Photo credit: Kristian Karaneshev
SMiP Advisors: Prof. Dr. Andreas Voss, Prof. Dr. Thorsten Meiser, Prof. Jeffrey Rouder, Ph.D.
Thesis: Deep learning architectures for amortized Bayesian inference in cognitive modeling
Degree: Dr. phil. (summa cum laude), received from the University of Heidelberg in May 2021
First placement: Start-up funding from RTG SMiP, researcher at the University of Heidelberg
Current position: Postdoctoral researcher at STRUCTURES Cluster of Excellence, Heidelberg University
Research Areas
- Machine learning & deep learning
- Bayesian analysis
- Simulation science
- Cognitive modeling
Teaching (SMIP)
- Workshop „Python Basics“ (together with Ulf Mertens)
Publications
von Krause*, M., Radev*, S. T., & Voss, A. (2022). Mental speed is high until age 60 as revealed by analysis of over a million participants. Nature human behaviour, 6(5), 700–708. https://doi.org/10.1038/s41562-021-01282-7 [*shared first authorship]
Radev, S. T., Graw, F., Chen, S., Mutters, N. T., Eichel, V. M., Bärnighausen, T., & Köthe, U. (2021). 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, 17(10), e1009472. https://doi.org/10.1371/journal.pcbi.1009472
Bieringer, S., Butter, A., Heimel, T., Höche, S., Köthe, U., Plehn, T., & Radev, S. T. (2021). Measuring QCD splittings with invertible networks. SciPost Physics, 10(6), 126. https://doi.org/10.21468/SciPostPhys.10.6.126
Radev, S. T., D'Alessandro, M., Mertens, U. K., Voss, A., Köthe, U., & Bürkner, P. C. (2021). Amortized Bayesian model comparison with evidential deep learning. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3124052
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, 33(4), 1452-1466. https://doi.org/10.1109/TNNLS.2020.3042395
Talks
Radev, S. (2019, September). Pushing the boundaries: Tackling intractable response-time models with deep learning methods [Talk]. 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.