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
  2. Team
  3. Lukas Gosch

Lukas Gosch

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

Room: 00.11.065
Phone: +49 89 / 289-18581
E-Mail: lukas.gosch [at] tum [dot] de

Personal Webpage: https://saper0.github.io/
Twitter: @lukgosch
GitHub: @saper0
Google Scholar
LinkedIn

Research Focus

  • Machine Learning for Combinatorial Optimization
  • Graph Neural Networks
  • Adversarial Robustness and Verification of Deep Learning

Research Experience

  • 2022 - now: PhD Student at TU Munich
  • 2020 - 2021: Master's Thesis at the Austrian Institute of Technology, Integrated Transport Optimization Group
  • 2019 - 2020: Research Intern at IST Austria, Machine Learning and Computer Vision Group
  • 2018: Research Intern at Fraunhofer Institute for Algorithms & Scientific Computing, Numerical Data Driven Prediction Research Group
  • 2017: Research Intern at Queen's University Belfast

Education

  • 2018 - 2021: M.Sc. Computational Science at University of Vienna (GPA: 1.0; 1=best 5=worst)
  • 2013 - 2017: B.Sc. Physics at Vienna University of Technology

Academic Honors and Awards

  • 2024: Best Paper Award at the Adv-ML Frontiers Workshop @ NeurIPS 2024
  • 2022: Selected Oral Talk at TSRML @ NeurIPS 2022
  • 2021: Price for the best Master's Thesis by the Austrian Society for Operations Research
  • 2020: Performance Scholarship University of Vienna
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

  • Privacy
  • Imprint
  • Accessibility