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. This includes understanding models' overall performance, but also their robustness to distribution shift, fairness, and privacy, with the aim of using this empirical understanding to select or 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; music and audio (e.g. transcription, syntheisis); federated and collaborative learning; and reproducibility.
Currently (2022), I am also a Student Researcher on the Magenta team at Google Brain, working on 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.
Subgroup Robustness Grows on Trees: An Empirical Baseline Study.
Josh Gardner, Zoran Popović, Ludwig Schmidt.
Neural Information Processing Systems (NeurIPS) 2022.
MT3: Multi-Task Multitrack Music Transcription
Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse Engel.
International Conference on Learning Representations (ICLR) 2022.
Spotlight Presentation (top 6.7% of submissions)
[arxiv] [web] [blog] [code]
Advances and Open Problems in Federated Learning.
Peter Kairouz, H. Brendan McMahan, [...], Josh Gardner, [...] . (59 authors.)
Foundations and Trends® in Machine Learning (2021): Vol. 14: No. 1-2, pp 1-210.
Evaluating the Fairness of Predictive Student Models Through Slicing Analysis
Josh Gardner, Christopher Brooks, and Ryan Baker.
International Conference on Learning Analytics and Knowledge (LAK) 2019.
Best Paper Award