Melanie Weber

Melanie Weber

Assistant Professor

Harvard University


I am an Assistant Professor of Applied Mathematics and of Computer Science at Harvard. My research focuses on utilizing geometric structure in data for the design of efficient Machine Learning and Optimization methods.

In 2021-2022, I was a Hooke Research Fellow at the Mathematical Institute in Oxford and a Nicolas Kurti Junior Research Fellow at Brasenose College. In Fall 2021, I was a Research Fellow at the Simons Institute in Berkeley, where I participated in the program Geometric Methods for Optimization and Sampling. Previously, I received my PhD from Princeton University (2021) under the supervision of Charles Fefferman, held visiting positions at MIT and the Max Planck Institute for Mathematics in the Sciences and interned in the research labs of Facebook, Google and Microsoft. I am also interested in applications of Artificial Intelligence in the legal space and am the Chief Scientist of the start up Claudius Legal Intelligence.

  • Data Geometry
  • Machine Learning in Non-Euclidean Spaces
  • Optimization on Manifolds
  • Learning with Little Data
  • PhD in Applied Mathematics, 2021

    Princeton University

  • BSc/MSc in Mathematics and Physics, 2016

    University of Leipzig

  • MSc in Applied Mathematics (during year abroad), 2015

    University of Washington

Recent Publications

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(2022). Controlling Unknown Linear Dynamics with Bounded Multiplicative Regret. Rev. Mat. Iberoamericana.

Preprint PDF Project

(2022). LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension. Under Review.


(2022). Mixed-Membership Community Detection via Line Graph Curvature. Symmetry and Geometry in Neural Representations.

PDF Project


Discrete Curvature and Machine Learning on Graphs
Discrete Ricci curvature for curvature-based analysis of complex networks.
Optimization on Manifolds
Exploiting geometric structure in (non)convex (constrained) optimization.
Learning to Control with Little Data
Provable, adaptive strategies for agnostic control.
Machine Learning in Non-Euclidean Spaces
Harnessing the geometric structure of data in Machine Learning.

Upcoming and Recent Talks

AATRN Vietoris-Rips Seminar
University of California Santa Barbara
SIAM Conference on Optimization
Rensselaer Polytechnic Institute MIDO Seminar


  • Office hours by appointment. If you are a Harvard undergraduate or graduate student interested in working with me, please send me an email. Due to high email volume, I am unable to reply to most emails from non-Harvard students regarding admission, supervised projects or internships.