Dr. Stefan Radev
Photo credit: Kristian Karaneshev
Stefan Radev successfully defended his dissertation in May 2021. He works as researcher at the University of Heidelberg.
Primary Advisor: Prof. Dr. Andreas Voss (University of Heidelberg)
Additional Supervisors: Prof. Dr. Thorsten Meiser, Prof. Jeffrey Rouder, Ph.D.
Dissertation Proposal: Pushing the boundaries: Tackling intractable response-time models with deep learning methods
Thesis: Deep learning architectures for amortized Bayesian inference in cognitive modeling
Research Areas
- Machine Learning & Deep Learning
- Bayesian Methods for Parameter Estimation and Model Comparison
- Mathematische Modelle
- Neurofeedback
Teaching (SMIP)
- Workshop „Python Basics“ (together with Ulf Mertens)
Publications
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., Mertens, U. K., Voss, A., Ardizzone, L., & Kothe, U. (in press). BayesFlow: learning complex stochastic models with invertible neural networks. IEEE Transactions on Neural Networks and Learning Systems. Retrieved from: 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 18th 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
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
Talks
Radev, S. (2019). Pushing the boundaries: Tackling intractable response-time models with deep learning methods. Talk given at the 14th Conference of the Section Methods and Evaluation of the German Psychological Society (DGPs), Kiel, Germany.
Posters
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 50th 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.