Joshua P. Gardner

Paul G. Allen School of Computer Science and Engineering
Google Artificial Intelligence Lab
The University of Washington

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I am currently pursuing a PhD in computer science at the University of Washington's Paul G. Allen School of Computer Science & Engineering, where I am fortunate to be advised by Ludwig Schmidt and Zoran Popović . I hold an M.S. in Applied Statistics and an M.S. in Information Science from the University of Michigan. I also hold a B.A. with Highest Honors in Philosophy from the University of Michigan.

My research focuses on empirical machine learning: characterizing the conditions under which modern machine learning models succeed and fail, and using this understanding to develop improved methods for machine learning. My current research centers on training and fine-tuning large "foundation"-type models and empirically assessing their prediction, robustness, and generalization capabilities, with the aim of using this empirical understanding to select and design new methods that address these limitations. I have studied a diverse set of domains and applications under this general theme, including tabular and structured data; multimodal learning; music and audio; and federated and collaborative learning.

Previously, I was fortunate to spend Summer 2023 as a Research Scientist Intern at Spotify Research building LLark. Before that, I spent just shy of two years as a Research Intern + Student Researcher on the Magenta team at Google DeepMind (fka Brain), working on core machine learning problems in the music and audio domain, including MT3 (see additional publications here).

Awards and honors for my past work include a Best Paper Award at the International Conference on Learning Analytics and Knowledge (LAK), the Margaret Mann Award, the UMSI Professional Practice Fellowship, and the William K. Frankena Prize.

Selected Publications

For a full list of publications see my research page or Google Scholar profile.