I am a Ram and Vijay Shriram Data Science Fellow at Stanford Data Science, where I work with Prof. Russ Poldrack and Prof. Christopher RĂ© to build deep learning tools that can help us understand the brain (leveraging methods from self-supervised learning and explainable AI).
I am also affiliated with Stanford's Center for Open and Reproducible Science, Stanford's Center for Research on Foundation Models, and the Max Planck Institute for Human Development.
🧠I am excited about:
- The brain
- Foundation models, especially large language and vision models
- Open science
🔎 Find me online:
🗞️ 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]Â 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
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- [October, 2021] I had the opportunity to give a practical tutorial on reproducible modelling for the 2021 fall lecture series of Stanford's Center for Open and REproducible Science; A recording of the talk can be found here and the accompanying GitHub repository here
- [September, 2021] The Massachusetts Society for Medical Research wrote a brief summary of our work on many-alternative choices.
- [August, 2021] We uploaded a new preprint in which we discuss challenges (and solutions) for deep learning methods in brain decoding; together with with Russ Poldrack and Chris RĂ©
- [August, 2021] Happy to have contributed to the report on foundation models (i.e., broadly pretrained models with wide adaptation; e.g., GPT-3, BERT, CLIP) by Stanford HAI with a section on the interpretability of foundation models
- [June, 2021] I will be working along side an amazing team as head technical mentor for Stanford's Data Science For Social Good (DSSG) summer program this year
- [May, 2021] Honored to be awarded a Google Cloud Computing Grant by Stanford HAI
- [April, 2021] Our paper on the computational mechanisms underlying many-alternative choices appeared in eLife!
- [January, 2021] Thrilled to begin my work as a Ram and Vijay Shriram postdoctoral fellow with Stanford Data Science
đź“š Publications
- Thomas, A. W., RĂ©, 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., RĂ©, C., & Poldrack, R. A. (2022). Comparing interpretation methods in mental state decoding analyses with deep learning models. arXiv preprint arXiv:2205.15581.
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- Thomas, A. W., RĂ©, 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
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- 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
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- Thomas, A. W., Heekeren, H. R., MĂĽller, 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., MĂĽller, 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
- The basics of deep learning
- Very quick introduction to deep learning
- Tutorial on reproducible computational modeling
đź“© reach out: athms.research@gmail.com