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Research Scientist

Scott Workman

I develop computer vision and machine learning algorithms that unify disparate sensor signals—ranging from overhead imagery and ground-level views to ambient audio—fusing them to create richer models of our world. By leveraging geo-temporal context as a universal alignment key, my research builds the foundations for multimodal Geospatial AI.

Scott Workman
Computer Vision Machine Learning Geospatial AI Generative AI 3D Vision Multimodal Fusion Remote Sensing

Spotlight Research

Research

My work spans a variety of areas, including view synthesis, multimodal fusion, pose estimation, and autonomous navigation. I am particularly interested in developing methods for combining visual data from multiple sensors and viewpoints with other modalities to create richer, more accurate models of the environment. Additionally, I explore innovative methods for leveraging image metadata—such as location and time (the geo-temporal context of an image)—to improve the performance and reliability of computer vision systems.

Experience

Independent Research Scientist

2025–Present

Principal Research Scientist

DZYNE Technologies · 2021–2025

Senior Research Scientist

DZYNE Technologies · 2019–2021

Ph.D. Computer Science

University of Kentucky · 2012–2018

Recognition

  • Lead Area Chair, CVPR 2026
  • Outstanding Reviewer: CVPR, ICCV, ECCV, NeurIPS
  • Oral Presentation, CVPR 2020
  • Young Researcher, Heidelberg Laureate Forum