Dipl.-Ing. Dr.techn. David Steyrl
T: +43-1-4277-47123
Consultation hours: On request only
Summer term 2025
200142 SE Master's Thesis Seminar (A)
Winter term 2024
200185 SE Master's Thesis Seminar (A)
Geidel, B., Siegel, M., Steyrl, D., Goldberg, A. E., Bodenmann, G., & Zemp, M. (2025). Study Protocol for the Rainbow Austrian Longitudinal Family (RALF) study: A longitudinal, multi-method, multi-rater investigation of risk and resilience factors in Austrian LGBTQ+ parent families. BMC Psychology, 13. https://doi.org/10.31219/osf.io/p6zhj_v1, https://doi.org/10.1186/s40359-025-02828-4
Lor, C. S., Steyrl, D., Karner, A., Götzendorfer, S. J., Klimesch, A., Eder, S. J., Renz, F. M., Rother, J., Scharnowski, F., & Melinscak, F. (2025). SpiderPhy dataset: A multimodal dataset of Physiological, Psychometric and Behavioral Responses to fear stimuli. Scientific Data, 12(1), 599. https://doi.org/10.1038/s41597-025-04908-x
Zhang, M., Karner, A., Kostorz, K., Shea, S., Steyrl, D., Melinscak, F., Sladky, R., Lor, C. S., & Scharnowski, F. (2025). SpiDa-MRI behavioral and (f)MRI data of adults with fear of spiders. Scientific Data, 12(1), 284. Article 284. https://doi.org/10.1038/s41597-025-04569-w
Nicholson, A. A., Lieberman, J. M., Hosseini-Kamkar, N., Eckstrand, K., Rabellino, D., Kearney, B., Steyrl, D., Narikuzhy, S., Densmore, M., Théberge, J., Hosseiny, F., & Lanius, R. A. (2025). Exploring the impact of biological sex on intrinsic connectivity networks in PTSD: A data-driven approach. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 136, Article 111180. https://doi.org/10.1016/j.pnpbp.2024.111180
Todorova, B., Steyrl, D., Hornsey, M. J., Pearson, S., Brick, C., Lange, F., Van Bavel, J. J., Vlasceanu, M., Lamm, C., & Doell, K. C. (2025). Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors. npj climate action, 4(1), 46. https://doi.org/10.1038/s44168-025-00251-4
Siegel, M., Steyrl, D., Goldberg, A. E., Nicholson, A., & Zemp, M. (2025). Minority stress and structural stigma predict well-being in European LGBTQ+ parents. Journal of Marriage and Family. https://doi.org/10.1111/jomf.13071
Chiappini, E., Massaccesi, C., Korb, S., Steyrl, D., Willeit, M., & Silani, G. (2024). Neural Hyperresponsivity During the Anticipation of Tangible Social and Nonsocial Rewards in Autism Spectrum Disorder: A Concurrent Neuroimaging and Facial Electromyography Study. Biological psychiatry. Cognitive neuroscience and neuroimaging, 9(9), 948-957. https://doi.org/10.1016/j.bpsc.2024.04.006
Spee, B. T. M., Leder, H., Mikuni, J., Scharnowski, F., Pelowski, M., & Steyrl, D. (2024). Using Machine Learning to Predict Judgments on Western Visual Art Along Content-Representational and Formal-Perceptual Attributes. PLoS ONE, 19(9), Article e0304285. https://doi.org/10.1371/journal.pone.0304285
Mikuni, J., Spee, B. T. M., Forlani, G., Leder, H., Scharnowski, F., Nakamura, K., Watanabe, K., Kawabata, H., Pelowski, M., & Steyrl, D. (2024). Cross-Cultural Comparison of Beauty Judgments in Visual Art Using Machine Learning Analysis of Art Attribute Predictors Among Japanese and German Speakers. Scientific Reports, 14(1), Article 15948. https://doi.org/10.1038/S41598-024-65088-Z
Thanhaeuser, M., Gsoellpointner, M., Kornsteiner-Krenn, M., Steyrl, D., Brandstetter, S., Jilma, B., Berger, A., & Haiden, N. (2024). Introduction of Solid Foods in Preterm Infants and Its Impact on Growth in the First Year of Life-A Prospective Observational Study. Nutrients, 16(13), Article 2077. https://doi.org/10.3390/nu16132077
Karner, A., Obenaus, L., Zhang, M., Lor, C., Kostorz, K., Pegler, D., Leopold, M.-L., Melinšcak, F., Steyrl, D., & Scharnowski, F. (2024). Visual attributes of spiders associated with aversiveness in spider-fearful individuals: A machine learning analysis. PsyArXiv. https://doi.org/10.31234/osf.io/ht2pr
Zhang, M., Karner, A., Kostorz, K., Shea, S., Steyrl, D., Melinšcak, F., Sladky, R., Lor, C., & Scharnowski, F. (2024). SpiDa-MRI, behavioral and (f)MRI data of adults with fear of spiders. bioRxiv. https://doi.org/10.1101/2024.02.07.578564
Juergensen, V., Peter, L.-J., Steyrl, D., Lor, C. S., Bui, A. P., McLaren, T., Muehlan, H., Tomczyk, S., Schmidt, S., & Schomerus, G. (2024). The help-seeking process and predictors of mental health care use among individuals with depressive symptoms: a machine learning approach. Frontiers in Public Health, 12, Article 1504720. https://doi.org/10.3389/fpubh.2024.1504720
Karner, A., Zhang, M., Lor, C. S., Steyrl, D., Götzendorfer, S. J., Weidt, S., Melinscak, F., & Scharnowski, F. (2024). The "SpiDa" dataset: self-report questionnaires and ratings of spider images from spider-fearful individuals. Frontiers in Psychology, 15, Article 1327367. https://doi.org/10.3389/fpsyg.2024.1327367
Kostorz, K., Nguyen, T., Pan, Y., Melinšcak, F., Steyrl, D., Hu, Y., Sorger, B., Hoehl, S., & Scharnowski, F. (2023). Towards fNIRS Hyperfeedback: A Feasibility Study on Real-Time Interbrain Synchrony. bioRxiv. https://doi.org/10.1101/2023.12.11.570765
Park, A. H., Patel, H., Mirabelli, J., Eder, S. J., Steyrl, D., Lueger-Schuster, B., Scharnowski, F., O'Connor, C., Martin, P., Lanius, R. A., McKinnon, M. C., & Nicholson, A. (2023). Machine learning models predict PTSD severity and functional impairment: A personalized medicine approach for uncovering complex associations among heterogeneous symptom profiles. Psychological Trauma, 17(2), 372-386. https://doi.org/10.1037/tra0001602
Lieberman, J. M., Rabellino, D., Densmore, M., Frewen, P. A., Steyrl, D., Scharnowski, F., Théberge, J., Hosseini-Kamkar, N., Neufeld, R. W. J., Jetly, R., Frey, B. N., Ros, T., Lanius, R. A., & Nicholson, A. A. (2023). A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study. Frontiers in Neuroscience, 17, Article 1229729. https://doi.org/10.3389/fnins.2023.1229729
Spee, B. T. M., Mikuni, J., Leder, H., Scharnowski, F., Pelowski, M., & Steyrl, D. (2023). Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings. Scientific Reports, 13(1), Article 12966. https://doi.org/10.1038/s41598-023-39865-1
Siegel, M., Zemp, M., Goldberg, A. E., Nicholson, A., & Steyrl, D. (2023, Jul 14). Preregistration: Hidden hearts: Structural stigma and minority stress as predictors of avoiding public display of affection among individuals in same-gender relationships from 28 European countries. https://doi.org/10.17605/OSF.IO/AR2VH
Siegel, M., Steyrl, D., Goldberg, A. E., Nicholson, A., & Zemp, M. (2023, Jul 14). Preregistration: Individual-, couple-, and family-level minority stress in LGBT parents from 22 European countries: An intersectional, machine learning-based approach. https://doi.org/10.17605/OSF.IO/HQTGA
Interpretable machine learning for hypothesis
David Steyrl (Speaker), Alexander Karner (Speaker), Blanca Thea Maria Spee (Speaker) & Frank Scharnowski (Speaker)
16 Sept 2024 → 24 Sept 2024
Activity: Talks and presentations › Talk or oral contribution › Science to Science
Die Modellierung von Intersektionalität in LGBTQ-Paaren und -Familien mittels Machine Learning-basierten Ansätzen: Evidenz aus 28 europäischen Ländern
Magdalena Siegel (Speaker), David Steyrl (Contributor), Abbie E. Goldberg (Contributor), Andrew Nicholson (Contributor), Niki Hosseini-Kamkar (Contributor) & Martina Zemp (Contributor)
15 Jun 2024
Activity: Talks and presentations › Talk or oral contribution › Science to Science
Introduction to Machine Learning / Deep Learning and its application to social sciences
David Steyrl (Speaker)
30 Sept 2019
Activity: Talks and presentations › Talk or oral contribution › Science to Science
Department of Cognition, Emotion, and Methods in Psychology
Liebiggasse 5
1010 Wien
Room: O3.50
T: +43-1-4277-47123