Dr. Stefan Radev

• Machine Learning & Deep Learning
• Bayesian Methods for Parameter Estimation and Model Comparison
• Mathematische Modelle
• Neurofeedback

• Workshop „Python Basics“ (together with Ulf Mertens)

D’Alessandro, M., Radev, S. T., Voss, A., & Lombardi, L. (2020). A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task. PeerJ, 8(e10316), 1–32 https://doi.org/10.7717/peerj.10316

Konicar, L., Radev, S. T., Prillinger, K., Klöbl, M., Diehm, R., Birbaumer, N., ... & Poustka, L. (2021). Volitional modification of brain activity in adolescents with Autism Spectrum Disorder: A Bayesian analysis of Slow Cortical Potential neurofeedback. NeuroImage: Clinical, 29, 1-10. doi.org/10.1016/j.nicl.2021.102557

Mertens, U. K., Voss, A., & Radev, S. (2018). ABrox—A user-friendly Python module for approximate Bayesian computation with a focus on model comparison. PLOS ONE, 13(3), e0193981. https://doi.org/10.1371/journal.pone.0193981

Radev, S. T., Graw, F., Chen, S., Mutters, N. T., Eichel, V. M., Bärnighausen, T., & Köthe, U. (in press). OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany. PLoS Computational Biology.

Radev, S. T., Mertens, U. K., Voss, A., Ardizzone, L., & Köthe, U. (2020). BayesFlow: learning complex stochastic models with invertible neural networks. IEEE Transactions on Neural Networks and Learning Systems. doi.org/10.1109/TNNLS.2020.3042395

Radev, S. T., Mertens, U. K., Voss, A., & Köthe, U. (2019). Towards end‐to‐end likelihood‐free inference with convolutional neural networks. Br J Math Stat Psychol. doi:10.1111/bmsp.12159

Radev S. T., Wieschen E. M., Voss A., & Bürkner P. C. (2020). Amortized Bayesian inference for models of cognition. In Stewart, T. C. (Ed.). Proceedings of the 18^th International Conference on Cognitive Modelling (pp. 201-207). University Park, PA: Applied Cognitive Science Lab, Penn State. Retrieved from: iccm conference.neocities.org/2020/ICCM2020Proceedings.pdf

von Krause, M., Radev, S., & Voss, A. (in press). Mental speed is high until age 60 as revealed by a model-based neural network analysis of big data. Nature: Human Behavior.

von Krause, M., Radev, S.T., Voss, A., Quintus, M., Egloff, B., & Wrzus, C. (2021). Stability and change in diffusion model parameters over two years. Journal of Intelligence, 9(2), 26. doi.org/10.3390/jintelligence9020026

Wieschen, E. M., Voss, A., & Radev, S. (2020). Jumping to Conclusion? A Lévy Flight Model of Decision Making. The Quantitative Methods for Psychology, 16(2), 120–132. https://doi.org/10.20982/tqmp.16.2.p120

Radev, S. (2019). Pushing the boundaries: Tackling intractable response-time models with deep learning methods. Talk given at the 14^th Conference of the Section Methods and Evaluation of the German Psychological Society (DGPs), Kiel, Germany.

Radev, S.T. (2019, June). Taming the Intractable: Deep Learning for Universal Parameter Estimation. Poster at the 34th IOPS/SMiP Summer Conference, 13–14 June 2019, Utrecht, Netherlands.

Radev, S.T. (2019). BayesFlow: Learning complex stochastic models with invertible neural networks. Poster presented at the 50^th Meeting of the European Mathematical Psychology Group (EMPG), Heidelberg, Germany.

Radev, S.T., Mertens, U., Voss, A. (2018). Abrox – a graphical user interface for approximate Bayesian computation. In: 60. Tagung experimentell arbeitender Psychologen. Marburg, Germany.

Radev, S. T., Lerche, V., Mertens, U., & Voss, A. (July 2017). Diffusion model analysis: a graphical user interface with fast-dm. Poster presented at the „50th Annual Meeting of the Society for Mathematical Psychology (MathPsych)“ 2017 in Warwick.

Radev, S. T., Lerche, V., Mertens, U., & Voss, A. (September 2017). Diffusion model analysis: a graphical user interface to fast-dm. Poster presented at the „13. Tagung der Fach­gruppe Methoden & Evaluation der Deutschen Gesellschaft für Psychologie (FGME)“ 2017 in Tübingen.