Michael Crawshaw

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About Me

I am a Ph.D. candidate in the department of Computer Science at George Mason University, advised by Professor Mingrui Liu. My research is in the theory of optimization for machine learning. In particular, I am working on problems in distributed optimization/federated learning, with the goal of designing optimization algorithms with provable guarantees of computation and communication efficiency under realistic theoretical assumptions. Before studying at George Mason, I received a B.S. in mathematics and computer science from Ohio State University.

Publications

• Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression.
  Michael Crawshaw, Blake Woodworth, Mingrui Liu
  International Conference on Learning Representations, 2025.
• Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness.
  Michael Crawshaw, Mingrui Liu
  International Conference on Learning Representations, 2025.
• Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis.
  Michael Crawshaw, Mingrui Liu
  Neural Information Processing Systems, 2024.
• Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective.
  Yajie Bao, Michael Crawshaw, Mingrui Liu
  International Conference on Machine Learning, 2024.
• Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds.
  Michael Crawshaw, Yajie Bao, Mingrui Liu (first two authors contributed equally)
  Neural Information Processing Systems, 2023.
• EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data.
  Michael Crawshaw, Yajie Bao, Mingrui Liu
  International Conference on Learning Representations, 2023.
• Robustness to Unbounded Smoothness of Generalized SignSGD.
  (Alphabetical order) Michael Crawshaw, Mingrui Liu, Francesco Orabona, Wei Zhang, Zhenxun Zhuang
  Neural Information Processing Systems, 2022.
• Fast Composite Optimization and Statistical Recovery in Federated Learning.
  Yajie Bao, Michael Crawshaw, Mingrui Liu
  International Conference on Machine Learning, 2022.
• Multi-Task Learning with Deep Neural Networks: A Survey.
  Michael Crawshaw
  arXiv:2009.09796, 2020.

Service

• Reviewer for NeurIPS 2023 (awarded Top Reviewer, 8% acceptance).
• Reviewer for AISTATS, ICML, Neurips 2024.
• Review for ICLR, AISTATS 2025.

Teaching

• GMU Graduate Teaching Assistant:
  • CS 657: Mining Massive Datasets (Fall 2020, Fall 2021)
  • CS 471: Operating Systems (Fall 2020, Fall 2021, Spring 2022)
  • CS 583: Analysis of Algorithms (Spring 2021)
  • CS 571: Operating Systems (Spring 2021)
  • CS 330: Formal Methods and Models (Fall 2019, Spring 2020)
• OSU Undergraduate Teaching Assistant:
  • CSE 3321: Automata and Formal Languages (Summer 2017, Fall 2017, Spring 2018)
• OSU Undergraduate Honors Math Mentor:
  • Math 4181H: Honors Analysis I (Fall 2016)
  • Math 4182H: Honors Analysis II (Spring 2017)

Misc

• In my spare time I like to develop open-source projects to use as tools in my life, including a simple arXiv paper recommender and a program to generate lessons for language learning by converting text files into bilingual audiobooks. Both projects are available at my GitHub page.
• My personal record for the 5000m run is 16:28 (this was in 2014, but I intend to be this fast again one day!)
• Mostly, I just want to work on interesting problems and help others do the same.