Marius Hobbhahn


Research Interests

Uncertainty Quantification in Deep Learning
Bayesian Inference
Bayesian Deep Learning
AI Safety and Alignment
Fairness in Machine Learning


International Max-Planck Research School Tübingen, Germany

PhD with Philipp Hennig, 2020-now
Topic: Development of a new approximate inference scheme

University of Tübingen, Germany

M.Sc., Machine Learning/Computer Science, 2018-2020
Grade (currently 1.5)

University of Tübingen, Germany

B.Sc., Computer Science, 2016-2019
Grade: 1.7 (3.3 GPA equivalent)

University of Tübingen, Germany

B.Sc., Cognitive Science, 2015-2018
Grade: 1.8 (3.2 GPA equivalent)

Willstätter Gymnasium Nürnberg, Germany

Abitur, 2007 - 2015
Grade: 1.4 (3.6 GPA equivalent)


Fast Predictive Uncertainty for Classification with Bayesian Deep Networks

Hobbhahn, Marius; Kristiadi, Agustinus and Hennig, Philipp. arXiv preprint arXiv:2003.01227 (2020).
[arxiv] [code (soon)]

Sequence Classification using Ensembles of Recurrent Generative Expert Modules

Hobbhahn, Marius; Butz, Martin; Fabi, Sarah and Otte, Sebastian. ESANN (2020).
[(ESANN is postponed to 10/2020)] [code]

ML Experience

Object Detection using the Scattering Transform (Grade: 1.7)

[pdf] [code]

Inverse Classification using Generative Models (Grade: 1.3)

[pdf] [code]

Reconstruction of the Paper "Reading Scene Text in Deep Convolutional Sequences" (Grade: 1.3)

[arxiv] [code]

Bayesian Prediction of Winprobability in League of Legends (Grade: 1.0)

[html] [code]

Additional Information

Successfully completed over 20 lectures related to ML, AI, NNs, RL, Stats, etc. at the University of Tübingen

Programming Experience

Programming Languages: Python > R > Java, JavaScript, C++, SQL, Julia, Flutter
ML related: Pytorch, Tensorflow, Keras, Cuda


English (C2 = IELTS 8.0)
French (B2)
German (native)