Michael Widrich, MSc

Forschungsthemen

  • Deep Learning and Neural Networks
  • Long Short-Term Memory Networks
  • Convolutional Neural Networks
  • Reinforcement Learning
  • Image and Video Segmentation
  • Autonomous Driving
  • Machine Learning in Bioinformatics

Publikationen

  • Michael Widrich, Bernhard Schäfl, Milena Pavlović, Hubert Ramsauer, 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 with spotlight talk at 2020 Conference on Advances in Neural Information Processing Systems[PDF]
  • Jose Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, and Sepp Hochreiter (2019). RUDDER: Return Decomposition for Delayed Rewards. Accepted at 2019 Conference on Advances in Neural Information Processing Systems[PDF]
  • Leila Arras, José Arjona-Medina, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus-Robert Müller, Sepp Hochreiter, Wojciech Samek: Explaining and Interpreting LSTMs. In: Samek W., Montavon G., Vedaldi A., Hansen L., Muller KR. (eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science, vol 11700, pp 211-238. Springer, Cham, 2019. [PDF]
  • Michael Treml*, José Arjona-Medina*, Thomas Unterthiner*, Rupesh Durgesh, Felix Friedmann, Peter Schuberth, Andreas Mayr, Martin Heusel, Markus Hofmarcher, Michael Widrich, Bernhard Nessler, Sepp Hochreiter: Speeding up Semantic Segmentation for Autonomous DrivingMachine Learning for Intelligent Transportation Systems, in conjunction with Neural Information Processing Systems (NIPS), 2016

Lehre

  • 2017: Exercises in Machine Learning: Unsupervised Techniques, Lecturer, JKU Linz
  • 2017: Sequence Analysis and Phylogenetics, Co-Lecturer, JKU Linz
  • 2018: Exercises in Theoretical Concepts of Machine Learning, Lecturer, JKU Linz
  • 2018: Exercises in Machine Learning: Unsupervised Techniques, Lecturer, JKU Linz
  • 2018: Exercises in Bioinformatics, Lecturer, JKU Linz
  • 2018: Lecture in Programming in Python, Lecturer, JKU Linz
  • 2019: Lecture in Programming in Python I, Lecturer, JKU Linz
  • 2019: Lecture in Hands-on AI I, Co-Lecturer, JKU Linz
  • 2020: Lecture in Programming in Python II, Lecturer, JKU Linz
  • 2020: Lecture in Hands-on AI II, Co-Lecturer, JKU Linz
  • 2020: Lecture in Programming in Python I, Lecturer, JKU Linz
  • 2020: Lecture in Hands-on AI I, Co-Lecturer, JKU Linz

Vorträge und Externe Lehre

  • ÖAW AI Summer School 2019, Deep Learning Lecture series. (Homepage)
  1. [Introduction to Machine Learning] Introduction to Supervised Machine Learning
  2. [Deep Learning I] Logistic Regression
  3. [Deep Learning II] Neural Networks
  4. [Deep Learning III] Convolutional Neural Networks
  5. [Deep Learning IV] (Variational) Autoencoders and Generative Adversarial Networks
  6. [Deep Learning V] Recurrent Neural Networks

Software

Ausbildung

  • Oct 2016 - present: PhD in Bioinformatics, Johannes Kepler University, Linz
  • 2013-2016: MSc, Bioinformatics, with distinction, Johannes Kepler University, Linz, Austria
  • 2009-2012: BSc, Mechatronics/Robotics, with distinction, UAS Technikum Wien, Vienna, Austria

Beruflicher Werdegang

  • Oct 2016 - present: Research Assistant, Institute of Bioinformatics, Johannes Kepler University, Linz, Austria
  • Jul 2013: Internship, R&D, Plasmo Industrietechnik GmbH, Vienna, Austria
  • Jul 2012: Internship, R&D, Plasmo Industrietechnik GmbH, Vienna, Austria
  • Jul 2011 - Jan 2012: Internship with bachelor thesis, R&D, Schunk, Lauffen am Neckar, Germany