Armin W. Thomas

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I am a Ram and Vijay Shriram Data Science Fellow at Stanford Data Science, where I work with Prof. Russ Poldrack on the application of deep learning to functional neuroimaging data.

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I am interested in:

  • Deep & machine learning
  • Decision neuroscience
  • Open science & reproducibility
  • Model interpretability & robustness

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Where else to find me:

🗞️ News

📚 Publications

2021
  • Thomas, A. W., Ré, C., & Poldrack, R. A. (2021). Challenges for cognitive decoding using deep learning methods. arXiv preprint arXiv:2108.06896.
  • Thomas, A. W., Molter, F., & Krajbich, I. (2021). Uncovering the computational mechanisms underlying many-alternative choice. Elife, 10, e57012. https://doi.org/10.7554/eLife.57012
  • Molter, F., Thomas, A. W., Huettel, S. A., Heekeren, H., & Mohr, P. N. C. (2021). Gaze-dependent evidence accumulation predicts multi-alternative risky choice behaviour. PsyArXiv. https://doi.org/10.31234/osf.io/x6nbf
2020
  • Thomas, A. W. (2020). Machine learning methods for modeling gaze allocation in simple choice behavior and functional neuroimaging data on the level of the individual. Technische Universität Berlin, Berlin. https://doi.org/10.14279/depositonce-10932
2019
  • Thomas, A. W., Heekeren, H. R., Müller, K. R., & Samek, W. (2019). Analyzing Neuroimaging Data Through Recurrent Deep Learning Models. Frontiers in Neuroscience, 13, 1321. doi.org/10.3389/fnins.2019.01321
  • Thomas, A. W., Molter, F., Krajbich, I., Heekeren, H. R., & Mohr, P. N. (2019). Gaze bias differences capture individual choice behaviour. Nature human behaviour, 3(6), 625. doi.org/10.1038/s41562-019-0584-8
  • Thomas, A. W., Molter, F., Heekeren, H. R., & Mohr, P. N. (2019). GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour. PloS one, 14(12). doi.org/10.1371/journal.pone.0226428
  • Thomas, A. W., Müller, K. R., & Samek, W. (2019). Deep transfer learning for whole-brain FMRI analyses. In OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging (pp. 59-67). Springer, Cham. doi.org/10.1007/978-3-030-32695-1_7

Key research projects

On the computational mechanisms of simple choice:

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Modeling gaze biases
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Individual gaze bias differences capture individual choice behaviour
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Computational mechanisms underlying many-alternative choice

On the analysis of fMRI data with deep learning models:

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Analyzing fMRI data with deep learning models

Workshops

Deep learning:

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The basics of deep learning
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Very quick introduction to deep learning

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my-CV.pdf100.7KB

📩 reach out: athms.research@gmail.com