FH-Prof. DI Dr. techn.
Stefan Schrunner
Funktion:
Kontakt:
Standort:
- Professor for Artificial Intelligence
Kontakt:
+43 (0)5 90500-2251
s.schrunner[at]fh-kaernten[dot]at
Standort:
Europastraße 4
9524 Villach
WS 2025
| Lecture | Type | SPPS | ECTS-Credits | Course number |
|---|---|---|---|---|
| Advanced Topics | ILV | 3,5 | 5,0 | M2.08760.11.151 |
| Artificial Neural Networks and Deep Learning (II) | ILV | 3,5 | 5,0 | M2.08760.11.141 |
| Information& Probability Theory | ILV | 3,5 | 5,0 | M2.08760.11.011 |
| Project (III) Practical Implementation | PA | 3,5 | 5,0 | M2.08760.11.101 |
| Lecture | Type | SPPS | ECTS-Credits | Course number |
|---|---|---|---|---|
| Information& Probability Theory | ILV | 3,5 | 5,0 | M2.08760.11.011 |
SS 2025
| Lecture | Type | SPPS | ECTS-Credits | Course number |
|---|---|---|---|---|
| Artificial Neural Networks and Deep Learning (I) | ILV | 3,5 | 5,0 | M2.08760.11.131 |
| Master Thesis | MT | 0,5 | 20,0 | M2.08760.11.211 |
| Project (II) Frameworks and Concept Study | ILV | 3,5 | 5,0 | M2.08760.11.091 |
| Titel | Autor | Jahr |
|---|---|---|
| Automated Detection and Counting of Dislocations in Scanning Electron Microscopy Images Using Classical and Machine Learning-Based Image Segmentation | Eva Sarah Schwarzl | 2025 |
| Machine learning approaches for anomaly detection in the energy market. | Priscilla Zannier | 2025 |
| Titel | Autor | Jahr |
|---|---|---|
| Automated Detection and Counting of Dislocations in Scanning Electron Microscopy Images Using Classical and Machine Learning-Based Image Segmentation | Eva Sarah Schwarzl | 2025 |
| Machine learning approaches for anomaly detection in the energy market. | Priscilla Zannier | 2025 |
| Titel | Autor | Jahr |
|---|
| Run-Time | September/2025 - December/2025 |
| Project management | |
| Project staff | |
| Forschungsschwerpunkt | Gesundheitswissenschaften |
| Studiengang | |
| Forschungsprogramm | Wirtschaftliche Forschung |
| Förderinstitution/Auftraggeber |
Ziel ist es, MOVEVO in der Entwicklung eines datenbasierten und KI-gestützten Systems zur Individualisierung von Gesundheitsförderung zu unterstützen. Im Zentrum steht dabei die Konzeption und prototypische Vorbereitung einer skalierbaren „Adaptive Health Engine“, welche Nutzer:innenprofile erkennt, personalisierte Inhalte dynamisch ausspielt und langfristig das Gesundheitsverhalten positiv beeinflusst.
- MOVEVO Technologies GmbH (Fördergeber/Auftraggeber)
| Run-Time | September/2025 - December/2025 |
| Project management | |
| Project staff | |
| Forschungsschwerpunkt | Gesundheitswissenschaften |
| Studiengang | |
| Forschungsprogramm | Wirtschaftliche Forschung |
| Förderinstitution/Auftraggeber |
Ziel ist es, MOVEVO in der Entwicklung eines datenbasierten und KI-gestützten Systems zur Individualisierung von Gesundheitsförderung zu unterstützen. Im Zentrum steht dabei die Konzeption und prototypische Vorbereitung einer skalierbaren „Adaptive Health Engine“, welche Nutzer:innenprofile erkennt, personalisierte Inhalte dynamisch ausspielt und langfristig das Gesundheitsverhalten positiv beeinflusst.
- MOVEVO Technologies GmbH (Fördergeber/Auftraggeber)
| Articles in Journals | ||
|---|---|---|
| Title | Author | Year |
| Learning From Limited Temporal Data: Dynamically Sparse Historical Functional Linear Models With Applications to Earth Science Environmetrics, 36(4) | Janssen, J., Meng, S., Haris, A., Schrunner, S., Cao, J., Welch, W., Kunz, N., Ameli, A. | 2025 |
| A Gaussian sliding windows regression model for hydrological inference Journal of the Royal Statistical Society, Series C: Applied Statistics | Schrunner, S., Pishrobat, P., Janssen, J., Jenul, A., Cao, J., Ameli, A., Welch, W. | 2025 |
| Novel Ensemble Feature Selection Techniques Applied to High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms for the Prediction of Survival Computer Methods and Programs in Biomedicine, 244 | Jenul, A., Stokmo, H., Schrunner, S., Hjortland, G., Revheim, M., Tomic, O. | 2024 |
| UBayFS: An R Package for User Guided Feature Selection Journal of Open Source Software, 8 | Jenul, A., Schrunner, S. | 2023 |
| Principal component-based image segmentation: a new approach to outline in vitro cell colonies Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11:18-30 | Arous, D., Schrunner, S., Hanson, I., Edin, N., Malinen, E. | 2022 |
| A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS) Machine Learning, 111:3897-3923 | Jenul, A., Schrunner, S., Pilz, J., Tomic, O. | 2022 |
| RENT: A Python Package for Repeated Elastic Net Feature Selection Journal of Open Source Software, 6 | Jenul, A., Schrunner, S., Huynh, B., Tomic, O. | 2021 |
| RENT - repeated elastic net technique for feature selection IEEE Access, 9 | Jenul, A., Schrunner, S., Liland, K., Indahl, U., Futsaether, C., Tomic, O. | 2021 |
| An explicit solution for image restoration using Markov Random Fields Journal of Signal Processing Systems, 92:257-267 | Pleschberger, M., Schrunner, S., Pilz, J. | 2019 |
| Feature extraction from analog wafermaps: a comparison of classical image processing and a deep generative model IEEE Transactions on Semiconductor Manufacturing, 32:190-198 | Santos, T., Schrunner, S., Geiger, B., Pfeiler, O., Zernig, A., Kästner, A., Kern, R. | 2019 |
| Conference contributions | ||
|---|---|---|
| Title | Author | Year |
| Component Based Pre-filtering of Noisy Data for Improved Tsetlin Machine Modelling in: IEEE (Hrsg.), International Symposium on the Tsetlin Machine (ISTM), 20-21 Jun 2022, Grimstad, Norway | Jenul, A., Bhattarai, B., Liland, K., Jiao, L., Schrunner, S., Futsaether, C., Granmo, O., Tomic, O. | 2022 |
| Ranking Feature-Block Importance in Artificial Multiblock Neural Networks in: Springer Lecture Notes in Computer Science (Hrsg.), International Conference on Artificial Neural Networks 2022, 06-09 Sep 2022, Bristol, UK, S. 163-175 | Jenul, A., Schrunner, S., Huynh, B., Helin, R., Futsaether, C., Liland, K., Tomic, O. | 2022 |
| A generative semi-supervised classifier for datasets with unknown classes in: Association for Computing Machinery (Hrsg.), SAC '20: ACM Symposium on Applied Computing 2020, 30 Mar-03 Apr 2020, Brno, Czech Republic, S. 1066-1074 | Schrunner, S., Geiger, B., Zernig, A., Kern, R. | 2020 |
| A health factor for process patterns - enhancing semiconductor manufacturing by pattern recognition in analog wafermaps in: IEEE (Hrsg.), IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), 06-09 Oct 2019, Bari, Italy | Schrunner, S., Jenul, A., Scheiber, M., Zernig, A., Kästner, A., Kern, R. | 2019 |
| A comparison of supervised approaches for process pattern recognition in analog semiconductor wafer test data in: IEEE (Hrsg.), IEEE International Conference on Machine Learning and Applications (ICMLA 2018), 17-20 Dec 2018, Orlando, FL, USA | Schrunner, S., Pfeiler, O., Zernig, A., Kästner, A., Kern, R. | 2018 |
| Markov random fields for pattern extraction in analog wafer test data in: IEEE (Hrsg.), International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), 28 Nov-01 Dec 2017, Montreal, Canada | Schrunner, S., Pfeiler, O., Zernig, A., Kästner, A., Kern, R. | 2017 |
| Articles in Journals | ||
|---|---|---|
| Title | Author | Year |
| Learning From Limited Temporal Data: Dynamically Sparse Historical Functional Linear Models With Applications to Earth Science Environmetrics, 36(4) | Janssen, J., Meng, S., Haris, A., Schrunner, S., Cao, J., Welch, W., Kunz, N., Ameli, A. | 2025 |
| A Gaussian sliding windows regression model for hydrological inference Journal of the Royal Statistical Society, Series C: Applied Statistics | Schrunner, S., Pishrobat, P., Janssen, J., Jenul, A., Cao, J., Ameli, A., Welch, W. | 2025 |
| Articles in Journals | ||
|---|---|---|
| Title | Author | Year |
| Novel Ensemble Feature Selection Techniques Applied to High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms for the Prediction of Survival Computer Methods and Programs in Biomedicine, 244 | Jenul, A., Stokmo, H., Schrunner, S., Hjortland, G., Revheim, M., Tomic, O. | 2024 |
| Articles in Journals | ||
|---|---|---|
| Title | Author | Year |
| UBayFS: An R Package for User Guided Feature Selection Journal of Open Source Software, 8 | Jenul, A., Schrunner, S. | 2023 |
| Articles in Journals | ||
|---|---|---|
| Title | Author | Year |
| Principal component-based image segmentation: a new approach to outline in vitro cell colonies Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11:18-30 | Arous, D., Schrunner, S., Hanson, I., Edin, N., Malinen, E. | 2022 |
| A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS) Machine Learning, 111:3897-3923 | Jenul, A., Schrunner, S., Pilz, J., Tomic, O. | 2022 |
| Conference contributions | ||
|---|---|---|
| Title | Author | Year |
| Component Based Pre-filtering of Noisy Data for Improved Tsetlin Machine Modelling in: IEEE (Hrsg.), International Symposium on the Tsetlin Machine (ISTM), 20-21 Jun 2022, Grimstad, Norway | Jenul, A., Bhattarai, B., Liland, K., Jiao, L., Schrunner, S., Futsaether, C., Granmo, O., Tomic, O. | 2022 |
| Ranking Feature-Block Importance in Artificial Multiblock Neural Networks in: Springer Lecture Notes in Computer Science (Hrsg.), International Conference on Artificial Neural Networks 2022, 06-09 Sep 2022, Bristol, UK, S. 163-175 | Jenul, A., Schrunner, S., Huynh, B., Helin, R., Futsaether, C., Liland, K., Tomic, O. | 2022 |
| Articles in Journals | ||
|---|---|---|
| Title | Author | Year |
| RENT: A Python Package for Repeated Elastic Net Feature Selection Journal of Open Source Software, 6 | Jenul, A., Schrunner, S., Huynh, B., Tomic, O. | 2021 |
| RENT - repeated elastic net technique for feature selection IEEE Access, 9 | Jenul, A., Schrunner, S., Liland, K., Indahl, U., Futsaether, C., Tomic, O. | 2021 |
| Articles in Journals | ||
|---|---|---|
| Title | Author | Year |
| An explicit solution for image restoration using Markov Random Fields Journal of Signal Processing Systems, 92:257-267 | Pleschberger, M., Schrunner, S., Pilz, J. | 2019 |
| Feature extraction from analog wafermaps: a comparison of classical image processing and a deep generative model IEEE Transactions on Semiconductor Manufacturing, 32:190-198 | Santos, T., Schrunner, S., Geiger, B., Pfeiler, O., Zernig, A., Kästner, A., Kern, R. | 2019 |
| Conference contributions | ||
|---|---|---|
| Title | Author | Year |
| A generative semi-supervised classifier for datasets with unknown classes in: Association for Computing Machinery (Hrsg.), SAC '20: ACM Symposium on Applied Computing 2020, 30 Mar-03 Apr 2020, Brno, Czech Republic, S. 1066-1074 | Schrunner, S., Geiger, B., Zernig, A., Kern, R. | 2020 |
| A health factor for process patterns - enhancing semiconductor manufacturing by pattern recognition in analog wafermaps in: IEEE (Hrsg.), IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), 06-09 Oct 2019, Bari, Italy | Schrunner, S., Jenul, A., Scheiber, M., Zernig, A., Kästner, A., Kern, R. | 2019 |
| A comparison of supervised approaches for process pattern recognition in analog semiconductor wafer test data in: IEEE (Hrsg.), IEEE International Conference on Machine Learning and Applications (ICMLA 2018), 17-20 Dec 2018, Orlando, FL, USA | Schrunner, S., Pfeiler, O., Zernig, A., Kästner, A., Kern, R. | 2018 |
| Markov random fields for pattern extraction in analog wafer test data in: IEEE (Hrsg.), International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), 28 Nov-01 Dec 2017, Montreal, Canada | Schrunner, S., Pfeiler, O., Zernig, A., Kästner, A., Kern, R. | 2017 |
Please use this link for external references on the profile of Stefan Schrunner: www.fh-kaernten.at/mitarbeiter-details?person=s.schrunner
