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    • Stephan Günnemann
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    • Eike Eberhard
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    • Simon Geisler
    • Lukas Gosch
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    • Anna-Kathrin Kopetzki
    • Arthur Kosmala
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    • Marten Lienen
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    • Yan Scholten
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    • Alumni
      • Amir Akbarnejad
      • Roberto Alonso
      • Bertrand Charpentier
      • Marin Bilos
      • Aleksandar Bojchevski
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      • Maria Kaiser
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      • 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
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  4. Roberto Alonso

Roberto Alonso

Technical University of Munich
Department of Informatics - I3
Boltzmannstr. 3
85748 Garching b. München
Germany

Room: 02.11.033
Phone: +49 (0)89 / 289-18568
Fax: +49 (0)89 / 289-17257
E-Mail: alonsor@in.tum.de

Research Focus

  • Machine Learning
  • Graph Mining
  • Network Security

Research Background

  • 2016: Post-Doctoral Researcher, Technische Universität München, Munich, Germany
  • 2008 - 2015: Research Assistant at Tecnologico de Monterrey, Mexico
  • 2010: Visiting Researcher at the Research Center for Mathematics, Guanajuato, Mexico
  • 2008: Visiting Researcher at Tecnologico de Monterrey, Monterrey, Mexico
  • 2010 - 2015: Ph.D. in Computer Science at Tecnologico de Monterrey. Thesis: “A Social Structure Model to Detect Anomalies in DNS Servers”. Graduated with Outstanding Thesis (Highest grade for a thesis).
  • 2008 - 2010: M.Sc. in Computer Science at Tecnologico de Monterrey. Thesis: “Towards a Social Model to Detect DDoS Attacks in DNS Server”.
  • 2004 - 2008: B.Sc. in Systems Engineering at Universidad del Valle de Mexico. Final proficiency project: “Controlling a DC motor using a cellphone”.

Academic Honors and Awards

  • 2015: CONACyT Postdoctoral Fellowship to conduct research at Technische Universität München, Munich, Germany. Funded from January 2016 to December 2016.
  • 2012: Second best paper award in the 2012 Mexican Conference on Artificial for the paper “Computational Complexity of a Special Case of Computing Groups in a Graph”, award granted by the Mexican Society for Artificial Intelligence.
  • 2010: CONACyT Scholarship to conduct studies towards a Ph.D. in Computer Science.
  • 2008: ITESM-CEM Scholarship to conduct studies towards a M.Sc. in Computer Science.

Scientific Community Services

ORGANIZATION

  • Organizer of the 1st Workshop in Computer Security and Pattern Recognition 2013, Mexico

REVIEWER FOR INTERNATIONAL CONFERENCES

  • 13th Mexican International Conference for Artificial Intelligence, MICAI, 2014
  • 22th International Conference on Electronics, Communications and Computers, CONIELECOMP 2012.
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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

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