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Michael Widrich, MSc

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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

  • 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

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