Lars Ruthotto

Lars Ruthotto

Associate Professor of Mathematics and Computer Science

Emory University, Atlanta, GA, USA

Biography

I am an applied mathematician developing computational methods for machine learning and inverse problems. I am an Associate Professor in the Department of Mathematics and the Department of Computer Science at Emory University and a member of the Scientific Computing Group. Prior to joining Emory, I was a postdoc at the University of British Columbia and I held PhD positions at the University of Lübeck and the University of Münster.

I received an NSF CAREER award and am also supported grants from the US Israeli Binational Science Foundation, the US Department of Energy’s Advanced Scientific Computing Research program, and the Air Force Office of Scientific Research. Previously, I was supported by the Centers for Disease Control.

Interests

  • Scientific Computing
  • Optimization
  • Machine Learning
  • Inverse Problems
  • Image Registration

Education

  • PhD in Mathematics, 2012

    University of Münster, Germany

  • Diploma in Mathematics, 2010

    University of Münster, Germany

Research

My research and teaching focus on the intersection of applied mathematics and data science, particularly deep learning and inverse problems.

In deep learning, I seek to create new insights and efficient training for continuous models based on ordinary and partial differential equations. Also, I develop machine learning approaches for solving high-dimensional partial differential equations and optimal control problems.

In inverse problems, I have been focusing on applications in image registration and reconstruction and computational techniques including optimal experimental design, uncertainty quantification, numerical optimization, multiscale and multigrid methods, and regularization. Over the years, I had many fruitful collaborations with domain-experts from public health, geophysics, and medical imaging.

Teaching

I offer regular one-semester courses in applied mathematics as well as advanced courses, seminars, and workshops in my research area. As part of my NSF CAREER project, I have also developed a new graduate-level course on Numerical Methods for Deep Learning. I offered this class twice at Emory and gave short versions of the class at TU Berlin, University of Chemnitz, and Scuola Normale Superiore di Pisa.

See also the detailed list of my courses. I am currently offering:

MATH 789R - Reading Seminar on Mathematics of Machine Learning

In this seminar, we discuss one recent work at the interface of applied mathematics and machine learning with the goal of exposing new research questions.

Selected Activities

Team

  • Elizabeth Newman, Distinguished Visiting Assistant Professor, since June 2019
  • Bas Peters, Distinguished Visiting Assistant Professor, since Sept. 2020
  • Derek Onken, Computer Science and Informatics PhD, since Sept. 2018
  • Kelvin Kan, Computational Mathematics PhD, since Sept. 2018
  • Abigail Julian, Computer Science and Informatics PhD, since Jan. 2020
  • Malvern Madondo, Computer Science and Informatics PhD, since Jan. 2020
  • Xingjian Li, Computational Mathematics PhD, since Sept. 2020
  • Uriel Yang, Undergraduate Honors student, since May 2020

Recent & Upcoming Talks

Click here for more talks.

Research Highlights at the interface of Applied Mathematics and Deep Learning

invited talk in the CU Data Science Team Meeting
Oct 20, 2020 7:30 PM

Machine Learning meets Optimal Transport

invited talk in the Sayas Numerics Seminar
Oct 13, 2020 3:30 PM
Slides Video

Machine Learning meets Partial Differential Equations

invited plenary talk at the joint SIAM/CAIMS Annual Meeting (AN20)
Jul 6, 2020 5:00 PM
PDF Video

Machine Learning meets Optimal Transport

invited talk in the One World Seminar: Mathematical Methods for Arbitrary Data Sources (MADS)
May 18, 2020 8:00 AM
PDF Video

Selected Publications

Click here for more publications.
Lars Ruthotto, Stanley J Osher, Wuchen Li, Levon Nurbekyan, Samy Wu Fung (2020). A machine learning framework for solving high-dimensional mean field game and mean field control problems. Proceedings of the National Academy of Sciences.

Preprint Code DOI

Lars Ruthotto, Eldad Haber (2020). Deep neural networks motivated by partial differential equations. Journal of Mathematical Imaging and Vision.

Preprint PDF DOI

Andreas Mang, Lars Ruthotto (2017). A Lagrangian Gauss-Newton-Krylov Solver for Mass- and Intensity-Preserving Diffeomorphic Image Registration. SIAM Journal on Scientific Computing.

Preprint PDF Code DOI

Eldad Haber, Lars Ruthotto (2017). Stable architectures for deep neural networks. Inverse Problems.

Preprint PDF DOI

Martin Burger, Jan Modersitzki, Lars Ruthotto (2013). A Hyperelastic Regularization Energy for Image Registration. SIAM Journal on Scientific Computing.

PDF DOI

Contact

  • lruthotto@emory.edu
  • 400 Dowman Drive, Atlanta, GA 30322
  • Office W408, located in the 4th floor in the Math and Science Center
  • email me to schedule an appointment