Armin W. Thomas

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 on state-of-the-art AI tools that can help us understand the mind and brain (leveraging methods from high-dimensional time-series analysis, self-supervised learning, and explainable AI).

🧠 I am excited about:

  • Foundation models, such as large language and vision models and their application to science outside of conventional deep learning domains
  • Advancing AI methods for high-dimensional time-series modeling
  • Advancing model interpretability beyond current heatmapping approaches
  • Open science

🗞️ News


📚 Publications

  • Thomas, A. W., Ré, C., & Poldrack, R. A. (2022). Interpreting mental state decoding with deep learning models. Trends in Cognitive Sciences, 26(11), 972-986.
  • Thomas, A. W., Ré, C., & Poldrack, R. A. (2022). Self-supervised learning of brain dynamics from broad neuroimaging data. In Advances in Neural Information Processing Systems, 35. (preprint: arXiv:2206.11417).
  • Ciric, R., Thomas, A. W., Esteban, O., & Poldrack, R. A. (2022). Differentiable programming for functional connectomics. In Machine Learning for Health. Proceedings of Machine Learning Research. (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.
  • Molter, F., Thomas, A. W., Huettel, S. A., Heekeren, H. R., & Mohr, P. N. (2022). Gaze-dependent evidence accumulation predicts multi-alternative risky choice behaviour. PLoS computational biology, 18(7), e1010283.
  • Bommasani, R., …, Thomas, A. W., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
  • Thomas, A. W., Molter, F., & Krajbich, I. (2021). Uncovering the computational mechanisms underlying many-alternative choice. Elife, 10, e57012.
  • Thomas, A. W., Lindenberger, U., Samek, W., & Müller, K. R. (2021). Evaluating deep transfer learning for whole-brain cognitive decoding. arXiv preprint arXiv:2111.01562.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.


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