seit Dezember 2020 Mitglied bei ELLIS.

Von Jänner 2021 bis Juni 2022 bin ich in Max Welling's Gruppe in Amsterdam.

CV_Brandstetter.pdf

• Deep Learning
• Deep Reinforcement Learning
• Few-Shot Learning
• Natural Language Processing
• Generative Modeling
• Physics-inspired and physics-informed machine learning

2020

[1] Michael Widrich, Bernhard Schäfl, Hubert Ramsauer, Milena Pavlović, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter and Günter Klambauer (2020). Modern Hopfield networks and attention for immune repertoire classification. Accepted at 2020 Conference on Advances in Neural Information Processing Systems. [PDF]

[2] Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Milena Pavlović, Geir Kjetil Sandve, Victor Greiff, David Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter (2020). Hopfield networks is all you need. [PDF]

[3] Vihang P Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M Blies, Johannes Brandstetter, Jose A Arjona-Medina, and Sepp Hochreiter (2020). Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution. [PDF]

[4] Thomas Adler, Johannes Brandstetter, Michael Widrich, Andreas Mayr, David Kreil, Michael Kopp, Günter Klambauer, and Sepp Hochreiter (2020). Cross-Domain Few-Shot Learing by Representation Fusion. [PDF]

[5] Markus Holzleitner, Lukas Gruber, Jose Arjona-Medina, Johannes Brandstetter, Sepp Hochreiter (2020). Convergence Proof of Actor-Critic Methods Applied to PPO and RUDDER. [PDF]

2019

[1] Jose Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, and Sepp Hochreiter (2019). RUDDER: Return Decomposition for Delayed Rewards. Advances in Neural Information Processing Systems (13566-13577). [PDF]

[2] Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, and Sepp Hochreiter (2019). Quantum Optical Experiments Modeled by Long Short-Term Memory. NeurIPS 2019 Workshop: Machine Learning and the Physical Sciences. [PDF]

[3] Frederik Kratzert, Daniel Klotz, Johannes Brandstetter, Pieter-Jan Hoedt, Grey Nearing, and Sepp Hochreiter (2019). Using LSTMs for climate change assessment studies on droughts and floods. NeurIPS 2019 Workshop: Tackling Climate Change with ML. [PDF]

[4] Hubert Ramsauer, Johannes Brandstetter, Michael Gillhofer, Bernhard Schäfl, Sepp Hochreiter (2019). A GAN based solver idea for derivative-free optimization problems. NeurIPS 2019 Workshop: Science meets Engineering of Deep Learning.

[5] Michael Gillhofer, Hubert Ramsauer, Johannes Brandstetter, Bernhard Schäfl, Sepp Hochreiter (2019). A GAN based solver of black-box inverse problems. NeurIPS 2019 Workshop: Solving inverse problems with deep networks.

•  Analysis of Integral Probability Metrics for Generative Adversarial Networks, Lukas Gruber (2019)
•  Image Segmentation of Microplastics via Hyperspectral Imaging, Kristina Duswald, ongoing

• 2018: Exercises in Machine Learning: Supervised Techniques, Lecturer, JKU Linz
• 2018: Lecture in Numerical and Symbolic Methods for Bioinformatics, Lecturer, JKU Linz
• 2019: Exercises in Machine Learning: Unsupervised Techniques, Lecturer, JKU Linz
• 2019: Exercises in Theoretical Concepts of Machine Learning, Lecturer, JKU Linz
• 2019: Lecture in Machine Learning: Supervised Techniques, Lecturer, JKU Linz
• 2019: Exercises in Machine Learning: Supervised Techniques, Lecturer, JKU Linz
• 2019: Lecture in Hands-on AI I, Lecturer, JKU Linz
• 2019: Lecture in Programming in Python I, Co-Lecturer, JKU Linz
• 2020: Lecture in Hands-on AI II, Lecturer, JKU Linz
• 2020: Lecture in Deep Learning and Neural Nets II, Co-Lecturer, JKU Linz
• 2020: Lecture in Hands-on AI I, Lecturer, JKU Linz
• 2020: Exercises in Hands-on AI I, Lecturer, JKU Linz

• ÖAW AI Summer School 2019, Deep Learning Lecture series. (Homepage)

• Blog post to the paper RUDDER: Return Decomposition for Delayed Rewards.
• Blog post to the paper Hopfield Networks is All You Need.
• Blog post to the paper Align-RUDDER: Learning from Few Demonstrations by Reward Redistribution.
• Blog post to the paper Cross-Domain Few-Shot Learning by Representation Fusion.
• Blog post Looking at the Performer from a Hopfield point of view.

• Born September 25, 1989
• BSc in Technical Physics (TU Wien), July 2012
• MSc in Technical Physics (TU Wien), November 2014
• PhD in High Energy Physics (CERN, Hephy Vienna), May 2018 (PhD Thesis)
• Post-Doc at the Institute for Machine Learning (JKU Linz), July 2018 - July 2020
• Assistant Professor (Tenure Track, JKU Linz), August 2020 - ongoing
• Guest Research at the University of Amsterdam (Max Welling group), January 2021 - ongoing