Tyler Maunu's webpage

About Me

I am an Instructor of Applied Mathematics at MIT. Previously, I obtained my PhD in Mathematics and MS in Statistics at the University of Minnesota in 2018. My current research interests span statistics, machine learning, computer vision, and nonconvex optimization. In the past, I have specifically focused on the problem of robust subspace recovery.



  • Tyler Maunu and Gilad Lerman, Robust Subspace Recovery with Adversarial Outliers, 2019.
  • Tyler Maunu, Teng Zhang, and Gilad Lerman, (in press) A Well-Tempered Landscape for Non-convex Robust Subspace Recovery, 2019.
  • Gilad Lerman and Tyler Maunu, An Overview of Robust Subspace Recovery, Proceedings of the IEEE, 2018.
  • Gilad Lerman and Tyler Maunu, Fast, Robust and Non-convex Subspace Recovery, Information and Inference: A Journal of the IMA, 2017.


  • Robust subspace recovery
    • Information Theory and Applications Workshop, San Diego, 2016.
    • Yale Applied Math Seminar, 2017.
    • IMA Data Science Lab Seminar University of Minnesota 2016,2017.
    • SILO Seminar, University of Wisconsin, Madison, 2018.
    • SIAM Conference on Applied Linear Algebra, Hong Kong, 2018.
    • INFORMS Annual Meeting, Phoenix, 2018.
    • UCLA IPAM Geometry and Learning from Data Tutorials, 2019.
    • Recent Themes in Resource Tradeoffs: Privacy, Fairness and Robustness, IMA, University of Minnesota, 2019.



maunut (at) mit.edu