profile photo

Nathan Jacobs

Dr. Nathan Jacobs is a Professor in the Computer Science & Engineering department at Washington University in St. Louis and Director of the Multimodal Vision Research Laboratory. His research focuses on computer vision, specializing in learning-based algorithms for processing large-scale image collections. His current work develops techniques for understanding the visual world from geotagged imagery, including images from social networks, outdoor webcams, and satellites. His research has been funded by NSF, NIH, DARPA, IARPA, NGA, ARL, AFRL, and Google.

He has graduated 14 PhD students, with 6 placed in tenure-track faculty positions and others at leading technology companies including Microsoft, Zillow, and Kitware. He has also mentored numerous MS students and undergraduates, many of whom have gone on to pursue advanced degrees or careers in industry.

Roles and Affiliations

Selected Achievements

  • National Science Foundation CAREER Award, 2016
  • Google Faculty Research Awards, 2016 and 2018
  • Dean's Award for Excellence in Research, University of Kentucky, 2018
  • Best Paper Award, EarthVision Workshop at CVPR 2024

In the News

    1. press-item
    2. press-item
    3. press-item
    4. press-item
    5. press-item
    6. press-item
    7. press-item
    8. press-item
    9. press-item

Selected Contributions

See my lab's page for a complete listing of publications.

  1. a thumbnail for ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecology
    Sastry S, Khanal S, Dhakal A, Lin J, Cher D, Jarosz P, Jacobs N. 2026. ProM3E: Probabilistic Masked MultiModal Embedding Model for Ecology. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    bibtex | paper | website | press release | code
  2. a thumbnail for SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images
    Dhakal A, Khanal S, Sastry S, Arndt J, Dias PA, Lunga D, Jacobs N. 2026. SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    bibtex | paper | code
  3. a thumbnail for PRUE: A Practical Recipe for Field Boundary Segmentation at Scale
    Muhawenayo G, Robinson C, Khanal S, Fang Z, Corley I, Wollam A, Gao T, Strnad L, Avery R, Estes L, Tárano AM, Jacobs N, Kerner H. 2026. PRUE: A Practical Recipe for Field Boundary Segmentation at Scale. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    bibtex | paper | press release
  4. a thumbnail for DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments
    Sarkar A, Sastry S, Pirinen A, Jacobs N, Vorobeychik Y. 2026. DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments. In: International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
    bibtex
  5. a thumbnail for VectorSynth: Fine-Grained Satellite Image Synthesis with Structured Semantics
    Cher D, Wei B, Sastry S, Jacobs N. 2026. VectorSynth: Fine-Grained Satellite Image Synthesis with Structured Semantics. In: IEEE Winter Conference on Applications of Computer Vision (WACV).
    bibtex | paper
  6. a thumbnail for Towards Open-World Generation of Stereo Images and Unsupervised Matching
    Qiao F, Xiong Z, Xing E, Jacobs N. 2025. Towards Open-World Generation of Stereo Images and Unsupervised Matching. In: IEEE/CVF International Conference on Computer Vision (ICCV).
    bibtex | paper | website | linkedin | code
  7. a thumbnail for Global and Local Entailment Learning for Natural World Imagery
    Sastry S, Dhakal A, Xing E, Khanal S, Jacobs N. 2025. Global and Local Entailment Learning for Natural World Imagery. In: IEEE/CVF International Conference on Computer Vision (ICCV).
    bibtex | paper | website | linkedin | code
  8. a thumbnail for ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval
    Xing E, Kolouju P, Pless R, Stylianou A, Jacobs N. 2025. ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    bibtex | paper | linkedin | code
  9. a thumbnail for RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings
    Dhakal A, Sastry S, Khanal S, Ahmad A, Xing E, Jacobs N. 2025. RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    bibtex | paper | linkedin | code
  10. a thumbnail for Mixed-View Panorama Synthesis using Geospatially Guided Diffusion
    Xiong Z, Xing X, Workman S, Khanal S, Jacobs N. 2025. Mixed-View Panorama Synthesis using Geospatially Guided Diffusion. Transactions on Machine Learning Research (TMLR).
    bibtex | paper | website | linkedin