Robert Müller, M.Sc.

Robert Müller, M.Sc.

Lehrstuhl für Mobile und Verteilte Systeme

Ludwig-Maximilians-Universität München, Institut für Informatik

Oettingenstraße 67
80538 München

Raum E 107

Telefon: +49 89 / 2180-9157

Fax: +49 89 / 2180-9148


Research Interests

  • Deep Learning
  • Self-Supervised Learning
  • Probabilistic Programming
  • Multi-Agent Systems / Reinforcement Learning
  • Sports Analytics


  • Robert Müller, Stefan Langer, Fabian Ritz, Christoph Roch, Steffen Illium and Claudia Linnhoff-Popien, „Soccer Team Vectors“, 6th Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA’19), 2019. [arXiv]
  • Stefan Langer, Robert Müller, Claudia Linnhoff-Popien and Kyrill Schmid, „Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks“,  6th Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA’19), 2019. [arXiv]
  • Daniel Elsner, Stefan Langer, Fabian Ritz, Robert Müller and Steffen Illium, „Deep Neural Baselines for Computational Paralinguistics“, INTERSPEECH , 2019. [arXiv]
  • Thomy Phan, Thomas Gabor, Robert Müller, Christoph Roch, and Claudia Linnhoff-Popien, „Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning“, 28th International Joint Conference on Artificial Intelligence (IJCAI ’19), 2019. [arXiv]
  • Christoph Roch, Thomy Phan, Sebastian Feld, Robert Müller, Thomas Gabor and Claudia Linnhoff-Popien, „A Quantum Annealing Algorithm for Finding Pure Nash Equilibria in Graphical Games“, Preprint, 2019. [arXiv]

Offene Arbeiten

Bachelorarbeit: B, Masterarbeit: M, Einzelpraktikum: P

Bei Interesse einfach eine Mail schreiben.

  • Self-Supervised Audio Feature Learning by Sorting Audio Snippets (B,M,P)
  • Matchmaking in Sports using Monte Carlo Tree Search (B,M,P)
  • Using the ELMo Architecture for Audio Files (B,M,P)

In Bearbeitung

  • Neural Soccer Transfer Prediction (B)
  • Online Fashion Recommendation using Automated Feature Extraction (B)
  • Multi Task Reinforcement Learning using Natural Language (B)
  • Inverse Reinforcement Learning using Natural Language (B,M,P)