Professor Dr. Stefan Faußer
Professor für Data Analytics
Studiengangleiter Künstliche Intelligenz und Informationsmanagement (Data Science Management)
Senior Research Fellow am Institut für Digitale Innovation (IDI)
Lehrveranstaltungen
- Big Data and Artificial Intelligence
- Deep Learning
- Machine Learning
- Big Data
- Business Intelligence (Methods and Tools for Data Analysis and Reporting)
- Car Entertainment and Advanced Driver Assistance Systems
- Angewandte Statistik und Mathematik (Mathematik und Statistik)
Vorherige Lehrveranstaltungen/ previous lectures:
- Natural Language Processing (AIDA Master)
- Predictive Analytics and Data Mining (BIA Master)
- BI Platforms and Tools (BIA Master)
- Datenbanken (IMUK Bachelor)
Vita
- Seit September 2020 Professor für Data Analytics an der Hochschule für angewandte Wissenschaften Neu-Ulm (HNU)
- Promotion in theoretischer Informatik an der Universität Ulm, Institut für Neuroinformatik. Schwerpunkte: Reinforcement Learning Ensembles und Methoden zur Bildung nicht-sphärischer Cluster aus großen Datenmengen
- Studium der Informatik an der Universität Ulm (Master) und an der Hochschule Ravensburg-Weingarten (FH-Diplom)
- Von 2018 - 2020 Data Scientist und IT-Projektleiter für die digitalen Services der professionellen Kaffeemaschinen (IoT Devices) bei WMF Group GmbH in Geislingen
- Von 2016 - 2017 Data Scientist für die TeamViewer Software Produkte bei TeamViewer GmbH in Göppingen
- Von 2008 - 2016 Software Engineer im Embedded Bereich bei der Real-Time Systems GmbH in Ravensburg
Publikationen
- Finze, Nikola and Jechle, Deinera and Faußer, Stefan A. and Gewald, Heiko
(2024)
How are We Doing Today? Using Natural Speech Analysis
to Assess Older Adults’ Subjective Well-Being. (öffnet neues Fenster)
Business & Information Systems Engineering : BISE.
ISSN 1867-0202
Volltext abrufen - Vangeepuram, Madhurima and Ehm, Hans and Ratusny, Marco and Faußer, Stefan A. and Heilmayer, Stefan and Welling, Tobias L.
(2023)
Assessing delivery commitments in supply chains : A matrix-based framework. (öffnet neues Fenster)
In: (Proceedings of the) Winter Simulation Conference (WSC) "Simulation for Resilient Systems", December, 10-13, 2023, San Antonio, TX, USA, pp. 2182-2193.
ISBN 9798350369663
Volltext abrufen - Meyer, Dany and Faußer, Stefan A. (2023) A framework for the design, implementation and evaluation of ai based real-life learning scenarios for computer science non-majors. (öffnet neues Fenster) In: (Proceedings of the) International Conference on Education, Research and Innovation (ICERI), November, 13-15, 2023, Seville, Spain. ISBN 9788409559428
- Gebele, Jens and Brune, Philipp and Faußer, Stefan A. (2022) Face Value: On the Impact of Annotation (In-)Consistencies and Label Ambiguity in Facial Data on Emotion Recognition. (öffnet neues Fenster) In: International Conference on Pattern Recognition (ICPR); 26th Montreal, QC, Canada: IEEE, pp. 2597-2604. ISBN 9781665490627
- Thaler, Fabian and Faußer, Stefan A. and Gewald, Heiko
(2021)
Using NLP to analyze whether customer statements comply with their inner belief. (öffnet neues Fenster)
arXiv:2107.11175.
Volltext abrufen - Faußer, Stefan A. and Schwenker, Friedhelm (2015) Selective neural network ensembles in reinforcement learning: Taking the advantage of many agents. (öffnet neues Fenster) Neurocomputing, 169. pp. 350-357. ISSN 0925-2312
- Faußer, Stefan A.
(2015)
Large state spaces and large data: Utilizing neural network ensembles in reinforcement learning and kernel methods for clustering. (öffnet neues Fenster)
Dissertation thesis, Universität Ulm.
Volltext abrufen - Faußer, Stefan A. and Schwenker, Friedhelm
(2015)
Neural Network Ensembles in Reinforcement Learning. (öffnet neues Fenster)
Neural Processing Letters, 41.
pp. 55-69.
ISSN 1573-773X
Volltext abrufen - Faußer, Stefan A. and Schwenker, Friedhelm
(2014)
Selective Neural Network Ensembles in Reinforcement Learning. (öffnet neues Fenster)
In: (Proceedings of the) 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 23.-25. April 2014, Bruges, Belgium, pp. 105-110.
ISBN 9782874190957
Volltext abrufen - Faußer, Stefan A. and Schwenker, Friedhelm (2014) Semi-supervised Clustering of Large Data Sets with Kernel Methods. (öffnet neues Fenster) Pattern Recognition Letters, 37. pp. 78-84. ISSN 0167-8655
- Faußer, Stefan A. and Schwenker, Friedhelm (2012) Clustering large datasets with kernel methods. (öffnet neues Fenster) In: (Proceedings of the) 21st International Conference on Pattern Recognition. (ICPR ’12) ; Vol. 1, November, 11-15th, 2012, Tsukuba, Japan, pp. 501-504. ISBN 9781467322164
- Faußer, Stefan A. and Schwenker, Friedhelm (2012) Semi-Supervised Kernel Clustering with Sample-to-cluster Weights. (öffnet neues Fenster) In: (Proceedings of the) 1st IAPR TC3 Workshop, PSL 2011, 15.-16. September 2011, Ulm, Germany, pp. 72-81. ISBN 9783642282577
- Faußer, Stefan A. and Schwenker, Friedhelm (2011) Ensemble Methods for Reinforcement Learning with Function Approximation. (öffnet neues Fenster) In: (Proceedings of the) 10th International Workshop on Multiple Classifier Systems (MCS), June, 15-17, 2011, Naples, Italy, pp. 56-65. ISBN 9783642215568
- Faußer, Stefan A. and Schwenker, Friedhelm (2010) Learning a Strategy with Neural Approximated Temporal-Difference Methods in English Draughts. (öffnet neues Fenster) In: (Proceedings of the) 20th International Conference on Pattern Recognition (ICPR), August, 23-26, 2010, Istanbul, Turkey, pp. 2925-2928. ISBN 9781424475421
- Faußer, Stefan A. and Schwenker, Friedhelm (2010) Parallelized Kernel Patch Clustering. (öffnet neues Fenster) In: (Proceedings of the) 4th IAPR TC3 Conference on Artificial Neural Networks in Pattern Recognition (ANNPR), April, 11-13, 2010, Cairo, Egypt, pp. 131-140. ISBN 9783642121586
- Faußer, Stefan A. and Schwenker, Friedhelm (2008) Neural Approximation of Monte CarloPolicy Evaluation Deployed in Connect Four. (öffnet neues Fenster) In: (Proceedings of the) 3rd IAPR Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), July, 2-4, 2008, Paris, France, pp. 90-100. ISBN 9783540699385
Forschung
Forschungsprojekte
- Voice Analysis for Customer Emotions (VACE)
