Publications

Roman is a multi-disciplinary researcher and combines fundamental research (conducted on the University of Technology in Graz) and applied research (conducted on the Know-Center). Here just a subset of all publications are listed to provide an initial overview.

A list of all publications is available via: Google Scholar

Natural Language Processing

• Rexha, A., Dragoni, M., & Kern, R. (2020). A Neural-based Architecture For Small Datasets Classification. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (pp. 319–327). New York, NY, USA: ACM. https://doi.org/10.1145/3383583.3398535

Causal Data Science

Causal NLP

• Razouk, H., & Kern, R. (2022). Improving the Consistency of the Failure Mode Effect Analysis (FMEA) Documents in Semiconductor Manufacturing. Applied Sciences, 12(4), 1840.

Causal Discovery

• Koutroulis, G., Botler, L., Mutlu, B., Diwold, K., Römer, K., & Kern, R. (2021). KOMPOS: Connecting Causal Knots in Large Nonlinear Time Series with Non-Parametric Regression Splines. ACM Transactions on Intelligent Systems and Technology (TIST), 12(5), 1-27.

Knowledge Discovery

Time Series

• Toller, M., Santos, T., & Kern, R. (2019). SAZED: parameter-free domain-agnostic season length estimation in time series data. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-019-00645-z

Clustering

• Toller, M. B., Geiger, B. C., & Kern, R. (2021). Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory. IEEE Transactions on Knowledge and Data Engineering.

Machine Learning

Deep Learning

• Egger, J., Pepe, A., Gsaxner, C., Jin, Y., Li, J., & Kern, R. (2021). Deep learning—a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact. PeerJ Computer Science, 7, e773.

Semi-Supervised

• Schrunner, S., Geiger, B. C., Zernig, A., & Kern, R. (2020). A generative semi-supervised classifier for datasets with unknown classes. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 1066–1074). New York, NY, USA: ACM. https://doi.org/10.1145/3341105.3373890

Graph Neural Networks

• Hussain, H., Duricic, T., Lex, E., Helic, D., & Kern, R. (2021). The interplay between communities and homophily in semi-supervised classification using graph neural networks. Applied Network Science, 6(1), 1-26.
• Hussain, H., Duricic, T., Lex, E., Kern, R., & Helic, D. (2021). On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks. In R. M. Benito, C. Cherifi, H. Cherifi, E. Moro, L. M. Rocha, & M. Sales-Pardo (Eds.), Complex Networks {&} Their Applications IX (pp. 15–26). Cham: Springer International Publishing.

Privacy Preservation

• Sousa, S., Kern, R., & Guetl, C. (2021). Privacy in Open Search: A Review of Challenges and Solutions. Third International Open Search Symposium

Applied Research

Covid-19

• Lovrić, M., Pavlović, K., Vuković, M., Grange, S. K., Haberl, M., & Kern, R. (2020). Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning. Environmental Pollution, 115900. https://doi.org/10.1016/j.envpol.2020.115900

Metal Forging

• Hoffer, J. G., Geiger, B. C., & Kern, R. (2022). Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys. Applied Sciences, 12(3), 1089.
• Hoffer, J. G., Geiger, B. C., Ofner, P., & Kern, R. (2021). Mesh-Free Surrogate Models for Structural Mechanic FEM Simulation: A Comparative Study of Approaches. Applied Sciences, 11(20), 9411.

Casting of Steel

• Cemernek, D., Cemernek, S., Gursch, H., Pandeshwar, A., Leitner, T., Berger, M., … Kern, R. (2021). Machine learning in continuous casting of steel: a state-of-the-art survey. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01754-7

Electro Galvanizing

• Lovrić, M., Meister, R., Steck, T., Fadljević, L., Gerdenitsch, J., Schuster, S., … Kern, R. (2020). Parasitic resistance as a predictor of faulty anodes in electro galvanizing: a comparison of machine learning, physical and hybrid models. Advanced Modeling and Simulation in Engineering Sciences, 7(1), 46. https://doi.org/10.1186/s40323-020-00184-z

Semiconductor Manufacturing

• Santos, T., Schrunner, S., Geiger, B. C., Pfeiler, O., Zernig, A., Kaestner, A., & Kern, R. (2019). Feature Extraction from Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Model. IEEE Transactions on Semiconductor Manufacturing, 32(2), 190–198. https://doi.org/10.1109/TSM.2019.2911061