Max Pfeffer

I am a junior professor at Universität Göttingen.

Research Interests

• Matrix and Tensor Factorizations

• Riemannian Optimization

• Cancer Classification

• Low-Rank Approximation

• Numerics in Quantum Chemistry

• Parametric PDEs

Recent Collaborations

• TU Chemnitz:
  Martin Stoll

• SimulaMet, Oslo:
  Evrim Acar Ataman

• MPI MiS Leipzig
  Bernd Sturmfels

• Universität Augsburg
  André Uschmajew

• Johannes-Gutenberg-Universität Mainz
  Markus Bachmayr

• Ospedale Policlinico San Martino, Genova
  Ulrich Pfeffer

• TU München
  Felix Krahmer

• Pontificia Universidad Católica de Chile, Santiago
  José Samper

A detailed CV is available upon request.

Positions

• Since April 2023:    Junior professor at Georg-August-Universität Göttingen

• May 2022 - April 2023:    Research consultant at SimulaMet, Oslo, Norway

• April 2022 - March 2023:    Adjunct professor at Technische Universität Chemnitz

• January 2021 - March 2022:    DFG Eigene Stelle at Technische Universität Chemnitz

• July 2020 - December 2020:    PostDoc with Markus Bachmayr at Johannes-Gutenberg-Universität Mainz

• April 2020 - September 2020:    Adjunct professor at Universität Leipzig

• October 2017 - June 2020:    PostDoc with André Uschmajew at Max Planck Institute for Mathematics in the Sciences, Leipzig

• February 2014 - October 2017:    PhD-student with Reinhold Schneider at TU Berlin

Education

• March 2018:    PhD in Mathematics at TU Berlin

• January 2014:    M.Sc. in Mathematics at TU Berlin

• October 2011:    B.Sc. in Mathematics at TU Berlin

• June 2007:    Abitur at Werner-von-Siemens-Gymnasium, Berlin

• July 2024: We uploaded our new preprint "tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data".

• February 2024: Our article "Machine Learning Methods for Gene Selection in Uveal Melanoma" has been published in the International Journal of Molecular Sciences.

• January 2024: Our article "The cone of 5×5 completely positive matrices" has been published in Discrete & Computational Geometry.

• December 2023: We uploaded our new preprint "Efficient training of Gaussian processes with tensor product structure".

• October 2023: Our article "A Time-Aware Tensor Decomposition for Tracking Evolving Patterns" has been published in the proceedings of IEEE MLSP 2023.

• August 2023: We uploaded our new preprint "A Time-aware tensor decomposition for tracking evolving patterns".

• April 2023: I have assumed a junior professorship at Universität Göttingen.

• February 2023: We uploaded our new preprint "A weighted subspace exponential kernel for support tensor machines".

• December 2022: Our article "Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma" has been published in Biomedicines.

• October 2022: Our article "Riemannian thresholding methods for row-sparse and low-rank matrix recovery" has been published in Numerical Algorithms.

• October 2022: For the winter semester, I have extended my adjunct professorship at Technische Universität Chemnitz.

• May 2022: Our article "Particle number conservation and block structures in matrix product states" has been published in Calcolo.

• May 2022: I have assumed a part-time position as research consultant at SimulaMet, Oslo.

• April 2022: For the summer semester, I have assumed an adjunct professorship at Technische Universität Chemnitz.

• August 2021: We uploaded our new preprint "The cone of 5×5 completely positive matrices".

• May 2021: We announce the COMinDS YoungResearchers' Seminar that will take place online on Friday, May 28, at 2 pm CET.

• April 2021: We uploaded our new preprint "Particle number conservation and block structures in Matrix Product States".

• March 2021: We uploaded our new preprint "Riemannian thresholding methods for row-sparse and low-rank matrix recovery".

• February 2021: Our article "Computing eigenspaces with low rank constraints" has been published in SISC.

• January 2021: I have started my DFG project on Constrained Matrix and Tensor Factorizations and Their Application in Data Fusion.