Specializations

  • Natural language processing
  • Multimodal models, including vision language models, speech, and diffusion systems
  • Multilingually and multiculturally representative systems

Biography

Michael Saxon is a Siegel Postdoctoral Fellow with the Tech Policy Lab and the Information School at the University of Washington. His research sits on the intersection of generative model benchmarking, multimodality, and AI ethics. He is particularly interested in difficult evaluation questions that arise in multimodal systems, and in developing methods to make systems performant and authentically user-responsive across languages and cultures. Saxon earned his bachelor’s in Electrical Engineering and master’s in Computer Engineering at Arizona State University, and his Ph.D. in Computer Science at the University of California, Santa Barbara, advised by William Wang.

Education

  • Ph D, Computer Science, University of California, Santa Barbara, 2025
  • MS, Computer Engineering, Arizona State University, 2020
  • BS, Electrical Engineering, Arizona State University, 2018

Awards

  • Neal Fenzi—Resonant Founder Fellowship - University of California, Santa Barbara, 2024
  • Rising Star in Generative AI - UMass Amherst Rising Stars Workshop, 2024
  • Outstanding Reviewer Award - ACL 2023, 2023
  • Center for Responsible Machine Learning Fellowship - University of California, Santa Barbara, 2020
  • Graduate Division Central Fellowship - University of California, Santa Barbara, 2020
  • National Science Foundation Graduate Research Fellowship - National Science Foundation (NSF GRFP), 2020

Publications and Contributions

Presentations

  • How to nitpick multimodal evaluations (2025)
    CVPR 2025 - Virtual
  • Multilingual multimodal evaluation: how and why (2025)
    Google Translate Research - Mountain View, CA
  • Rigorous measurement in text-to-image systems (2024)
    UMD CLIP Seminar - College Park MD
  • Rigorous measurement in text-to-image systems (2024)
    Stanford SALT Group - Palo Alto, CA
  • Rigorous measurement in text-to-image systems (2024)
    Georgetown University - Washington DC
  • Disparities in Text-to-Image Model Conceptual Knowledge Across Languages (2023)
    2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT) - Chicago, IL
  • Rigorous measurement in text-to-image systems (2023)
    USC Information Sciences Institute - Marina Del Rey, CA
  • Rigorous measurement in text-to-image systems (2023)
    Arizona State University - Tempe, AZ