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© 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 also affiliated with Stanford's Center for Research on Foundation Models, Stanford's Center for Open and Reproducible Science, and the Max Planck Institute for Human Development.
🧠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
- [November, 2022] Super happy that our recent work on exploring differential programming as a paradigm to learn analysis pipelines for functional connectomics is now accepted at the upcoming Machine Learning for Health (ML4H) workshop at NeurIPS!
- [October, 2022] Our latest review on “Interpreting mental state decoding with deep learning models” is now published in Trends in Cognitive Science!
- [September, 2022] Looking forward to presenting our paper on self-supervised learning for neuroimaging data at NeurIPS 2022 in New Orleans!
- [July, 2022] Our recent work on the role of gaze allocation in multi-alternative risky choice is now published in PLOS Computational Biology!
- [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). Interpreting mental state decoding with deep learning models. Trends in Cognitive Sciences, 26(11), 972-986. doi.org/10.1016/j.tics.2022.07.003.
- Thomas, A. W., RĂ©, 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., RĂ©, 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. doi.org/10.1371/journal.pcbi.1010283
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- 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. doi.org/10.7554/eLife.57012
- Thomas, A. W., Lindenberger, U., Samek, W., & MĂĽller, K. R. (2021). Evaluating deep transfer learning for whole-brain cognitive decoding. arXiv preprint arXiv:2111.01562.
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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. 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