About
I am a third year PhD student in the Department of Computer Science at ETH Zurich, where I am advised by Prof. David Steurer.
I am broadly interested in problems arising in computational learning theory and high-dimensional estimation. More recently, I am also working on questions involving differential privacy.
Education
- 2023 - now: PhD in Computer Science, ETH Zurich
- 2020 - 2022: MSc in Mathematics, ETH Zurich
- 2017 - 2020: BSc in Mathematics, ETH Zurich
Papers
-
Agnostic learning in (almost) optimal time via Gaussian surface area.
Lucas Pesenti, Lucas Slot, Manuel Wiedmer. Preprint.
arXiv -
Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity.
Haitong Liu, Deepak Narayanan Sridharan, David Steurer, Manuel Wiedmer. 39th Annual Conference on Learning Theory (COLT 2026).
arXiv coming soon -
Hesse's Redemption: Efficient Convex Polynomial Programming.
Lucas Slot, David Steurer, Manuel Wiedmer. 58th ACM Symposium on Theory of Computing (STOC 2026).
arXiv -
Testably Learning Polynomial Threshold Functions.
Lucas Slot, Stefan Tiegel, Manuel Wiedmer. Advances in Neural Information Processing Systems 37 (NeurIPS 2024).
arXiv · Conference proceedings -
Nonconvergence of a sum-of-squares hierarchy for global polynomial optimization based on push-forward measures.
Lucas Slot, Manuel Wiedmer. Numerical Algebra, Control and Optimization (special issue).
arXiv · Journal -
Right-angled Artin groups as finite-index subgroups of their outer automorphism groups (Master Thesis).
Manuel Wiedmer. Bulletin of the London Mathematical Society.
arXiv · Journal · Master Thesis
2026
2024
2023
Teaching
- Algorithmen und Datenstrukturen, ETH Zurich (Fall 2024, Fall 2025), Head Teaching Assistant
- Algorithmen und Datenstrukturen, ETH Zurich (Fall 2023), Teaching Assistant
- Algorithms, Probability and Computing, ETH Zurich (Fall 2020, Fall 2021), Teaching Assistant