profile photo

Nathan Jacobs

Nathan Jacobs earned a Ph.D. in Computer Science at Washington University in St. Louis (2010). After many years at the University of Kentucky, he is currently a Professor in the Computer Science & Engineering department at Washington University in St. Louis. Dr. Jacobs' research area is computer vision; his specialty is developing learning-based algorithms and systems for processing large-scale image collections. His current focus is on developing techniques for mining information about people and the natural world from geotagged imagery, including images from social networks, publicly available outdoor webcams, and satellites. His research has been funded by NSF, NIH, DARPA, IARPA, NGA, ARL, AFRL, and Google.

Research Lab  /  CV  /  Scholar  /  Github  /  LinkedIn

current roles

selected recent publications

See our my lab's page for a complete listing.
  1. Qiao F, Xiong Z, Xing E, Jacobs N. 2025. Towards Open-World Generation of Stereo Images and Unsupervised Matching. In: IEEE International Conference on Computer Vision (ICCV).
    bibtex | paper | website | linkedin | code
  2. Sastry S, Dhakal A, Xing E, Khanal S, Jacobs N. 2025. Global and Local Entailment Learning for Natural World Imagery. In: IEEE International Conference on Computer Vision (ICCV).
    bibtex | paper | website
  3. 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
  4. 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
  5. 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
  6. a thumbnail for TaxaBind: A Unified Embedding Space for Ecological Applications
    Sastry S, Khanal S, Dhakal A, Ahmad A, Jacobs N. 2025. TaxaBind: A Unified Embedding Space for Ecological Applications. In: IEEE Winter Conference on Applications of Computer Vision (WACV).
    bibtex | paper | website | linkedin | code
  7. a thumbnail for Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation
    Kerner H, Chaudhari S, Ghosh A, Robinson C, Ahmad A, Choi E, Jacobs N, Holmes C, Mohr M, Dodhia R, Ferres JML, Marcus J. 2025. Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation. In: Association for the Advancement of Artificial Intelligence (AAAI).
    bibtex | paper | website | linkedin
  8. a thumbnail for GOMAA-Geo: GOal Modality Agnostic Active Geo-localization
    Sarkar A, Sastry S, Pirinen A, Zhang C, Jacobs N, Vorobeychik Y. 2024. GOMAA-Geo: GOal Modality Agnostic Active Geo-localization. In: Neural Information Processing Systems (NeurIPS).
    bibtex | paper | linkedin | code
  9. a thumbnail for PSM: Learning Probabilistic Embeddings for Multi-scale Zero-shot Soundscape Mapping
    Khanal S, Xing E, Sastry S, Dhakal A, Xiong Z, Ahmad A, Jacobs N. 2024. PSM: Learning Probabilistic Embeddings for Multi-scale Zero-shot Soundscape Mapping. In: ACM Multimedia. DOI: 10.1145/3664647.3681620.
    bibtex | paper | doi | linkedin
  10. a thumbnail for FroSSL: Frobenius Norm Minimization for Self-Supervised Learning
    Skean O, Dhakal A, Jacobs N, Giraldo LGS. 2024. FroSSL: Frobenius Norm Minimization for Self-Supervised Learning. In: European Conference on Computer Vision (ECCV).
    bibtex | paper | linkedin