CVPR'25
arXiv / Website
TL;DR ▶

A large-scale image editing dataset of real user requests and human-made edits from Reddit. A model trained on it outperforms competitors by up to 165 Elo points in human judgment.

Misc.

Outside of work, I'm into biking, hiking, running, going to the gym, spending time in nature, and reading books. I have a small books page where I write my thoughts about the books I read.