Dipl.-Ing. Dr.techn. David Steyrl
T: +43-1-4277-47123
Sprechstunde: Nur auf Anfrage
Wintersemester 2024
200185 SE Masterarbeitsseminar
Sommersemester 2024
200029 UE Übungen zur Statistik I
200033 UE Übungen zur Statistik I
200068 UE Übungen zur Statistik I
200142 SE Masterarbeitsseminar
200195 UE Übungen zur Statistik I
Wintersemester 2023
200185 SE Masterarbeitsseminar
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), [e0304285]. https://doi.org/10.1371/journal.pone.0304285, 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), [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), [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
Siegel, M., Steyrl, D., Goldberg, A. E., Nicholson, A., & Zemp, M. (2024). Exposure to minority stress and structural stigma predict well-being in LGBTQ+ parents across 19 European countries: An intersectional, machine learning-based approach. https://doi.org/10.31234/osf.io/hqkxb
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
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, [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. 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, [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), [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
Thanhaeuser, M., Steyrl, D., Fuiko, R., Brandstaetter, S., Binder, C., Thajer, A., Huber-Dangl, M., Haiden, N., Berger, A., & Repa, A. (2023). A secondary outcome analysis of a randomized trial using a mixed lipid emulsion containing fish oil in infants with extremely low birth weight: Cognitive and behavioral outcome at preschool age. The Journal of pediatrics, 254, 68-74.e3. https://doi.org/10.1016/j.jpeds.2022.10.014
Lieberman, J. M., Rabellino, D., Densmore, M., Frewen, P. A., Steyrl, D., Scharnowski, F., Théberge, J., Neufeld, R. W. J., Schmahl, C., Jetly, R., Narikuzhy, S., Lanius, R. A., & Nicholson, A. A. (2023). Posterior cingulate cortex targeted real-time fMRI neurofeedback recalibrates functional connectivity with the amygdala, posterior insula, and default-mode network in PTSD. Brain and Behavior, 13(3), [e2883]. https://doi.org/10.1002/brb3.2883
Lor, C. S., Zhang, M., Karner, A., Steyrl, D., Sladky, R., Scharnowski, F., & Haugg, A. (2023). Pre- and post-task resting-state differs in clinical populations. NeuroImage: Clinical, 37, [103345]. https://doi.org/10.1016/j.nicl.2023.103345
Giordano, V., Luister, A., Reuter, C., Czedik-Eysenberg, I., Singer, D., Steyrl, D., Vettorazzi, E., & Deindl, P. (2022). Audio Feature Analysis for Acoustic Pain Detection in Term Newborns. Neonatology, 119(6), 760-768. https://doi.org/10.1159/000526209
Giordano, V., Bibl, K., Felnhofer, A., Kothgassner, O., Steinbauer, P., Eibensteiner, F., Gröpel, P., Scharnowski, F., Wagner, M., Berger, A., Olischar, M., & Steyrl, D. (2022). Relationship between psychological characteristics, personality traits, and training on performance in a neonatal resuscitation scenario: A machine learning based analysis. Frontiers in Pediatrics , 10, [1000544]. https://doi.org/10.3389/fped.2022.1000544
Lor, C. S., Zhang, M., Karner, A., Steyrl, D., Sladky, R., Scharnowski, F., & Haugg, A. (2022). Pre- and post-task resting-state differs in clinical populations. bioRxiv. https://doi.org/10.1101/2022.09.20.508750
Interpretable machine learning for hypothesis
David Steyrl (Vortragende*r), Alexander Karner (Vortragende*r), Blanca Thea Maria Spee (Vortragende*r) & Frank Scharnowski (Vortragende*r)
16 Sep. 2024 → 24 Sep. 2024
Aktivität: Vorträge › Vortrag › Science to Science
Die Modellierung von Intersektionalität in LGBTQ-Paaren und -Familien mittels Machine Learning-basierten Ansätzen: Evidenz aus 28 europäischen Ländern
Magdalena Siegel (Vortragende*r), David Steyrl (Autor*in), Abbie E. Goldberg (Autor*in), Andrew Nicholson (Autor*in), Niki Hosseini-Kamkar (Autor*in) & Martina Zemp (Autor*in)
15 Juni 2024
Aktivität: Vorträge › Vortrag › Science to Science
Introduction to Machine Learning / Deep Learning and its application to social sciences
David Steyrl (Vortragende*r)
30 Sep. 2019
Aktivität: Vorträge › Vortrag › Science to Science
Institut für Psychologie der Kognition, Emotion und Methoden
Liebiggasse 5
1010 Wien
Zimmer: O3.50
T: +43-1-4277-47123