Skip to content
  • de
  • en
  • Data Analytics and Machine Learning Group
  • TUM School of Computation, Information and Technology
  • Technical University of Munich
Technical University of Munich
  • Home
  • 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 Klicpera
      • Maria Kaiser
      • Richard Kurle
      • Hao Lin
      • John Rachwan
      • Oleksandr Shchur
      • Armin Moin
      • Daniel Zügner
  • Teaching
    • 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
    • Summer Term 2022
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar (Selected Topics)
      • Seminar (Time Series)
    • Winter Term 2021/22
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Summer Term 2021
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar
    • Winter Term 2020/21
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Summer Term 2020
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar
    • Winter Term 2019/2020
      • Machine Learning
      • Large-Scale Machine Learning
    • Summer Term 2019
      • Mining Massive Datasets
      • Large-Scale Machine Learning
      • Oberseminar
    • Winter Term 2018/2019
      • Machine Learning
      • Large-Scale Machine Learning
      • Oberseminar
    • Summer Term 2018
      • Mining Massive Datasets
      • Large-Scale Machine Learning
      • Oberseminar
    • Winter Term 2017/2018
      • Machine Learning
      • Oberseminar
    • Summer Term 2017
      • Robust Data Mining Techniques
      • Efficient Inference and Large-Scale Machine Learning
      • Oberseminar
    • Winter Term 2016/2017
      • 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
  • Research
    • Robust Machine Learning
    • Machine Learning for Graphs/Networks
    • Machine Learning for Temporal and Dynamical Data
    • Bayesian (Deep) Learning / Uncertainty
    • Efficient ML
    • Code
  • Publications
  • Open Positions
    • FAQ
  • Open Theses
  1. Home

Welcome

Our group’s research centers around the development of reliable and efficient machine learning methods (e.g. robustness and uncertainty), with major focus on learning principles for graphs (e.g. graph neural networks) and sequential data (e.g. LLMs, point process models).

Since in many real-world applications the collected data is rarely of high-quality but often noisy, prone to errors, or vulnerable to manipulations, robustness of algorithms is crucial to ensure reliable and trustworthy results. Therefore, our goal is to design techniques which handle different forms of errors and corruptions in an automatic way. In this regard, our group is especially interested in designing techniques for non-independent data: While one of the most common assumptions in many machine learning and data analysis tasks is that the given data points are realizations of independent and identically distributed random variables, this assumption is often violated. Sensors are interlinked with each other in networked cyber physical systems, people exchange information in social networks, and molecules or proteins interact based on biochemical events. In our research, we exploit these dependencies by developing learning methods for, e.g., graphs/networks and sequential data.

 

News

28.01.2025

Nine papers accepted at ICLR 2025, one at KAIS 2025

Our group will present 9 papers at ICLR 2025 (incl. 4 spotlights, 1 oral presentation). Congratulations to the entire team and all co-authors. More… [read more]

Open Positions & Theses:

  • If you are interested in a PhD or PostDoc position, please check out our openings and requirements here.
  • We also offer various Bachelor/Master thesis topics and Guided Research projects; see here.
  • If you want to join our group as a student assistant (research Hiwi), e.g. supporting us in the field of machine learning for graphs, please apply here.
To top

Informatics 26 - Data Analytics and Machine Learning


Prof. Dr. Stephan Günnemann

Technical University of Munich
TUM School of Computation, Information and Technology
Department of Computer Science 
Boltzmannstr. 3
85748 Garching
Germany

Secretary's office:
Room 00.11.057
Phone: +49 89 289-17256
Fax: +49 89 289-17257

News

28.01.2025

Nine papers accepted at ICLR 2025, one at KAIS 2025


02.10.2024

Eleven papers accepted at NeurIPS 2024


02.10.2024

Two papers at ICML 2024


27.02.2024

Our papers at ICLR 2024


05.10.2023

Four papers accepted at NeurIPS 2023

  • Privacy
  • Imprint
  • Accessibility