About

I am a PhD student at the University of Tübingen and part of the International Max-Planck Research School for Intelligent Systems (IMPRS-IS). My supervisor is Prof. Dr. Jakob Macke. I am developing machine learning tools to perform Bayesian inference for simulation-based models. I am interested in the intersection of machine learning, statistics and science applications. Some of my main interests are: Bayesian inference Neural density estimation and generative models Robustness Probabilistic and differentiable programming Uncertainty quantification/calibration

Curriculum Vitae

Education

• PHD student at the International Max-Planck Research School for Intelligent Systems (IMPRS-IS)
  Apr 2022 - Now
  Supervised by Prof. Dr. Jakob Macke, University Tübingen.

• Master of Science in Bioinformatics
  Okt 2019 - Mar 2022
  University Tübingen (Grade: 1.15, 3.9 GPA equivalent)
  Thesis: “Variational methods for simulation-based inference”
  Transcript: Transcript of Records, Certificate
  Transcript (GPA):Grade summary

• Bachelor of Science in Bioinformatics
  Okt 2016 - Sep 2019
  University Tübingen (Grade: 1.31, 3.7 GPA equivalent)
  Thesis: “The landscapes of CD8+ T cell immunogenicity from a self-tolerance based perspective in sequence space”
  Transcript: Transcript of Records, Certificate
  Transcript (GPA):Grade summary

• A-Levels
  Joachim-Hahn-Gymnasium, Blaubeuren, Germany (Grade: 2.1, 3.0 GPA equivalent)

Work experience

• Research assistant
  Seq 2020 - Feb 2022
  University Tübingen, Computational Systems Biology, Junior Prof. Dr. Andreas Dräger
  Supervised by Dr. Reihaneh Mostolizadeh.

• Student assistant
  Okt 2018 - Feb 2019
  University Tübingen, Theory of Machine Learning Group, Prof. Dr. Ulrike von Luxburg.
  Teaching assistant for lecture “Algorithms”.

Selected Publications

For a full list of publications please refer to google scholar.

• All-in-one simulation-based inference, ICML 2024
  Manuel Glöckler, Michael Deistler, Christian Weilbach, Frank Wood, Jakob H Macke [arxiv]

• Variational methods for simulation-based inference, ICLR 2022
  Manuel Glöckler, Michael Deistler, Jakob H Macke [arxiv]

• Adversarial robustness of amortized Bayesian inference, ICML 2023
  Manuel Glöckler, Michael Deistler, Jakob H Macke [arxiv]

Other

• IOP trusted reviewer [certificate]
• Cambridge ELLIS Machine Learning Summer School and poster presentation 2022 [certificate]
• Machine Learning Summer School 2021 [certificate]

Here is a CV as PDF.

Some notes

If I read into interesting topics I typically as default write a small article about it together with a way simplified example (if it is a method). This is mostly what I will post here. I try to explain it from scratch and try to include (simple) proof (ideas) for any claims I raise, but I assume from the reader some basic knowledge about math and statistics.