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Stefan Wegenkittl

Titel/Grad: Prof. (FH) Univ.-Doz. Mag. Dr.

Funktionen: Wissenschaftlicher Leiter der Studiengänge Applied Image and Signal Processing und Business Informatics, Fachbereichsleiter Angewandte Mathematik und Data-Mining

  • Applied Mathematics and Statistics
  • Data Science and Analytics
  • Deep Learning
  • Artificial Intelligence
  • Medical Image Processing
Ausgewählte Publikationen:

Mona Bartling et al. “Modeling Patterns in Map Use Contexts and Mobile Map Design Usability”. In: ISPRS International Journal of Geo-Information 10.8 (2021). issn: 2220-9964. doi: 10.3390/ijgi10080527.

Clemens Havas et al. “Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics”.'
In: ISPRS International Journal of Geo-Information 10.8 (2021), p. 498. issn:2220-9964. doi: 10.3390/ijgi10080498.

Cornelia Ferner et al. “Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping”. In: Information 11.8 (2020). issn: 2078-2489. doi: 10.3390/info11080376. URL: www.mdpi.com/2078-2489/11/8/376.

Maximilian Ernst Tschuchnig, Cornelia Ferner, and Stefan Wegenkittl. “Sequential IoT Data Augmentation Using Generative Adversarial Networks”.
In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2020, pp. 4212– 4216. doi: 10.1109/ICASSP40776.2020.9053949.

Cornelia Ferner and Stefan Wegenkittl. “A Semi-discriminative Approach for Sub-sentence Level Topic Classification on a Small Dataset”.'
In: Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Springer, 2019.

Cornelia Ferner et al. “Pool detection from smart metering data with convolutional neural networks”. In: Energy Informatics 2019 2.1 (2019), pp. 1–12. doi: 10.1186/s42162-019-0097-8.

Martin Schnöll, Cornelia Ferner, and Stefan Wegenkittl. “The Effectiveness of the Max Entropy Classifier for Feature Selection”.
In: Data Science – Analytics and Applications. Ed. by Peter Haber, Thomas Lampoltshammer, and Manfred Mayr. Wiesbaden: Springer Fachmedien Wiesbaden, 2019, pp. 37–39. isbn: 978-3-658-27495-5.

Michael Buchecker et al. “An entropy approach for evaluating adaptive motor learning processes while walking with unstable footwear”. In: Human Movement Science 60 (2018), pp. 48–56. issn: 0167-9457. doi:
https://doi.org/10.1016/j.humov.2018.05.005. url: http://www.sciencedirect.com/science/article/pii/S0167945717309612.

T. Wielek et al. “Sleep in patients with disorders of consciousness characterized by means of machine learning”. In: PLoS ONE 13.1 (2018), e0190458.

C. Ferner et al. “Information Extraction Engine for Sentiment-Topic Matching in Product Intelligence Applications”. In: Data Science – Analytics and Applications. Springer, 2017, pp. 53–57.

C. Ferner et al. “Topic-Klassifizierung für automatisierte Produktbewertungen mittels Hidden Markov Modellen”. In: Tagungsband 10. Forschungsforum der Österreichischen Fachhochschulen 2016. 2016.

W. Pomwenger et al. “Robust Automatic Renal Stone Detection In Ultrasonic Live Streams For Improving Extracorporeal Shock Wave Therapy”. In: The Journal of Urology 193.4 (2016), e886.
url: http://dx.doi.org/10.1016/j.juro.2015.02.2588.

Peter Hofmann et al. “Towards a framework for agent-based image analysis of remote-sensing data”. In: International Journal of Image and Data Fusion 6.2 (2015), pp. 115–137. doi: 10.1080/19479832.

Josef Laimer et al. “MAESTRO-multi agent stability prediction upon point mutations”. In: BMC bioinformatics 16.1 (2015), p. 116.

R. J. Graf and S.Wegenkittl. “Integration von Support Vector Machines in die objektbasierte Bildklassifizierung am Beispiel der Entwicklung eines Plug-Ins für eCognition.” In: Angewandte Geoinformatik 2012.
Beiträge zum AGIT-Symposium Salzburg. Ed. by J. Strobl, T. Blaschke, and G. Griesebner. Herbert Wichmann Verlag, VDE VERLAG GMBH, Berlin/Offenbach., 2014, pp. 52–61.

S. Wegenkittl. “Entropy and Divergence Statistics for Markovian Processes and Related Structural and Statistical Properties of Pseudorandom Number Generators”. Habilitation Thesis. Universität Salzburg, Österreich, 2003.

P. L’Ecuyer, R. Simard, and S. Wegenkittl. “Sparse Serial Tests of Uniformity for Random Number Generators”. In: SIAM Journal on Scientific Computing 24.2 (2002), pp. 652–668. url:

S. Wegenkittl. “A generalized _-divergence for asymptotically multivariate normal models”. In: Journal of Multivariate Analysis 83 (2002), pp. 288–302. url: ftp://random.mat.sbg.ac.at/pub/publications/ste/genphi/genphi.ps.gz.

S. Wegenkittl. “Entropy Estimators and Serial Tests for Ergodic Chains”. In: IEEE Transactions on Information Theory 47.6 (2001), pp. 2480–2489.

S. Wegenkittl. “Gambling Tests for Pseudorandom Number Generators”. In: Mathematics and Computers in Simulation 55.1–3 (2001), pp. 281–288.

S. Wegenkittl. “Monkeys, Gambling, and Return Times: Assessing Pseudorandomness”. In: Proceedings of the 1999 Winter Simulation Conference. Ed. by P.A. Farrington et al. Piscataway, N.J.: IEEE Press,1999, pp. 625–631.


fon:  +43 (0)50-2211-1303
fax:  +43(0)50-2211-1349
email: stefan.wegenkittl@fh-salzburg.ac.at

Stefan Wegenkittl

Stefan Wegenkittl (Foto: FH Salzburg)