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  • Data Analytics and Machine Learning Group
  • TUM School of Computation, Information and Technology
  • Technische Universität München
Technische Universität München
  • Startseite
  • Team
    • Stephan Günnemann
    • Sirine Ayadi
    • Tim Beyer
    • Jonas Dornbusch
    • Eike Eberhard
    • Dominik Fuchsgruber
    • Nicholas Gao
    • Simon Geisler
    • Lukas Gosch
    • Filippo Guerranti
    • Leon Hetzel
    • Niklas Kemper
    • Amine Ketata
    • Marcel Kollovieh
    • Anna-Kathrin Kopetzki
    • Arthur Kosmala
    • Aleksei Kuvshinov
    • Richard Leibrandt
    • Marten Lienen
    • David Lüdke
    • Aman Saxena
    • Sebastian Schmidt
    • Yan Scholten
    • Jan Schuchardt
    • Leo Schwinn
    • Johanna Sommer
    • Tom Wollschläger
    • Alumni
      • Amir Akbarnejad
      • Roberto Alonso
      • Bertrand Charpentier
      • Marin Bilos
      • Aleksandar Bojchevski
      • Johannes Gasteiger, né Klicpera
      • Maria Kaiser
      • Richard Kurle
      • Hao Lin
      • John Rachwan
      • Oleksandr Shchur
      • Armin Moin
      • Daniel Zügner
  • Lehre
    • Sommersemester 2025
      • Advanced Machine Learning: Deep Generative Models
      • Applied Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
      • Seminar: Current Topics in Machine Learning
    • Wintersemester 2024/25
      • Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
      • Seminar: Current Topics in Machine Learning
    • Sommersemester 2024
      • Machine Learning for Graphs and Sequential Data
      • Advanced Machine Learning: Deep Generative Models
      • Applied Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
    • Wintersemester 2023/24
      • Machine Learning
      • Applied Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
      • Seminar: Machine Learning for Sequential Decision Making
    • Sommersemester 2023
      • Machine Learning for Graphs and Sequential Data
      • Advanced Machine Learning: Deep Generative Models
      • Large-Scale Machine Learning
      • Seminar
    • Wintersemester 2022/23
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Sommersemester 2022
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar (Selected Topics)
      • Seminar (Time Series)
    • Wintersemester 2021/22
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Sommersemester 2021
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar
    • Wintersemester 2020/21
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Sommersemester 2020
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar
    • Wintersemester 2019/20
      • Machine Learning
      • Large-Scale Machine Learning
    • Sommersemester 2019
      • Mining Massive Datasets
      • Large-Scale Machine Learning
      • Oberseminar
    • Wintersemester 2018/19
      • Machine Learning
      • Large-Scale Machine Learning
      • Oberseminar
    • Sommersemester 2018
      • Mining Massive Datasets
      • Large-Scale Machine Learning
      • Oberseminar
    • Wintersemester 2017/18
      • Machine Learning
      • Oberseminar
    • Sommersemester 2017
      • Robust Data Mining Techniques
      • Efficient Inference and Large-Scale Machine Learning
      • Oberseminar
    • Wintersemester 2016/17
      • Mining Massive Datasets
    • Sommersemester 2016
      • Large-Scale Graph Analytics and Machine Learning
    • Wintersemester 2015/16
      • Mining Massive Datasets
    • Sommersemester 2015
      • Data Science in the Era of Big Data
    • Machine Learning Lab
  • Forschung
    • Robust Machine Learning
    • Machine Learning for Graphs/Networks
    • Machine Learning for Temporal and Dynamical Data
    • Bayesian (Deep) Learning / Uncertainty
    • Efficient ML
    • Code
  • Publikationen
  • Offene Stellen
    • FAQ
  • Abschlussarbeiten
  1. Startseite
  2. Team
  3. Johanna Sommer

Johanna Sommer

Technical University of Munich
Department of Informatics - I26
Boltzmannstr. 3
85748 Garching b. München
Germany

Room: 00.11.055
Phone: +49 89 289-17832
E-Mail: sommer [at] in [dot] tum [dot] de

Selected Publications

  • MAGNet: Motif-Agnostic Generation of Molecules from Shapes
    Leon Hetzel and Johanna Sommer (equal contribution), Bastian Rieck, Fabian Theis, Stephan Günnemann
    Preprint, 2023
  • The Power of Motifs as Inductive Bias for Learning Molecular Distributions
    Johanna Sommer and Leon Hetzel (equal contribution), David Lüdke, Fabian Theis, Stephan Günnemann
    Machine Learning for Drug Discovery @ ICLR, 2023
  • Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
    Simon Geisler and Johanna Sommer (equal contribution),, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
    International Conference on Learning Representations (ICLR), 2022
  • Neural Flows: Efficient Alternative to Neural ODEs
    Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram, Tim Januschowski, Stephan Günnemann
    Neural Information Processing Systems (NeurIPS), 2021

Education

2019 - 2021: M.Sc. Computer Science | Technical University of Munich, passed with distinction
2016 - 2019: B.Sc. Applied Computer Science | Baden-Württemberg Cooperative State University

Experience

2019: IBM Research Intern | Zurich, Switzerland
2018: IBM Research Intern | San Jose, CA, USA 

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Informatik 26 - Data Analytics and Machine Learning


Prof. Dr. Stephan Günnemann

Technische Universität München
TUM School of Computation, Information and Technology
Department of Computer Science
Boltzmannstr. 3
85748 Garching 

Sekretariat:
Raum 00.11.057
Tel.: +49 89 289-17256
Fax: +49 89 289-17257

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