Fabian Jogl

Fabian Jogl

PhD student in machine learning, interested in graph neural networks.

Fabian Jogl

I am a second-year PhD student at TU Wien advised by Thomas Gärtner. My research interests lie in the intersection of machine learning and classical algorithmics. Currently, I am focusing on the expressiveness of graph neural networks. My goal is to perform research that combines applied machine learning with a strong theoretical and mathematical basis.

News

  • Happy to be recognized as a top reviewer at NeurIPS 2023
  • Two extended abstracts accepted at the LoG 2023 conference: Extending Graph Neural Networks with Global Features and Maximally Expressive GNNs for Outerplanar Graphs.
  • Our paper Maximally Expressive GNNs for Outerplanar Graphs has been accepted as an oral at the GLFrontiers@NeurIPS workshop.
  • Our paper Expressivity-Preserving GNN Simulation has been accepted at NeurIPS 2023.
  • Best Poster Award at G-Research’s ICML poster party where Maximilian Thiessen presented our poster on Expectation-Complete Graph Representations with Homomorphisms
  • Our paper Expectation-Complete Graph Representations with Homomorphisms has been accepted at ICML 2023.

Education

  • PhD in Computer Science at CAIML and TU Wien advised by Thomas Gärtner (2022-2026)
  • MSc in Logic and Computation at TU Wien (2019-2022)
  • BSc in Physics as University of Vienna (2016-2019)

Awards and Scholarships

  • Best Poster Award at G-Research’s ICML poster party for Expectation-Complete Graph Representations with Homomorphisms
  • Best Poster Award at MLG@ECML determined via community vote (2022)
  • Merit Scholarship in Logic and Computation (2020)
  • Merit Scholarship in Physics, first place of all physics students (2019)
  • Merit Scholarship in Computer Science, first place of all computer science students (2018)
  • Merit Scholarship in Physics (2017)

Other Research Experience

  • Summer@EPFL Research Fellowship: DATA Lab at EPFL (Summer 2022)
    Research on high dimensional data cubes advised by Christoph Koch and Peter Lindner

  • Student Employee: CV Lab at TU Wien (2021 - 2022)
    Research on applying computer vision techniques to historical films