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 and Prof. Christopher Ré to build deep learning tools that can help us understand the brain (leveraging methods from self-supervised learning and explainable AI).

🧠 I am excited about:

  • The brain
  • Foundation models, especially large language and vision models
  • Open science

🗞️ News

  • [June, 2022] I am really excited about our new preprint in which we devise self-supervised learning frameworks for broad functional neuroimaging data by taking inspiration from recent advances in NLP! All code, models, and our dataset can be found on GitHub
  • [June, 2022] We just uploaded a new preprint comparing different interpretation methods in mental state decoding analyses with deep learning models; Find the GitHub repo here
  • [June, 2022] Super happy to co-author this recent preprint exploring differential programming as a paradigm to learn analysis pipelines for functional connectomics; Find all code here
  • [March, 2022] Had a great time discussing recent advances in data science and machine learning at this year’s future leader’s summit by the Michigan Institute for Data Science
2021

📚 Publications

  • Thomas, A. W., Ré, C., & Poldrack, R. A. (2022). Self-Supervised Learning Of Brain Dynamics From Broad Neuroimaging Data. arXiv preprint arXiv:2206.11417.
  • Ciric, R., Thomas, A. W., Esteban, O., & Poldrack, R. A. (2022). Differentiable programming for functional connectomics. arXiv preprint arXiv:2206.00649.
  • Thomas, A. W., Ré, C., & Poldrack, R. A. (2022). Comparing interpretation methods in mental state decoding analyses with deep learning models. arXiv preprint arXiv:2205.15581.
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

🎒Teaching

📩 reach out: athms.research@gmail.com