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Date
IP Address
2021-02-01
172.67.154.146
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ClassC
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2024-07-12
76.76.21.21
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Port 443
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1);--heading5-size:calc(var(--heading-size) * 0.8125);--scrollbar-width:15px;--navbar-height:56px;--navbar-shadow: ;--navbar-button-border-radii:50px;--navbar-list-width-single-column:320px;--navbar-list-width:620px;--sidebar-width:280px;--sidebar-shadow: }html{scroll-padding-top:62px}body{font-family:var(--secondary-font)} /style>style id__jsx-undefined> html { font-size: 16px; } /style>/head>body>div classsuper-root>div classsuper-content-wrapper>main idpage-index classsuper-content page__index>nav classnotion-navbar>div classnotion-navbar__content>div classnotion-breadcrumb>a classnotion-link notion-breadcrumb__item single href/>div classnotion-navbar__title notion-breadcrumb__title>Armin Thomas/div>/a>/div>div classnotion-navbar__actions>/div>/div>/nav>div classnotion-header page>div classnotion-header__cover no-cover>/div>div classnotion-header__content max-width no-cover no-icon>div classnotion-header__title-wrapper>h1 classnotion-header__title>Armin Thomas/h1>/div>/div>/div>article idblock-1b182ae509214845be8136265dd800eb classnotion-root max-width>p idblock-a6c6dbb982d6408c8268763e7817c988 classnotion-text notion-text__content notion-semantic-string>span classhighlighted-color color-purple>a hrefhttp://linkedin.com/in/armin-thomas-57a66b98 classnotion-link link target_blank relnoopener noreferrer>LinkedIn/a>/span>span classhighlighted-color color-purple> /span>span classhighlighted-color color-purple>a hrefhttps://scholar.google.com/citations?userawtZJwkAAAAJ&hlen classnotion-link link target_blank relnoopener noreferrer>Google Scholar/a>/span>span classhighlighted-color color-purple> /span>span classhighlighted-color color-purple>a hrefhttps://github.com/athms classnotion-link link target_blank relnoopener noreferrer>GitHub/a>/span>/p>p idblock-33a332713fa647039896f39f6c989bcb classnotion-text notion-text__content notion-semantic-string>I am aspan classhighlighted-color color-pink> /span>span classhighlighted-color color-orange>a hrefhttps://datascience.stanford.edu/news/2020-2022-ram-and-vijay-shriram-data-science-fellows classnotion-link link target_blank relnoopener noreferrer>Ram and Vijay Shriram Data Science Fellow/a>/span>span classhighlighted-color color-orange> /span>at span classhighlighted-color color-orange>a hrefhttps://www.stanford.edu classnotion-link link target_blank relnoopener noreferrer>Stanford University/a>/span>, where I work in artificial intelligence research and its application to computational neuroscience and biology together with a hrefhttps://poldrack.github.io classnotion-link link target_blank relnoopener noreferrer>Russell A. Poldrack/a> and a hrefhttps://cs.stanford.edu/~chrismre/ classnotion-link link target_blank relnoopener noreferrer>Christopher Ré/a>. At Stanford, I am also affiliated with a hrefhttps://datascience.stanford.edu/ classnotion-link link target_blank relnoopener noreferrer>Stanford Data Science/a> and Stanford's Centers a hrefhttps://crfm.stanford.edu/ classnotion-link link target_blank relnoopener noreferrer>for Research on Foundation Models/a> and a hrefhttps://datascience.stanford.edu/cores classnotion-link link target_blank relnoopener noreferrer>for Open and Reproducible Science/a>./p>p idblock-1062fe8f92bf457ebf88ea338585295f classnotion-text notion-text__content notion-semantic-string>Prior to Stanford, I was a research fellow and mentor of the a hrefhttps://cognition.maxplanckschools.org/en classnotion-link link target_blank relnoopener noreferrer>Max Planck School of Cognition/a>, completed a PhD in AI/ML at Technical University of Berlin in the group ofspan classhighlighted-color color-gray> /span>a hrefhttps://scholar.google.com/citations?userjplQac8AAAAJ&hlen&oiao classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-default>Klaus-Robert Müller/span>/a>span classhighlighted-color color-default>, and worked as a /span>research scientist in a hrefhttps://www.hss.caltech.edu/people/antonio-rangel classnotion-link link target_blank relnoopener noreferrer>Antonio Rangel/a>’s Neuroeconomics laboratory at Caltech (in collaboration with Google ATAP)./p>div idblock-c46463ac56ee4d17af327f84d65db2af classnotion-text>p classnotion-text__content notion-semantic-string>My research is focused on diverse research problems in between AI and computational neuroscience. Some of my recent research focuses are…/p>div classnotion-text__children>p idblock-6664b8efd66149e985f8831e7a281849 classnotion-text notion-text__content notion-semantic-string>…training AI systems at scale (e.g., span classhighlighted-color color-default>models trained on large-scale brain data /span>span classhighlighted-color color-purple>a hrefhttps://arxiv.org/abs/2206.11417 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-default>; genomic foundation models such as Evo (7B parameters) /span>span classhighlighted-color color-purple>a hrefhttps://www.biorxiv.org/content/10.1101/2024.02.27.582234v1 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-default> and HyenaDNA (1M context)/span>span classhighlighted-color color-purple> /span>a hrefhttps://arxiv.org/abs/2306.15794 classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Paper/span>/a>span classhighlighted-color color-default>;/span>span classhighlighted-color color-purple> /span>language models with up to 7B parameters span classhighlighted-color color-purple>a hrefhttps://arxiv.org/abs/2403.17844 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span> a hrefhttps://arxiv.org/abs/2212.14052 classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Paper/span>/a>)/p>p idblock-e7b1c1f35f3e4a54bfdbf7eda59a4ad8 classnotion-text notion-text__content notion-semantic-string>…advancing the ability of AI systems to learn from long sequences (e.g., span classhighlighted-color color-purple>a hrefhttps://arxiv.org/abs/2310.12109 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>, span classhighlighted-color color-purple>a hrefhttps://arxiv.org/abs/2212.14052 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-default>,/span>span classhighlighted-color color-purple> /span>a hrefhttps://arxiv.org/abs/2302.06646 classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Paper/span>/a>span classhighlighted-color color-default>,/span>span classhighlighted-color color-purple> /span>a hrefhttps://arxiv.org/abs/2306.15794 classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Paper/span>/a>)/p>p idblock-a3c1a6b453c74416a86068edfc7e182f classnotion-text notion-text__content notion-semantic-string>…developing and evaluating explainable AI tools for neuroscience research (e.g.,span classhighlighted-color color-pink>a hrefhttps://arxiv.org/abs/2205.15581 classnotion-link link target_blank relnoopener noreferrer> /a>/span>a hrefhttps://www.sciencedirect.com/science/article/pii/S1053811923002550?via%3Dihub classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Paper/span>/a>span classhighlighted-color color-default>,/span>span classhighlighted-color color-purple> /span>a hrefhttps://www.sciencedirect.com/science/article/abs/pii/S1364661322001607 classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Paper/span>/a>span classhighlighted-color color-default>,/span>span classhighlighted-color color-purple> /span>a hrefhttps://www.frontiersin.org/articles/10.3389/fnins.2019.01321/full classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Paper/span>/a>)/p>p idblock-4f2c466a608946779e16098fb2fd19f8 classnotion-text notion-text__content notion-semantic-string>…building computational models to better understand the algorithms underlying human choice behavior (e.g., span classhighlighted-color color-purple>a hrefhttps://www.nature.com/articles/s41562-019-0584-8 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>, span classhighlighted-color color-purple>a hrefhttps://elifesciences.org/articles/57012 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-default>,/span>span classhighlighted-color color-purple> /span>a hrefhttps://journals.plos.org/ploscompbiol/article?id10.1371/journal.pcbi.1010283 classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Paper/span>/a>)/p>/div>/div>div idblock-f4844afe31054ba6aeb28683debdb14f classnotion-divider>/div>span classnotion-heading__anchor id86aa149fc9384179ad51a7e58aa70839>/span>h2 idblock-86aa149fc9384179ad51a7e58aa70839 classnotion-heading notion-semantic-string>strong>Papers/strong>/h2>p idblock-e11a8eb5707a4100b205023e59f716dd classnotion-text notion-text__content notion-semantic-string>* indicates equal contribution/p>div idblock-78555d78168243e48ece02c670b46376 classnotion-text>p classnotion-text__content notion-semantic-string>span classhighlighted-color color-gray>2024/span>/p>div classnotion-text__children>p idblock-38dcd69fa7f34acb8ad8b079cf9c4115 classnotion-text notion-text__content notion-semantic-string>strong>Mechanistic Design and Scaling of Hybrid Architectures. /strong>Michael Poli*, Armin W. Thomas*, Eric Nguyen*, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli. ArXiv. span classhighlighted-color color-purple>a hrefhttps://arxiv.org/abs/2403.17844 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/athms/mad-lab classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>/p>p idblock-b995d879aa0e40d98aa4707617ecd597 classnotion-text notion-text__content notion-semantic-string>strong>Sequence modeling and design from molecular to genome scale with Evo./strong> Eric Nguyen*, Michael Poli*, Matthew G. Durrant*, Armin W. Thomas, Brian Kang, Jeremy Sullivan, Madelena Y. Ng, Ashley Lewis, Aman Patel, Aaron Lou, Stefano Ermon, Stephen A. Baccus, Tina Hernandez-Boussard, Christopher Ré, Patrick D. Hsu*, Brian L Hie*. bioRxiv. a hrefhttps://www.biorxiv.org/content/10.1101/2024.02.27.582234v1 classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Paper/span>/a>a hrefhttps://github.com/HazyResearch/m2 classnotion-link link target_blank relnoopener noreferrer> /a>a hrefhttps://github.com/evo-design/evo classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>a hrefhttps://github.com/HazyResearch/m2 classnotion-link link target_blank relnoopener noreferrer>./a>/p>/div>/div>div idblock-309cf1ee05de4d9482350902a0100237 classnotion-text>p classnotion-text__content notion-semantic-string>span classhighlighted-color color-gray>2023/span>/p>div classnotion-text__children>p idblock-922dbb63cc76477bb8fc925219e100a5 classnotion-text notion-text__content notion-semantic-string>strong>Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture./strong> Daniel Y. Fu, Simran Arora*, Jessica Grogan*, Isys Johnson*, Sabri Eyuboglu*, Armin W. Thomas*, Benjamin Spector, Michael Poli, Atri Rudra, Christopher Ré. em>Advances in Neural Information Processing Systems (NeurIPS)/em>. span classhighlighted-color color-purple>a hrefhttps://arxiv.org/abs/2310.12109 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>a hrefhttps://github.com/HazyResearch/m2 classnotion-link link target_blank relnoopener noreferrer> /a>span classhighlighted-color color-purple>a hrefhttps://github.com/HazyResearch/m2 classnotion-link link target_blank relnoopener noreferrer>Code/a>/span>a hrefhttps://github.com/HazyResearch/m2 classnotion-link link target_blank relnoopener noreferrer>./a> span classhighlighted-color color-orange>Oral/span>span classhighlighted-color color-default>./span>/p>p idblock-3c3c2f0f4ef745b8b4ca36af6daa461a classnotion-text notion-text__content notion-semantic-string>strong>HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. /strong>Eric Nguyen*, Michael Poli*, Marjan Faizi*, Armin W. Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Clayton Rabideau, Stefano Massaroli, Yoshua Bengio, Stefano Ermon, Stephen A. Baccus, Chris Ré. em>Advances in Neural Information Processing Systems (NeurIPS)/em>. span classhighlighted-color color-purple>a hrefhttps://arxiv.org/abs/2306.15794 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/HazyResearch/hyena-dna classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>. span classhighlighted-color color-orange>Spotlight/span>span classhighlighted-color color-default>./span>/p>p idblock-bdf9f4766064447694b3f2e3cfe54809 classnotion-text notion-text__content notion-semantic-string>strong>Benchmarking explanation methods for mental state decoding with deep learning models./strong> span classhighlighted-color color-default>Armin W. Thomas, Christopher /span>Ré, Russell A. Poldrack. em>NeuroImage/em>. span classhighlighted-color color-purple>a hrefhttps://doi.org/10.1016/j.neuroimage.2023.120109 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/athms/xai-brain-decoding-benchmark classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>/p>p idblock-fa8fd094cb08499fb109af847c7ff651 classnotion-text notion-text__content notion-semantic-string>strong>Evaluating deep transfer learning for whole-brain cognitive decoding./strong> Armin W. span classhighlighted-color color-default>Thomas,/span> Ullman Lindenberger, Wojciech Samek, Klaus-Robert Müller. em>Journal of the Franklin Institute/em>. span classhighlighted-color color-purple>a hrefhttps://doi.org/10.1016/j.jfranklin.2023.07.015 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/athms/evaluating-deeplight-transfer classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>./p>p idblock-2556f4e245d84acf8ab21f285cde150f classnotion-text notion-text__content notion-semantic-string>strong>Simple Hardware-Efficient Long Convolutions for Sequence Modeling./strong> Daniel Y. Fu*, Elliot Epstein*, Eric Nguyen, Armin W. span classhighlighted-color color-default>Thomas, Michael /span>Zhang, Tri Dao, Atri Rudra, Christopher Ré. em>International Conference on Machine Learning (ICML). /em>span classhighlighted-color color-purple>a hrefhttps://proceedings.mlr.press/v202/fu23a classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/HazyResearch/safari classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>span classhighlighted-color color-default>./span>/p>p idblock-d3429ea000f6480d82c133a61ef557cb classnotion-text notion-text__content notion-semantic-string>strong>Hungry Hungry Hippos: Towards Language Modeling with State Space Models. /strong>Tri Dao*, Daniel Y. Fu,* Khaled K. Saab,strong> /strong>Armin W. span classhighlighted-color color-default>Thomas, Atri /span>Rudra, Christopher Ré. em>International Conference on Learning Representations (ICLR)/em>. Preprint: span classhighlighted-color color-purple>a hrefhttps://arxiv.org/abs/2212.14052v1 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/HazyResearch/h3 classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>. span classhighlighted-color color-orange>Spotlight/span>span classhighlighted-color color-default>./span>/p>/div>/div>div idblock-fe31a7078eec425ab7b4ed406640732a classnotion-text>p classnotion-text__content notion-semantic-string>span classhighlighted-color color-gray>2022/span>/p>div classnotion-text__children>p idblock-4ef56f6e7941417aa9d2f99683aeb7d4 classnotion-text notion-text__content notion-semantic-string>strong>Interpreting mental state decoding with deep learning models./strong>span classhighlighted-color color-default>strong> /strong>/span>span classhighlighted-color color-default>Armin W. Thomas, Christopher /span>Ré, Russell A. Poldrack. em>Trends in Cognitive Sciences/em>. span classhighlighted-color color-purple>a hrefhttps://doi.org/10.1016/j.tics.2022.07.003 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>./p>p idblock-ce72ced9281e42fa881e25d17338b69b classnotion-text notion-text__content notion-semantic-string>strong>Self-supervised learning of brain dynamics from broad neuroimaging data. /strong>span classhighlighted-color color-default>Armin W. Thomas, Christopher /span>Ré, Russell A. Poldrack em>Advances in Neural Information Processing Systems (NeurIPS)/em>. span classhighlighted-color color-purple>a hrefhttps://proceedings.neurips.cc/paper_files/paper/2022/hash/8600a9df1a087a9a66900cc8c948c3f0-Abstract-Conference.html classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttp://github.com/athms/learning-from-brains classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>./p>p idblock-e86a42bf0d9e427d94dbc6a389ba0657 classnotion-text notion-text__content notion-semantic-string>strong>Differentiable programming for functional connectomics./strong> Rastko Ciric, Armin W. span classhighlighted-color color-default>Thomas,/span> Oscar Esteban, Russell A. Poldrack. em>Machine Learning for Health Workshop at NeurIPS/em>. span classhighlighted-color color-purple>a hrefhttps://proceedings.mlr.press/v193/ciric22a.html classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/hypercoil/hypercoil classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>. span classhighlighted-color color-orange>Best Poster/span>span classhighlighted-color color-purple>./span>/p>p idblock-402f566d8b2641edab6d571a0c47af8b classnotion-text notion-text__content notion-semantic-string>strong>Gaze-dependent evidence accumulation predicts multi-alternative risky choice behaviour. /strong>Felix Molter, Armin W. span classhighlighted-color color-default>Thomas,/span> Scott A. Huettel., Hauke R. Heekeren, Peter N. Mohr. em>PLoS computational biology/em>. span classhighlighted-color color-purple>a hrefhttp://doi.org/10.1371/journal.pcbi.1010283 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/moltaire/gda-context classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>./p>/div>/div>div idblock-082109bedd834531b0731cd52f7178c7 classnotion-text>p classnotion-text__content notion-semantic-string>span classhighlighted-color color-gray>2021/span>/p>div classnotion-text__children>p idblock-4e34aef2d7a4485ab302982dc2af0588 classnotion-text notion-text__content notion-semantic-string>strong>On the opportunities and risks of foundation models./strong> Rishi Bommasani, R., …,span classhighlighted-color color-default> Armin W. Thomas, .../span> & Percy Liang. em>ArXiv/em>.em> /em>span classhighlighted-color color-purple>a hrefhttps://arxiv.org/abs/2108.07258 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>./p>p idblock-7073d0c4535e4eb991fa5eafe26b92f6 classnotion-text notion-text__content notion-semantic-string>strong>Uncovering the computational mechanisms underlying many-alternative choice. /strong>Armin W. Thomas, Felix Molter, Ian Krajbich. em>Elife/em>. span classhighlighted-color color-purple>a hrefhttps://doi.org/10.7554/eLife.57012 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/athms/many-item-choice classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>./p>/div>/div>div idblock-55a93a78d25b4aaa96adc547d6bcd444 classnotion-text>p classnotion-text__content notion-semantic-string>span classhighlighted-color color-gray>2020/span>/p>div classnotion-text__children>p idblock-3bbd4d3c049842fdb715fdc95e190f82 classnotion-text notion-text__content notion-semantic-string>strong>Machine learning methods for modeling gaze allocation in simple choice behavior and functional neuroimaging data on the level of the individual./strong> Armin W. span classhighlighted-color color-default>Thomas/span>. Technische Universität Berlin, Berlin. span classhighlighted-color color-purple>a hrefhttps://doi.org/10.14279/depositonce-10932 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-default>./span>/p>/div>/div>div idblock-9a8e82cf3ea54e1e9f60db73d2e1f978 classnotion-text>p classnotion-text__content notion-semantic-string>span classhighlighted-color color-gray>2019/span>/p>div classnotion-text__children>p idblock-f08137c9a31643dea4febff30a7f8d53 classnotion-text notion-text__content notion-semantic-string>strong>Gaze bias differences capture individual choice behaviour. /strong>Armin W. Thomas*, Felix Molter*, Ian Krajbich, Hauke R. Heekeren, Peter N. Mohr. em>Nature human behaviour/em>. span classhighlighted-color color-purple>a hrefhttp://doi.org/10.1038/s41562-019-0584-8 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/glamlab/gaze-bias-differences classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>./p>p idblock-d87ff87180604280af1e1131a933a1c7 classnotion-text notion-text__content notion-semantic-string>strong>Analyzing Neuroimaging Data Through Recurrent Deep Learning Models./strong> Armin W. Thomas, Hauke R. Heekeren, Klaus-Robert Müller, Wojciech Samek. em>Frontiers in Neuroscience/em>. span classhighlighted-color color-purple>a hrefhttp://doi.org/10.3389/fnins.2019.01321 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>./p>p idblock-d1492851b30c4219b1e27280054a6739 classnotion-text notion-text__content notion-semantic-string>strong>GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour. /strong>Felix Molter*, Armin W. Thomas*, Hauke R. Heekeren, Peter N. Mohr. em>PloS one/em>. span classhighlighted-color color-purple>a hrefhttp://doi.org/10.1371/journal.pone.0226428 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>span classhighlighted-color color-purple> /span>a hrefhttps://github.com/glamlab/glambox classnotion-link link target_blank relnoopener noreferrer>span classhighlighted-color color-purple>Code/span>/a>./p>p idblock-5362791ba86d41d99a77fc5a13c15e96 classnotion-text notion-text__content notion-semantic-string>strong>Deep transfer learning for whole-brain FMRI analyses. /strong>Armin W. Thomas, KLaus-Robert Müller, Wojciech Samek. em>Machine Learning in Clinical Neuroimaging Workshop at MICCAI 2019/em>. span classhighlighted-color color-purple>a hrefhttp://doi.org/10.1007/978-3-030-32695-1_7 classnotion-link link target_blank relnoopener noreferrer>Paper/a>/span>./p>/div>/div>div idblock-3c74d02689744cfbbbd998a5d9d89247 classnotion-divider>/div>span classnotion-heading__anchor idd12131f037174f3cab0f8f92f351e1bc>/span>h2 idblock-d12131f037174f3cab0f8f92f351e1bc classnotion-heading notion-semantic-string>Code/h2>p idblock-8c0a426053384e37a39a5ac551ad4da2 classnotion-text notion-text__content notion-semantic-string>I believe in open science and therefore put strong emphasis on open sourcing all code and data used for my research and teaching. Find some examples of open source projects below:/p>p idblock-512e3e6d037e4cc9a81391d062608a84 classnotion-text notion-text__content notion-semantic-string>strong>Research:/strong>/p>ul classnotion-bulleted-list>li idblock-8208172dec27480a8deeae606b29debc classnotion-list-item notion-semantic-string>a hrefhttps://github.com/athms/mad-lab classnotion-link link target_blank relnoopener noreferrer>MAD: Mechanistic Architecture Design to build improved AI architectures/a>/li>li idblock-e2a843921a244caebc159a246c780327 classnotion-list-item notion-semantic-string>a hrefhttps://github.com/evo-design/evo classnotion-link link target_blank relnoopener noreferrer>Evo: DNA foundation modeling from molecular to genome scale/a>/li>li idblock-ad4678d10eb840d3af17c19ff1be4987 classnotion-list-item notion-semantic-string>a hrefhttps://github.com/athms/learning-from-brains classnotion-link link target_blank relnoopener noreferrer>Self-supervised learning from brains/a>/li>li idblock-196d5791284c42a7b3c561e98224672b classnotion-list-item notion-semantic-string>a hrefhttps://github.com/HazyResearch/hyena-dna classnotion-link link target_blank relnoopener noreferrer>HyenaDNA: long-context genomic models/a>/li>li idblock-6d7feb948e024420a12ef572d8362152 classnotion-list-item notion-semantic-string>a hrefhttps://github.com/athms/xai-brain-decoding-benchmark classnotion-link link target_blank relnoopener noreferrer>Benchmarking explanation methods for neuroscience/a>/li>li idblock-16560de9e0a745d291457e2f13f580e5 classnotion-list-item notion-semantic-string>a hrefhttps://github.com/athms/evaluating-deeplight-transfer/ classnotion-link link target_blank relnoopener noreferrer>Evaluating deep transfer learning for neuroimaging/a>/li>li idblock-ff6e908ee8a240f2a4a94a7fc63ead7b classnotion-list-item notion-semantic-string>a hrefhttps://github.com/athms/gaze-bias-differences classnotion-link link target_blank relnoopener noreferrer>Modeling individual choice behavior/a>/li>li idblock-3b5154d4268848808384454a33b0cccb classnotion-list-item notion-semantic-string>a hrefhttps://github.com/glamlab/glambox classnotion-link link target_blank relnoopener noreferrer>A python toolbox for the gaze-weighted linear accumulator model (GLAM)/a>/li>li idblock-bedbb4314ec3446f90d95d2a45385ee6 classnotion-list-item notion-semantic-string>a hrefhttps://github.com/athms/many-item-choice classnotion-link link target_blank relnoopener noreferrer>Modeling individual choice behavior from many alternatives/a>/li>/ul>p idblock-ce225d3b5f6e48e3ad5f7c4283e4b460 classnotion-text notion-text__content notion-semantic-string>strong>Teaching & Tutorials:/strong>/p>ul classnotion-bulleted-list>li idblock-72188f55b1c548f4b4a6e269d1f94a5d classnotion-list-item notion-semantic-string>a hrefhttps://github.com/athms/deep-learning-basics classnotion-link link target_blank relnoopener noreferrer>Introduction to deep learning basics/a>/li>li idblock-105f4bb8f404413ebf4c958720ccc1c0 classnotion-list-item notion-semantic-string>a hrefhttps://github.com/athms/reproducible-modelling classnotion-link link target_blank relnoopener noreferrer>Introduction to reproducible computational modeling/a>/li>/ul>div idblock-7fc24fb1c95f4f40a80190e91e41cdb0 classnotion-divider>/div>span classnotion-heading__anchor ida73301b97d284f4caf05d665b5713a0b>/span>h3 idblock-a73301b97d284f4caf05d665b5713a0b classnotion-heading notion-semantic-string>Thanks to my mentors 🙏🏻 /h3>p idblock-ad4bfd4023f64af1bc5666caad92e28d classnotion-text notion-text__content notion-semantic-string>I’ve been very fortunate to have been advised by brilliant mentors, among them a hrefhttps://profiles.stanford.edu/russell-poldrack classnotion-link link target_blank relnoopener noreferrer>Russell A. Poldrack/a> (Stanford), a hrefhttps://cs.stanford.edu/~chrismre/ classnotion-link link target_blank relnoopener noreferrer>Christopher Ré/a> (Stanford), a hrefhttps://www.uni-hamburg.de/en/uhh/organisation/praesidium/praesident.html classnotion-link link target_blank relnoopener noreferrer>Hauke R. Heekeren/a> (Hamburg University), a hrefhttps://www.hss.caltech.edu/people/antonio-rangel classnotion-link link target_blank relnoopener noreferrer>Antonio Rangel/a> (Caltech), a hrefhttps://scholar.google.com/citations?userjplQac8AAAAJ&hlen&oiao classnotion-link link target_blank relnoopener noreferrer>Klaus-Robert Müller/a> (TU Berlin), a hrefhttps://psychology.osu.edu/people/krajbich.1 classnotion-link link target_blank relnoopener noreferrer>Ian Krajbich/a> (UCLA), and a hrefhttps://www.mpib-berlin.mpg.de/staff/ulman-lindenberger classnotion-link link target_blank relnoopener noreferrer>Ulman Lindenberger/a> (Max Planck). I’m grateful to them for their advice and support through the years./p>div idblock-4a239082c6e84ecba9dca6bf74c9bc20 classnotion-divider>/div>p idblock-020a78a2faf54620bc85a99036cf8d8c classnotion-text notion-text__content notion-semantic-string>📩span classhighlighted-color color-default> gmail: athms.research/span>/p>div idblock-2d125284b3744badb7f9cd246c414ce4 classnotion-text>/div>/article>/main>/div>/div>!--$!-->template data-dgstBAILOUT_TO_CLIENT_SIDE_RENDERING>/template>!--/$-->!--$!-->template data-dgstBAILOUT_TO_CLIENT_SIDE_RENDERING>/template>!--/$-->script src/_next/static/chunks/webpack-c9af07d5a568c5cf.js 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Thomas\,\updatedAt\:1711711987060,\type\:\page\,\spaceId\:\18fa346f-2796-4297-b030-91dce8fe74e1\,\coverPosition\:0.6062,\cardCoverPosition\:0,\createdTime\:1582972740000,\lastEditedTime\:1711711987060,\createdBy\:{\0\:\‣\,\1\:{\0\:\u\,\1\:\8a1cf299-c977-45b3-8170-718e6fb9b0ea\}},\lastEditedBy\:{\0\:\‣\,\1\:{\0\:\u\,\1\:\8a1cf299-c977-45b3-8170-718e6fb9b0ea\}},\uri\:\/1b182ae509214845be8136265dd800eb\,\fullWidth\:false,\smallText\:false,\noCover\:true,\superProperties\:{},\propertyValues\:{},\blockId\:\1b182ae5-0921-4845-be81-36265dd800eb\},\a6c6dbb982d6408c8268763e7817c988\:{\id\:\a6c6dbb982d6408c8268763e7817c988\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\LinkedIn\,\a\,\http://linkedin.com/in/armin-thomas-57a66b98\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Google Scholar\,\a\,\https://scholar.google.com/citations?userawtZJwkAAAAJ\u0026hlen\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\GitHub\,\a\,\https://github.com/athms\,\h\,\ColorPurple\,\updatedAt\:1697739388446,\type\:\text\},\33a332713fa647039896f39f6c989bcb\:{\id\:\33a332713fa647039896f39f6c989bcb\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\I am a\,\ \,\h\,\ColorPink\,\Ram and Vijay Shriram Data Science Fellow\,\a\,\https://datascience.stanford.edu/news/2020-2022-ram-and-vijay-shriram-data-science-fellows\,\h\,\ColorOrange\,\ \,\h\,\ColorOrange\,\at \,\Stanford University\,\a\,\https://www.stanford.edu\,\h\,\ColorOrange\,\, where I work in artificial intelligence research and its application to computational neuroscience and biology together with \,\Russell A. Poldrack\,\a\,\https://poldrack.github.io\,\ and \,\Christopher Ré\,\a\,\https://cs.stanford.edu/~chrismre/\,\. At Stanford, I am also affiliated with \,\Stanford Data Science\,\a\,\https://datascience.stanford.edu/\,\ and Stanfords Centers \,\for Research on Foundation Models\,\a\,\https://crfm.stanford.edu/\,\ and \,\for Open and Reproducible Science\,\a\,\https://datascience.stanford.edu/cores\,\.\,\updatedAt\:1709303038701,\type\:\text\},\1062fe8f92bf457ebf88ea338585295f\:{\id\:\1062fe8f92bf457ebf88ea338585295f\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Prior to Stanford, I was a research fellow and mentor of the \,\Max Planck School of Cognition\,\a\,\https://cognition.maxplanckschools.org/en\,\, completed a PhD in AI/ML at Technical University of Berlin in the group of\,\ \,\h\,\ColorGray\,\Klaus-Robert Müller\,\h\,\ColorDefault\,\a\,\https://scholar.google.com/citations?userjplQac8AAAAJ\u0026hlen\u0026oiao\,\, and worked as a \,\h\,\ColorDefault\,\research scientist in \,\Antonio Rangel\,\a\,\https://www.hss.caltech.edu/people/antonio-rangel\,\’s Neuroeconomics laboratory at Caltech (in collaboration with Google ATAP).\,\updatedAt\:1701470536580,\type\:\text\},\c46463ac56ee4d17af327f84d65db2af\:{\id\:\c46463ac56ee4d17af327f84d65db2af\,\children\:\6664b8efd66149e985f8831e7a281849\,\e7b1c1f35f3e4a54bfdbf7eda59a4ad8\,\a3c1a6b453c74416a86068edfc7e182f\,\4f2c466a608946779e16098fb2fd19f8\,\hasContent\:true,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\My research is focused on diverse research problems in between AI and computational neuroscience. Some of my recent research focuses are…\,\updatedAt\:1697901803411,\type\:\text\},\6664b8efd66149e985f8831e7a281849\:{\id\:\6664b8efd66149e985f8831e7a281849\,\children\:,\hasContent\:false,\parentId\:\c46463ac56ee4d17af327f84d65db2af\,\title\:\…training AI systems at scale (e.g., \,\models trained on large-scale brain data \,\h\,\ColorDefault\,\Paper\,\a\,\https://arxiv.org/abs/2206.11417\,\h\,\ColorPurple\,\; genomic foundation models such as Evo (7B parameters) \,\h\,\ColorDefault\,\Paper\,\a\,\https://www.biorxiv.org/content/10.1101/2024.02.27.582234v1\,\h\,\ColorPurple\,\ and HyenaDNA (1M context)\,\h\,\ColorDefault\,\ \,\h\,\ColorPurple\,\Paper\,\h\,\ColorPurple\,\a\,\https://arxiv.org/abs/2306.15794\,\;\,\h\,\ColorDefault\,\ \,\h\,\ColorPurple\,\language models with up to 7B parameters \,\Paper\,\a\,\https://arxiv.org/abs/2403.17844\,\h\,\ColorPurple\,\ \,\Paper\,\h\,\ColorPurple\,\a\,\https://arxiv.org/abs/2212.14052\,\)\,\updatedAt\:1711711826874,\type\:\text\},\e7b1c1f35f3e4a54bfdbf7eda59a4ad8\:{\id\:\e7b1c1f35f3e4a54bfdbf7eda59a4ad8\,\children\:,\hasContent\:false,\parentId\:\c46463ac56ee4d17af327f84d65db2af\,\title\:\…advancing the ability of AI systems to learn from long sequences (e.g., \,\Paper\,\a\,\https://arxiv.org/abs/2310.12109\,\h\,\ColorPurple\,\, \,\Paper\,\a\,\https://arxiv.org/abs/2212.14052\,\h\,\ColorPurple\,\,\,\h\,\ColorDefault\,\ \,\h\,\ColorPurple\,\Paper\,\h\,\ColorPurple\,\a\,\https://arxiv.org/abs/2302.06646\,\,\,\h\,\ColorDefault\,\ \,\h\,\ColorPurple\,\Paper\,\h\,\ColorPurple\,\a\,\https://arxiv.org/abs/2306.15794\,\)\,\updatedAt\:1701493951253,\type\:\text\},\a3c1a6b453c74416a86068edfc7e182f\:{\id\:\a3c1a6b453c74416a86068edfc7e182f\,\children\:,\hasContent\:false,\parentId\:\c46463ac56ee4d17af327f84d65db2af\,\title\:\…developing and evaluating explainable AI tools for neuroscience research (e.g.,\,\ \,\a\,\https://arxiv.org/abs/2205.15581\,\h\,\ColorPink\,\Paper\,\h\,\ColorPurple\,\a\,\https://www.sciencedirect.com/science/article/pii/S1053811923002550?via%3Dihub\,\,\,\h\,\ColorDefault\,\ \,\h\,\ColorPurple\,\Paper\,\h\,\ColorPurple\,\a\,\https://www.sciencedirect.com/science/article/abs/pii/S1364661322001607\,\,\,\h\,\ColorDefault\,\ \,\h\,\ColorPurple\,\Paper\,\h\,\ColorPurple\,\a\,\https://www.frontiersin.org/articles/10.3389/fnins.2019.01321/full\,\)\,\updatedAt\:1701493956136,\type\:\text\},\4f2c466a608946779e16098fb2fd19f8\:{\id\:\4f2c466a608946779e16098fb2fd19f8\,\children\:,\hasContent\:false,\parentId\:\c46463ac56ee4d17af327f84d65db2af\,\title\:\…building computational models to better understand the algorithms underlying human choice behavior (e.g., \,\Paper\,\a\,\https://www.nature.com/articles/s41562-019-0584-8\,\h\,\ColorPurple\,\, \,\Paper\,\a\,\https://elifesciences.org/articles/57012\,\h\,\ColorPurple\,\,\,\h\,\ColorDefault\,\ \,\h\,\ColorPurple\,\Paper\,\h\,\ColorPurple\,\a\,\https://journals.plos.org/ploscompbiol/article?id10.1371/journal.pcbi.1010283\,\)\,\updatedAt\:1701493960933,\type\:\text\},\f4844afe31054ba6aeb28683debdb14f\:{\id\:\f4844afe31054ba6aeb28683debdb14f\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:null,\updatedAt\:1697731535843,\type\:\divider\},\86aa149fc9384179ad51a7e58aa70839\:{\id\:\86aa149fc9384179ad51a7e58aa70839\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Papers\,\b\,null,\updatedAt\:1697730536795,\type\:\heading\,\subType\:\sub_header\,\depth\:2},\e11a8eb5707a4100b205023e59f716dd\:{\id\:\e11a8eb5707a4100b205023e59f716dd\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\* indicates equal contribution\,\updatedAt\:1697733508147,\type\:\text\},\78555d78168243e48ece02c670b46376\:{\id\:\78555d78168243e48ece02c670b46376\,\children\:\38dcd69fa7f34acb8ad8b079cf9c4115\,\b995d879aa0e40d98aa4707617ecd597\,\hasContent\:true,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\2024\,\h\,\ColorGray\,\updatedAt\:1711711854410,\type\:\text\},\38dcd69fa7f34acb8ad8b079cf9c4115\:{\id\:\38dcd69fa7f34acb8ad8b079cf9c4115\,\children\:,\hasContent\:false,\parentId\:\78555d78168243e48ece02c670b46376\,\title\:\Mechanistic Design and Scaling of Hybrid Architectures. \,\b\,null,\Michael Poli*, Armin W. Thomas*, Eric Nguyen*, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli. ArXiv. \,\Paper\,\a\,\https://arxiv.org/abs/2403.17844\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/athms/mad-lab\,\updatedAt\:1711711931953,\type\:\text\},\b995d879aa0e40d98aa4707617ecd597\:{\id\:\b995d879aa0e40d98aa4707617ecd597\,\children\:,\hasContent\:false,\parentId\:\78555d78168243e48ece02c670b46376\,\title\:\Sequence modeling and design from molecular to genome scale with Evo.\,\b\,null,\ Eric Nguyen*, Michael Poli*, Matthew G. Durrant*, Armin W. Thomas, Brian Kang, Jeremy Sullivan, Madelena Y. Ng, Ashley Lewis, Aman Patel, Aaron Lou, Stefano Ermon, Stephen A. Baccus, Tina Hernandez-Boussard, Christopher Ré, Patrick D. Hsu*, Brian L Hie*. bioRxiv. \,\Paper\,\h\,\ColorPurple\,\a\,\https://www.biorxiv.org/content/10.1101/2024.02.27.582234v1\,\ \,\a\,\https://github.com/HazyResearch/m2\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/evo-design/evo\,\.\,\a\,\https://github.com/HazyResearch/m2\,\updatedAt\:1711711854410,\type\:\text\},\309cf1ee05de4d9482350902a0100237\:{\id\:\309cf1ee05de4d9482350902a0100237\,\children\:\922dbb63cc76477bb8fc925219e100a5\,\3c3c2f0f4ef745b8b4ca36af6daa461a\,\bdf9f4766064447694b3f2e3cfe54809\,\fa8fd094cb08499fb109af847c7ff651\,\2556f4e245d84acf8ab21f285cde150f\,\d3429ea000f6480d82c133a61ef557cb\,\hasContent\:true,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\2023\,\h\,\ColorGray\,\updatedAt\:1709131403131,\type\:\text\},\922dbb63cc76477bb8fc925219e100a5\:{\id\:\922dbb63cc76477bb8fc925219e100a5\,\children\:,\hasContent\:false,\parentId\:\309cf1ee05de4d9482350902a0100237\,\title\:\Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture.\,\b\,null,\ Daniel Y. Fu, Simran Arora*, Jessica Grogan*, Isys Johnson*, Sabri Eyuboglu*, Armin W. Thomas*, Benjamin Spector, Michael Poli, Atri Rudra, Christopher Ré. \,\Advances in Neural Information Processing Systems (NeurIPS)\,\i\,null,\. \,\Paper\,\a\,\https://arxiv.org/abs/2310.12109\,\h\,\ColorPurple\,\ \,\a\,\https://github.com/HazyResearch/m2\,\Code\,\a\,\https://github.com/HazyResearch/m2\,\h\,\ColorPurple\,\.\,\a\,\https://github.com/HazyResearch/m2\,\ \,\Oral\,\h\,\ColorOrange\,\.\,\h\,\ColorDefault\,\updatedAt\:1709131568524,\type\:\text\},\3c3c2f0f4ef745b8b4ca36af6daa461a\:{\id\:\3c3c2f0f4ef745b8b4ca36af6daa461a\,\children\:,\hasContent\:false,\parentId\:\309cf1ee05de4d9482350902a0100237\,\title\:\HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. \,\b\,null,\Eric Nguyen*, Michael Poli*, Marjan Faizi*, Armin W. Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Clayton Rabideau, Stefano Massaroli, Yoshua Bengio, Stefano Ermon, Stephen A. Baccus, Chris Ré. \,\Advances in Neural Information Processing Systems (NeurIPS)\,\i\,null,\. \,\Paper\,\a\,\https://arxiv.org/abs/2306.15794\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/HazyResearch/hyena-dna\,\. \,\Spotlight\,\h\,\ColorOrange\,\.\,\h\,\ColorDefault\,\updatedAt\:1709131403131,\type\:\text\},\bdf9f4766064447694b3f2e3cfe54809\:{\id\:\bdf9f4766064447694b3f2e3cfe54809\,\children\:,\hasContent\:false,\parentId\:\309cf1ee05de4d9482350902a0100237\,\title\:\Benchmarking explanation methods for mental state decoding with deep learning models.\,\b\,null,\ \,\Armin W. Thomas, Christopher \,\h\,\ColorDefault\,\Ré, Russell A. Poldrack. \,\NeuroImage\,\i\,null,\. \,\Paper\,\a\,\https://doi.org/10.1016/j.neuroimage.2023.120109\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/athms/xai-brain-decoding-benchmark\,\updatedAt\:1709131403131,\type\:\text\},\fa8fd094cb08499fb109af847c7ff651\:{\id\:\fa8fd094cb08499fb109af847c7ff651\,\children\:,\hasContent\:false,\parentId\:\309cf1ee05de4d9482350902a0100237\,\title\:\Evaluating deep transfer learning for whole-brain cognitive decoding.\,\b\,null,\ Armin W. \,\Thomas,\,\h\,\ColorDefault\,\ Ullman Lindenberger, Wojciech Samek, Klaus-Robert Müller. \,\Journal of the Franklin Institute\,\i\,null,\. \,\Paper\,\a\,\https://doi.org/10.1016/j.jfranklin.2023.07.015\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/athms/evaluating-deeplight-transfer\,\.\,\updatedAt\:1709131403131,\type\:\text\},\2556f4e245d84acf8ab21f285cde150f\:{\id\:\2556f4e245d84acf8ab21f285cde150f\,\children\:,\hasContent\:false,\parentId\:\309cf1ee05de4d9482350902a0100237\,\title\:\Simple Hardware-Efficient Long Convolutions for Sequence Modeling.\,\b\,null,\ Daniel Y. Fu*, Elliot Epstein*, Eric Nguyen, Armin W. \,\Thomas, Michael \,\h\,\ColorDefault\,\Zhang, Tri Dao, Atri Rudra, Christopher Ré. \,\International Conference on Machine Learning (ICML). \,\i\,null,\Paper\,\a\,\https://proceedings.mlr.press/v202/fu23a\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/HazyResearch/safari\,\.\,\h\,\ColorDefault\,\updatedAt\:1709131403131,\type\:\text\},\d3429ea000f6480d82c133a61ef557cb\:{\id\:\d3429ea000f6480d82c133a61ef557cb\,\children\:,\hasContent\:false,\parentId\:\309cf1ee05de4d9482350902a0100237\,\title\:\Hungry Hungry Hippos: Towards Language Modeling with State Space Models. \,\b\,null,\Tri Dao*, Daniel Y. Fu,* Khaled K. Saab,\,\ \,\b\,null,\Armin W. \,\Thomas, Atri \,\h\,\ColorDefault\,\Rudra, Christopher Ré. \,\International Conference on Learning Representations (ICLR)\,\i\,null,\. Preprint: \,\Paper\,\a\,\https://arxiv.org/abs/2212.14052v1\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/HazyResearch/h3\,\. \,\Spotlight\,\h\,\ColorOrange\,\.\,\h\,\ColorDefault\,\updatedAt\:1709131403131,\type\:\text\},\fe31a7078eec425ab7b4ed406640732a\:{\id\:\fe31a7078eec425ab7b4ed406640732a\,\children\:\4ef56f6e7941417aa9d2f99683aeb7d4\,\ce72ced9281e42fa881e25d17338b69b\,\e86a42bf0d9e427d94dbc6a389ba0657\,\402f566d8b2641edab6d571a0c47af8b\,\hasContent\:true,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\2022\,\h\,\ColorGray\,\updatedAt\:1697730549943,\type\:\text\},\4ef56f6e7941417aa9d2f99683aeb7d4\:{\id\:\4ef56f6e7941417aa9d2f99683aeb7d4\,\children\:,\hasContent\:false,\parentId\:\fe31a7078eec425ab7b4ed406640732a\,\title\:\Interpreting mental state decoding with deep learning models.\,\b\,null,\ \,\b\,null,\h\,\ColorDefault\,\Armin W. Thomas, Christopher \,\h\,\ColorDefault\,\Ré, Russell A. Poldrack. \,\Trends in Cognitive Sciences\,\i\,null,\. \,\Paper\,\a\,\https://doi.org/10.1016/j.tics.2022.07.003\,\h\,\ColorPurple\,\.\,\updatedAt\:1699830990940,\type\:\text\},\ce72ced9281e42fa881e25d17338b69b\:{\id\:\ce72ced9281e42fa881e25d17338b69b\,\children\:,\hasContent\:false,\parentId\:\fe31a7078eec425ab7b4ed406640732a\,\title\:\Self-supervised learning of brain dynamics from broad neuroimaging data. \,\b\,null,\Armin W. Thomas, Christopher \,\h\,\ColorDefault\,\Ré, Russell A. Poldrack \,\Advances in Neural Information Processing Systems (NeurIPS)\,\i\,null,\. \,\Paper\,\a\,\https://proceedings.neurips.cc/paper_files/paper/2022/hash/8600a9df1a087a9a66900cc8c948c3f0-Abstract-Conference.html\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\http://github.com/athms/learning-from-brains\,\.\,\updatedAt\:1699832183395,\type\:\text\},\e86a42bf0d9e427d94dbc6a389ba0657\:{\id\:\e86a42bf0d9e427d94dbc6a389ba0657\,\children\:,\hasContent\:false,\parentId\:\fe31a7078eec425ab7b4ed406640732a\,\title\:\Differentiable programming for functional connectomics.\,\b\,null,\ Rastko Ciric, Armin W. \,\Thomas,\,\h\,\ColorDefault\,\ Oscar Esteban, Russell A. Poldrack. \,\Machine Learning for Health Workshop at NeurIPS\,\i\,null,\. \,\Paper\,\a\,\https://proceedings.mlr.press/v193/ciric22a.html\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/hypercoil/hypercoil\,\. \,\Best Poster\,\h\,\ColorOrange\,\.\,\h\,\ColorPurple\,\updatedAt\:1699832235840,\type\:\text\},\402f566d8b2641edab6d571a0c47af8b\:{\id\:\402f566d8b2641edab6d571a0c47af8b\,\children\:,\hasContent\:false,\parentId\:\fe31a7078eec425ab7b4ed406640732a\,\title\:\Gaze-dependent evidence accumulation predicts multi-alternative risky choice behaviour. \,\b\,null,\Felix Molter, Armin W. \,\Thomas,\,\h\,\ColorDefault\,\ Scott A. Huettel., Hauke R. Heekeren, Peter N. Mohr. \,\PLoS computational biology\,\i\,null,\. \,\Paper\,\a\,\http://doi.org/10.1371/journal.pcbi.1010283\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/moltaire/gda-context\,\.\,\updatedAt\:1699832259195,\type\:\text\},\082109bedd834531b0731cd52f7178c7\:{\id\:\082109bedd834531b0731cd52f7178c7\,\children\:\4e34aef2d7a4485ab302982dc2af0588\,\7073d0c4535e4eb991fa5eafe26b92f6\,\hasContent\:true,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\2021\,\h\,\ColorGray\,\updatedAt\:1697730555921,\type\:\text\},\4e34aef2d7a4485ab302982dc2af0588\:{\id\:\4e34aef2d7a4485ab302982dc2af0588\,\children\:,\hasContent\:false,\parentId\:\082109bedd834531b0731cd52f7178c7\,\title\:\On the opportunities and risks of foundation models.\,\b\,null,\ Rishi Bommasani, R., …,\,\ Armin W. Thomas, ...\,\h\,\ColorDefault\,\ \u0026 Percy Liang. \,\ArXiv\,\i\,null,\.\,\ \,\i\,null,\Paper\,\a\,\https://arxiv.org/abs/2108.07258\,\h\,\ColorPurple\,\.\,\updatedAt\:1699831036538,\type\:\text\},\7073d0c4535e4eb991fa5eafe26b92f6\:{\id\:\7073d0c4535e4eb991fa5eafe26b92f6\,\children\:,\hasContent\:false,\parentId\:\082109bedd834531b0731cd52f7178c7\,\title\:\Uncovering the computational mechanisms underlying many-alternative choice. \,\b\,null,\Armin W. Thomas, Felix Molter, Ian Krajbich. \,\Elife\,\i\,null,\. \,\Paper\,\a\,\https://doi.org/10.7554/eLife.57012\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/athms/many-item-choice\,\.\,\updatedAt\:1699832273589,\type\:\text\},\55a93a78d25b4aaa96adc547d6bcd444\:{\id\:\55a93a78d25b4aaa96adc547d6bcd444\,\children\:\3bbd4d3c049842fdb715fdc95e190f82\,\hasContent\:true,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\2020\,\h\,\ColorGray\,\updatedAt\:1697730559031,\type\:\text\},\3bbd4d3c049842fdb715fdc95e190f82\:{\id\:\3bbd4d3c049842fdb715fdc95e190f82\,\children\:,\hasContent\:false,\parentId\:\55a93a78d25b4aaa96adc547d6bcd444\,\title\:\Machine learning methods for modeling gaze allocation in simple choice behavior and functional neuroimaging data on the level of the individual.\,\b\,null,\ Armin W. \,\Thomas\,\h\,\ColorDefault\,\. Technische Universität Berlin, Berlin. \,\Paper\,\a\,\https://doi.org/10.14279/depositonce-10932\,\h\,\ColorPurple\,\.\,\h\,\ColorDefault\,\updatedAt\:1699831056787,\type\:\text\},\9a8e82cf3ea54e1e9f60db73d2e1f978\:{\id\:\9a8e82cf3ea54e1e9f60db73d2e1f978\,\children\:\f08137c9a31643dea4febff30a7f8d53\,\d87ff87180604280af1e1131a933a1c7\,\d1492851b30c4219b1e27280054a6739\,\5362791ba86d41d99a77fc5a13c15e96\,\hasContent\:true,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\2019\,\h\,\ColorGray\,\updatedAt\:1697730563464,\type\:\text\},\f08137c9a31643dea4febff30a7f8d53\:{\id\:\f08137c9a31643dea4febff30a7f8d53\,\children\:,\hasContent\:false,\parentId\:\9a8e82cf3ea54e1e9f60db73d2e1f978\,\title\:\Gaze bias differences capture individual choice behaviour. \,\b\,null,\Armin W. Thomas*, Felix Molter*, Ian Krajbich, Hauke R. Heekeren, Peter N. Mohr. \,\Nature human behaviour\,\i\,null,\. \,\Paper\,\a\,\http://doi.org/10.1038/s41562-019-0584-8\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/glamlab/gaze-bias-differences\,\.\,\updatedAt\:1699832324759,\type\:\text\},\d87ff87180604280af1e1131a933a1c7\:{\id\:\d87ff87180604280af1e1131a933a1c7\,\children\:,\hasContent\:false,\parentId\:\9a8e82cf3ea54e1e9f60db73d2e1f978\,\title\:\Analyzing Neuroimaging Data Through Recurrent Deep Learning Models.\,\b\,null,\ Armin W. Thomas, Hauke R. Heekeren, Klaus-Robert Müller, Wojciech Samek. \,\Frontiers in Neuroscience\,\i\,null,\. \,\Paper\,\a\,\http://doi.org/10.3389/fnins.2019.01321\,\h\,\ColorPurple\,\.\,\updatedAt\:1699832348662,\type\:\text\},\d1492851b30c4219b1e27280054a6739\:{\id\:\d1492851b30c4219b1e27280054a6739\,\children\:,\hasContent\:false,\parentId\:\9a8e82cf3ea54e1e9f60db73d2e1f978\,\title\:\GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour. \,\b\,null,\Felix Molter*, Armin W. Thomas*, Hauke R. Heekeren, Peter N. Mohr. \,\PloS one\,\i\,null,\. \,\Paper\,\a\,\http://doi.org/10.1371/journal.pone.0226428\,\h\,\ColorPurple\,\ \,\h\,\ColorPurple\,\Code\,\h\,\ColorPurple\,\a\,\https://github.com/glamlab/glambox\,\.\,\updatedAt\:1699832349907,\type\:\text\},\5362791ba86d41d99a77fc5a13c15e96\:{\id\:\5362791ba86d41d99a77fc5a13c15e96\,\children\:,\hasContent\:false,\parentId\:\9a8e82cf3ea54e1e9f60db73d2e1f978\,\title\:\Deep transfer learning for whole-brain FMRI analyses. \,\b\,null,\Armin W. Thomas, KLaus-Robert Müller, Wojciech Samek. \,\Machine Learning in Clinical Neuroimaging Workshop at MICCAI 2019\,\i\,null,\. \,\Paper\,\a\,\http://doi.org/10.1007/978-3-030-32695-1_7\,\h\,\ColorPurple\,\.\,\updatedAt\:1699832356602,\type\:\text\},\3c74d02689744cfbbbd998a5d9d89247\:{\id\:\3c74d02689744cfbbbd998a5d9d89247\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:null,\updatedAt\:1697731561468,\type\:\divider\},\d12131f037174f3cab0f8f92f351e1bc\:{\id\:\d12131f037174f3cab0f8f92f351e1bc\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Code\,\updatedAt\:1697731250116,\type\:\heading\,\subType\:\sub_header\,\depth\:2},\8c0a426053384e37a39a5ac551ad4da2\:{\id\:\8c0a426053384e37a39a5ac551ad4da2\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\I believe in open science and therefore put strong emphasis on open sourcing all code and data used for my research and teaching. Find some examples of open source projects below:\,\updatedAt\:1697739596900,\type\:\text\},\512e3e6d037e4cc9a81391d062608a84\:{\id\:\512e3e6d037e4cc9a81391d062608a84\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Research:\,\b\,null,\updatedAt\:1673119192913,\type\:\text\},\8208172dec27480a8deeae606b29debc\:{\id\:\8208172dec27480a8deeae606b29debc\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\MAD: Mechanistic Architecture Design to build improved AI architectures\,\a\,\https://github.com/athms/mad-lab\,\updatedAt\:1711712021805,\type\:\bulleted_list\},\e2a843921a244caebc159a246c780327\:{\id\:\e2a843921a244caebc159a246c780327\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Evo: DNA foundation modeling from molecular to genome scale\,\a\,\https://github.com/evo-design/evo\,\updatedAt\:1709303144110,\type\:\bulleted_list\},\ad4678d10eb840d3af17c19ff1be4987\:{\id\:\ad4678d10eb840d3af17c19ff1be4987\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Self-supervised learning from brains\,\a\,\https://github.com/athms/learning-from-brains\,\updatedAt\:1697812686209,\type\:\bulleted_list\},\196d5791284c42a7b3c561e98224672b\:{\id\:\196d5791284c42a7b3c561e98224672b\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\HyenaDNA: long-context genomic models\,\a\,\https://github.com/HazyResearch/hyena-dna\,\updatedAt\:1709303163731,\type\:\bulleted_list\},\6d7feb948e024420a12ef572d8362152\:{\id\:\6d7feb948e024420a12ef572d8362152\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Benchmarking explanation methods for neuroscience\,\a\,\https://github.com/athms/xai-brain-decoding-benchmark\,\updatedAt\:1697812720249,\type\:\bulleted_list\},\16560de9e0a745d291457e2f13f580e5\:{\id\:\16560de9e0a745d291457e2f13f580e5\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Evaluating deep transfer learning for neuroimaging\,\a\,\https://github.com/athms/evaluating-deeplight-transfer/\,\updatedAt\:1697812766101,\type\:\bulleted_list\},\ff6e908ee8a240f2a4a94a7fc63ead7b\:{\id\:\ff6e908ee8a240f2a4a94a7fc63ead7b\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Modeling individual choice behavior\,\a\,\https://github.com/athms/gaze-bias-differences\,\updatedAt\:1697812791933,\type\:\bulleted_list\},\3b5154d4268848808384454a33b0cccb\:{\id\:\3b5154d4268848808384454a33b0cccb\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\A python toolbox for the gaze-weighted linear accumulator model (GLAM)\,\a\,\https://github.com/glamlab/glambox\,\updatedAt\:1697812803227,\type\:\bulleted_list\},\bedbb4314ec3446f90d95d2a45385ee6\:{\id\:\bedbb4314ec3446f90d95d2a45385ee6\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Modeling individual choice behavior from many alternatives\,\a\,\https://github.com/athms/many-item-choice\,\updatedAt\:1697812835887,\type\:\bulleted_list\},\ce225d3b5f6e48e3ad5f7c4283e4b460\:{\id\:\ce225d3b5f6e48e3ad5f7c4283e4b460\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Teaching \u0026 Tutorials:\,\b\,null,\updatedAt\:1673220960000,\type\:\text\},\72188f55b1c548f4b4a6e269d1f94a5d\:{\id\:\72188f55b1c548f4b4a6e269d1f94a5d\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Introduction to deep learning basics\,\a\,\https://github.com/athms/deep-learning-basics\,\updatedAt\:1697812858135,\type\:\bulleted_list\},\105f4bb8f404413ebf4c958720ccc1c0\:{\id\:\105f4bb8f404413ebf4c958720ccc1c0\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Introduction to reproducible computational modeling\,\a\,\https://github.com/athms/reproducible-modelling\,\updatedAt\:1697812889417,\type\:\bulleted_list\},\7fc24fb1c95f4f40a80190e91e41cdb0\:{\id\:\7fc24fb1c95f4f40a80190e91e41cdb0\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:null,\updatedAt\:1697731587720,\type\:\divider\},\a73301b97d284f4caf05d665b5713a0b\:{\id\:\a73301b97d284f4caf05d665b5713a0b\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\Thanks to my mentors 🙏🏻 \,\updatedAt\:1697733570382,\type\:\heading\,\subType\:\sub_sub_header\,\depth\:3},\ad4bfd4023f64af1bc5666caad92e28d\:{\id\:\ad4bfd4023f64af1bc5666caad92e28d\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\I’ve been very fortunate to have been advised by brilliant mentors, among them \,\Russell A. Poldrack\,\a\,\https://profiles.stanford.edu/russell-poldrack\,\ (Stanford), \,\Christopher Ré\,\a\,\https://cs.stanford.edu/~chrismre/\,\ (Stanford), \,\Hauke R. Heekeren\,\a\,\https://www.uni-hamburg.de/en/uhh/organisation/praesidium/praesident.html\,\ (Hamburg University), \,\Antonio Rangel\,\a\,\https://www.hss.caltech.edu/people/antonio-rangel\,\ (Caltech), \,\Klaus-Robert Müller\,\a\,\https://scholar.google.com/citations?userjplQac8AAAAJ\u0026hlen\u0026oiao\,\ (TU Berlin), \,\Ian Krajbich\,\a\,\https://psychology.osu.edu/people/krajbich.1\,\ (UCLA), and \,\Ulman Lindenberger\,\a\,\https://www.mpib-berlin.mpg.de/staff/ulman-lindenberger\,\ (Max Planck). I’m grateful to them for their advice and support through the years.\,\updatedAt\:1698620244590,\type\:\text\},\4a239082c6e84ecba9dca6bf74c9bc20\:{\id\:\4a239082c6e84ecba9dca6bf74c9bc20\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:null,\updatedAt\:1697739626155,\type\:\divider\},\020a78a2faf54620bc85a99036cf8d8c\:{\id\:\020a78a2faf54620bc85a99036cf8d8c\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:\📩\,\ gmail: athms.research\,\h\,\ColorDefault\,\updatedAt\:1697901972705,\type\:\text\},\2d125284b3744badb7f9cd246c414ce4\:{\id\:\2d125284b3744badb7f9cd246c414ce4\,\children\:,\hasContent\:false,\parentId\:\1b182ae509214845be8136265dd800eb\,\title\:,\updatedAt\:1697739628382,\type\:\text\}},\entity\:{\8a1cf299-c977-45b3-8170-718e6fb9b0ea\:{\id\:\8a1cf299-c977-45b3-8170-718e6fb9b0ea\,\name\:\Armin 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computational neuroscience and biology together with \},\$\,\$L28\,\https://poldrack.github.io\,{\className\:\link\,\uri\:\https://poldrack.github.io\,\children\:\$\,\$29\,\7-Russell A. Poldrack\,{\children\:\Russell A. Poldrack\}},\$\,\$29\,\8- and \,{\children\:\ and \},\$\,\$L28\,\https://cs.stanford.edu/~chrismre/\,{\className\:\link\,\uri\:\https://cs.stanford.edu/~chrismre/\,\children\:\$\,\$29\,\9-Christopher Ré\,{\children\:\Christopher Ré\}},\$\,\$29\,\10-. At Stanford, I am also affiliated with \,{\children\:\. At Stanford, I am also affiliated with \},\$\,\$L28\,\https://datascience.stanford.edu/\,{\className\:\link\,\uri\:\https://datascience.stanford.edu/\,\children\:\$\,\$29\,\11-Stanford Data Science\,{\children\:\Stanford Data Science\}},\$\,\$29\,\12- and Stanfords Centers \,{\children\:\ and Stanfords Centers \},\$\,\$L28\,\https://crfm.stanford.edu/\,{\className\:\link\,\uri\:\https://crfm.stanford.edu/\,\children\:\$\,\$29\,\13-for Research on Foundation Models\,{\children\:\for Research on Foundation Models\}},\$\,\$29\,\14- and \,{\children\:\ and \},\$\,\$L28\,\https://datascience.stanford.edu/cores\,{\className\:\link\,\uri\:\https://datascience.stanford.edu/cores\,\children\:\$\,\$29\,\15-for Open and Reproducible Science\,{\children\:\for Open and Reproducible Science\}},\$\,\$29\,\16-.\,{\children\:\.\},\$undefined\},\$\,\p\,null,{\id\:\block-1062fe8f92bf457ebf88ea338585295f\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-Prior to Stanford, I was a research fellow and mentor of the \,{\children\:\Prior to Stanford, I was a research fellow and mentor of the \},\$\,\$L28\,\https://cognition.maxplanckschools.org/en\,{\className\:\link\,\uri\:\https://cognition.maxplanckschools.org/en\,\children\:\$\,\$29\,\1-Max Planck School of Cognition\,{\children\:\Max Planck School of Cognition\}},\$\,\$29\,\2-, completed a PhD in AI/ML at Technical University of Berlin in the group of\,{\children\:\, completed a PhD in AI/ML at Technical University of Berlin in the group of\},\$\,\span\,null,{\className\:\highlighted-color color-gray\,\children\:\$\,\$29\,\3- \,{\children\:\ \}},\$\,\$L28\,\https://scholar.google.com/citations?userjplQac8AAAAJ\u0026hlen\u0026oiao\,{\className\:\link\,\uri\:\https://scholar.google.com/citations?userjplQac8AAAAJ\u0026hlen\u0026oiao\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\4-Klaus-Robert Müller\,{\children\:\Klaus-Robert Müller\}}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\5-, and worked as a \,{\children\:\, and worked as a \}},\$\,\$29\,\6-research scientist in \,{\children\:\research scientist in \},\$\,\$L28\,\https://www.hss.caltech.edu/people/antonio-rangel\,{\className\:\link\,\uri\:\https://www.hss.caltech.edu/people/antonio-rangel\,\children\:\$\,\$29\,\7-Antonio Rangel\,{\children\:\Antonio Rangel\}},\$\,\$29\,\8-’s Neuroeconomics laboratory at Caltech (in collaboration with Google ATAP).\,{\children\:\’s Neuroeconomics laboratory at Caltech (in collaboration with Google ATAP).\},\$undefined\},\$\,\div\,null,{\id\:\block-c46463ac56ee4d17af327f84d65db2af\,\className\:\notion-text\,\children\:\$\,\p\,null,{\className\:\notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-My research is focused on diverse research problems in between AI and computational neuroscience. Some of my recent research focuses are…\,{\children\:\My research is focused on diverse research problems in between AI and computational neuroscience. Some of my recent research focuses are…\},\$undefined\},\$\,\div\,null,{\className\:\notion-text__children\,\children\:\$\,\p\,null,{\id\:\block-6664b8efd66149e985f8831e7a281849\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-…training AI systems at scale (e.g., \,{\children\:\…training AI systems at scale (e.g., \},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\1-models trained on large-scale brain data \,{\children\:\models trained on large-scale brain data \}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://arxiv.org/abs/2206.11417\,{\className\:\link\,\uri\:\https://arxiv.org/abs/2206.11417\,\children\:\$\,\$29\,\2-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\3-; genomic foundation models such as Evo (7B parameters) \,{\children\:\; genomic foundation models such as Evo (7B parameters) \}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://www.biorxiv.org/content/10.1101/2024.02.27.582234v1\,{\className\:\link\,\uri\:\https://www.biorxiv.org/content/10.1101/2024.02.27.582234v1\,\children\:\$\,\$29\,\4-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\5- and HyenaDNA (1M context)\,{\children\:\ and HyenaDNA (1M context)\}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\6- \,{\children\:\ \}},\$\,\$L28\,\https://arxiv.org/abs/2306.15794\,{\className\:\link\,\uri\:\https://arxiv.org/abs/2306.15794\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\7-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\8-;\,{\children\:\;\}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\9- \,{\children\:\ \}},\$\,\$29\,\10-language models with up to 7B parameters \,{\children\:\language models with up to 7B parameters \},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://arxiv.org/abs/2403.17844\,{\className\:\link\,\uri\:\https://arxiv.org/abs/2403.17844\,\children\:\$\,\$29\,\11-Paper\,{\children\:\Paper\}}},\$\,\$29\,\12- \,{\children\:\ \},\$\,\$L28\,\https://arxiv.org/abs/2212.14052\,{\className\:\link\,\uri\:\https://arxiv.org/abs/2212.14052\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\13-Paper\,{\children\:\Paper\}}},\$\,\$29\,\14-)\,{\children\:\)\},\$undefined\},\$\,\p\,null,{\id\:\block-e7b1c1f35f3e4a54bfdbf7eda59a4ad8\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-…advancing the ability of AI systems to learn from long sequences (e.g., \,{\children\:\…advancing the 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color-purple\,\children\:\$\,\$29\,\6-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\7-,\,{\children\:\,\}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\8- \,{\children\:\ \}},\$\,\$L28\,\https://arxiv.org/abs/2306.15794\,{\className\:\link\,\uri\:\https://arxiv.org/abs/2306.15794\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\9-Paper\,{\children\:\Paper\}}},\$\,\$29\,\10-)\,{\children\:\)\},\$undefined\},\$\,\p\,null,{\id\:\block-a3c1a6b453c74416a86068edfc7e182f\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-…developing and evaluating explainable AI tools for neuroscience research (e.g.,\,{\children\:\…developing and evaluating explainable AI tools for neuroscience research (e.g.,\},\$\,\span\,null,{\className\:\highlighted-color color-pink\,\children\:\$\,\$L28\,\https://arxiv.org/abs/2205.15581\,{\className\:\link\,\uri\:\https://arxiv.org/abs/2205.15581\,\children\:\$\,\$29\,\1- \,{\children\:\ \}}},\$\,\$L28\,\https://www.sciencedirect.com/science/article/pii/S1053811923002550?via%3Dihub\,{\className\:\link\,\uri\:\https://www.sciencedirect.com/science/article/pii/S1053811923002550?via%3Dihub\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\2-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\3-,\,{\children\:\,\}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\4- \,{\children\:\ \}},\$\,\$L28\,\https://www.sciencedirect.com/science/article/abs/pii/S1364661322001607\,{\className\:\link\,\uri\:\https://www.sciencedirect.com/science/article/abs/pii/S1364661322001607\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\5-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\6-,\,{\children\:\,\}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\7- \,{\children\:\ \}},\$\,\$L28\,\https://www.frontiersin.org/articles/10.3389/fnins.2019.01321/full\,{\className\:\link\,\uri\:\https://www.frontiersin.org/articles/10.3389/fnins.2019.01321/full\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\8-Paper\,{\children\:\Paper\}}},\$\,\$29\,\9-)\,{\children\:\)\},\$undefined\},\$\,\p\,null,{\id\:\block-4f2c466a608946779e16098fb2fd19f8\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-…building computational models to better understand the algorithms underlying human choice behavior (e.g., \,{\children\:\…building computational models to better understand the algorithms underlying 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\}},\$\,\$L28\,\https://journals.plos.org/ploscompbiol/article?id10.1371/journal.pcbi.1010283\,{\className\:\link\,\uri\:\https://journals.plos.org/ploscompbiol/article?id10.1371/journal.pcbi.1010283\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\6-Paper\,{\children\:\Paper\}}},\$\,\$29\,\7-)\,{\children\:\)\},\$undefined\}}},\$\,\div\,null,{\id\:\block-f4844afe31054ba6aeb28683debdb14f\,\className\:\notion-divider\},\$\,\span\,null,{\className\:\notion-heading__anchor\,\id\:\86aa149fc9384179ad51a7e58aa70839\},\$\,\h2\,null,{\id\:\block-86aa149fc9384179ad51a7e58aa70839\,\className\:\notion-heading notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Papers\,{\children\:\Papers\}},\$undefined\},\$\,\p\,null,{\id\:\block-e11a8eb5707a4100b205023e59f716dd\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-* indicates equal contribution\,{\children\:\* indicates equal contribution\},\$undefined\},\$\,\div\,null,{\id\:\block-78555d78168243e48ece02c670b46376\,\className\:\notion-text\,\children\:\$\,\p\,null,{\className\:\notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\span\,null,{\className\:\highlighted-color color-gray\,\children\:\$\,\$29\,\0-2024\,{\children\:\2024\}},\$undefined\},\$\,\div\,null,{\className\:\notion-text__children\,\children\:\$\,\p\,null,{\id\:\block-38dcd69fa7f34acb8ad8b079cf9c4115\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Mechanistic Design and Scaling of Hybrid Architectures. \,{\children\:\Mechanistic Design and Scaling of Hybrid Architectures. \}},\$\,\$29\,\1-Michael Poli*, Armin W. Thomas*, Eric Nguyen*, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli. ArXiv. \,{\children\:\Michael Poli*, Armin W. Thomas*, Eric Nguyen*, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Ré, Ce Zhang, Stefano Massaroli. ArXiv. \},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://arxiv.org/abs/2403.17844\,{\className\:\link\,\uri\:\https://arxiv.org/abs/2403.17844\,\children\:\$\,\$29\,\2-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\3- \,{\children\:\ \}},\$\,\$L28\,\https://github.com/athms/mad-lab\,{\className\:\link\,\uri\:\https://github.com/athms/mad-lab\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\4-Code\,{\children\:\Code\}}},\$undefined\},\$\,\p\,null,{\id\:\block-b995d879aa0e40d98aa4707617ecd597\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Sequence modeling and design from molecular to genome scale with Evo.\,{\children\:\Sequence modeling and design from molecular to genome scale with Evo.\}},\$\,\$29\,\1- Eric Nguyen*, Michael Poli*, Matthew G. Durrant*, Armin W. Thomas, Brian Kang, Jeremy Sullivan, Madelena Y. Ng, Ashley Lewis, Aman Patel, Aaron Lou, Stefano Ermon, Stephen A. Baccus, Tina Hernandez-Boussard, Christopher Ré, Patrick D. Hsu*, Brian L Hie*. bioRxiv. \,{\children\:\ Eric Nguyen*, Michael Poli*, Matthew G. Durrant*, Armin W. Thomas, Brian Kang, Jeremy Sullivan, Madelena Y. Ng, Ashley Lewis, Aman Patel, Aaron Lou, Stefano Ermon, Stephen A. Baccus, Tina Hernandez-Boussard, Christopher Ré, Patrick D. Hsu*, Brian L Hie*. bioRxiv. \},\$\,\$L28\,\https://www.biorxiv.org/content/10.1101/2024.02.27.582234v1\,{\className\:\link\,\uri\:\https://www.biorxiv.org/content/10.1101/2024.02.27.582234v1\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\2-Paper\,{\children\:\Paper\}}},\$\,\$L28\,\https://github.com/HazyResearch/m2\,{\className\:\link\,\uri\:\https://github.com/HazyResearch/m2\,\children\:\$\,\$29\,\3- \,{\children\:\ \}},\$\,\$L28\,\https://github.com/evo-design/evo\,{\className\:\link\,\uri\:\https://github.com/evo-design/evo\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\4-Code\,{\children\:\Code\}}},\$\,\$L28\,\https://github.com/HazyResearch/m2\,{\className\:\link\,\uri\:\https://github.com/HazyResearch/m2\,\children\:\$\,\$29\,\5-.\,{\children\:\.\}},\$undefined\}}},\$\,\div\,null,{\id\:\block-309cf1ee05de4d9482350902a0100237\,\className\:\notion-text\,\children\:\$\,\p\,null,{\className\:\notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\span\,null,{\className\:\highlighted-color color-gray\,\children\:\$\,\$29\,\0-2023\,{\children\:\2023\}},\$undefined\},\$\,\div\,null,{\className\:\notion-text__children\,\children\:\$\,\p\,null,{\id\:\block-922dbb63cc76477bb8fc925219e100a5\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture.\,{\children\:\Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture.\}},\$\,\$29\,\1- Daniel Y. Fu, Simran Arora*, Jessica Grogan*, Isys Johnson*, Sabri Eyuboglu*, Armin W. Thomas*, Benjamin Spector, Michael Poli, Atri Rudra, Christopher Ré. \,{\children\:\ Daniel Y. Fu, Simran Arora*, Jessica Grogan*, Isys Johnson*, Sabri Eyuboglu*, Armin W. Thomas*, Benjamin Spector, Michael Poli, Atri Rudra, Christopher Ré. \},\$\,\em\,\0-i\,{\children\:\$\,\$29\,\2-Advances in Neural Information Processing Systems (NeurIPS)\,{\children\:\Advances in Neural Information Processing Systems (NeurIPS)\}},\$\,\$29\,\3-. \,{\children\:\. \},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://arxiv.org/abs/2310.12109\,{\className\:\link\,\uri\:\https://arxiv.org/abs/2310.12109\,\children\:\$\,\$29\,\4-Paper\,{\children\:\Paper\}}},\$\,\$L28\,\https://github.com/HazyResearch/m2\,{\className\:\link\,\uri\:\https://github.com/HazyResearch/m2\,\children\:\$\,\$29\,\5- \,{\children\:\ \}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://github.com/HazyResearch/m2\,{\className\:\link\,\uri\:\https://github.com/HazyResearch/m2\,\children\:\$\,\$29\,\6-Code\,{\children\:\Code\}}},\$\,\$L28\,\https://github.com/HazyResearch/m2\,{\className\:\link\,\uri\:\https://github.com/HazyResearch/m2\,\children\:\$\,\$29\,\7-.\,{\children\:\.\}},\$\,\$29\,\8- \,{\children\:\ \},\$\,\span\,null,{\className\:\highlighted-color color-orange\,\children\:\$\,\$29\,\9-Oral\,{\children\:\Oral\}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\10-.\,{\children\:\.\}},\$undefined\},\$\,\p\,null,{\id\:\block-3c3c2f0f4ef745b8b4ca36af6daa461a\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. \,{\children\:\HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. \}},\$\,\$29\,\1-Eric Nguyen*, Michael Poli*, Marjan Faizi*, Armin W. Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Clayton Rabideau, Stefano Massaroli, Yoshua Bengio, Stefano Ermon, Stephen A. Baccus, Chris Ré. \,{\children\:\Eric Nguyen*, Michael Poli*, Marjan Faizi*, Armin W. Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Clayton Rabideau, Stefano Massaroli, Yoshua Bengio, Stefano Ermon, Stephen A. Baccus, Chris Ré. \},\$\,\em\,\0-i\,{\children\:\$\,\$29\,\2-Advances in Neural Information Processing Systems (NeurIPS)\,{\children\:\Advances in Neural Information Processing Systems (NeurIPS)\}},\$\,\$29\,\3-. \,{\children\:\. \},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://arxiv.org/abs/2306.15794\,{\className\:\link\,\uri\:\https://arxiv.org/abs/2306.15794\,\children\:\$\,\$29\,\4-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\5- \,{\children\:\ \}},\$\,\$L28\,\https://github.com/HazyResearch/hyena-dna\,{\className\:\link\,\uri\:\https://github.com/HazyResearch/hyena-dna\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\6-Code\,{\children\:\Code\}}},\$\,\$29\,\7-. \,{\children\:\. \},\$\,\span\,null,{\className\:\highlighted-color color-orange\,\children\:\$\,\$29\,\8-Spotlight\,{\children\:\Spotlight\}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\9-.\,{\children\:\.\}},\$undefined\},\$\,\p\,null,{\id\:\block-bdf9f4766064447694b3f2e3cfe54809\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Benchmarking explanation methods for mental state decoding with deep learning models.\,{\children\:\Benchmarking explanation methods for mental state decoding with deep learning models.\}},\$\,\$29\,\1- \,{\children\:\ \},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\2-Armin W. Thomas, Christopher \,{\children\:\Armin W. Thomas, Christopher \}},\$\,\$29\,\3-Ré, Russell A. Poldrack. \,{\children\:\Ré, Russell A. Poldrack. \},\$\,\em\,\0-i\,{\children\:\$\,\$29\,\4-NeuroImage\,{\children\:\NeuroImage\}},\$\,\$29\,\5-. \,{\children\:\. \},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://doi.org/10.1016/j.neuroimage.2023.120109\,{\className\:\link\,\uri\:\https://doi.org/10.1016/j.neuroimage.2023.120109\,\children\:\$\,\$29\,\6-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\7- \,{\children\:\ \}},\$\,\$L28\,\https://github.com/athms/xai-brain-decoding-benchmark\,{\className\:\link\,\uri\:\https://github.com/athms/xai-brain-decoding-benchmark\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\8-Code\,{\children\:\Code\}}},\$undefined\},\$\,\p\,null,{\id\:\block-fa8fd094cb08499fb109af847c7ff651\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Evaluating deep transfer learning for whole-brain cognitive decoding.\,{\children\:\Evaluating deep transfer learning for whole-brain cognitive decoding.\}},\$\,\$29\,\1- Armin W. \,{\children\:\ Armin W. \},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\2-Thomas,\,{\children\:\Thomas,\}},\$\,\$29\,\3- Ullman Lindenberger, Wojciech Samek, Klaus-Robert Müller. \,{\children\:\ Ullman Lindenberger, Wojciech Samek, Klaus-Robert Müller. \},\$\,\em\,\0-i\,{\children\:\$\,\$29\,\4-Journal of the Franklin Institute\,{\children\:\Journal of the Franklin Institute\}},\$\,\$29\,\5-. \,{\children\:\. \},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://doi.org/10.1016/j.jfranklin.2023.07.015\,{\className\:\link\,\uri\:\https://doi.org/10.1016/j.jfranklin.2023.07.015\,\children\:\$\,\$29\,\6-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\7- \,{\children\:\ \}},\$\,\$L28\,\https://github.com/athms/evaluating-deeplight-transfer\,{\className\:\link\,\uri\:\https://github.com/athms/evaluating-deeplight-transfer\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\8-Code\,{\children\:\Code\}}},\$\,\$29\,\9-.\,{\children\:\.\},\$undefined\},\$\,\p\,null,{\id\:\block-2556f4e245d84acf8ab21f285cde150f\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Simple Hardware-Efficient Long Convolutions for Sequence Modeling.\,{\children\:\Simple Hardware-Efficient Long Convolutions for Sequence Modeling.\}},\$\,\$29\,\1- Daniel Y. Fu*, Elliot Epstein*, Eric Nguyen, Armin W. \,{\children\:\ Daniel Y. Fu*, Elliot Epstein*, Eric Nguyen, Armin W. \},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\2-Thomas, Michael \,{\children\:\Thomas, Michael \}},\$\,\$29\,\3-Zhang, Tri Dao, Atri Rudra, Christopher Ré. \,{\children\:\Zhang, Tri Dao, Atri Rudra, Christopher Ré. \},\$\,\em\,\0-i\,{\children\:\$\,\$29\,\4-International Conference on Machine Learning (ICML). \,{\children\:\International Conference on Machine Learning (ICML). \}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://proceedings.mlr.press/v202/fu23a\,{\className\:\link\,\uri\:\https://proceedings.mlr.press/v202/fu23a\,\children\:\$\,\$29\,\5-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\6- \,{\children\:\ \}},\$\,\$L28\,\https://github.com/HazyResearch/safari\,{\className\:\link\,\uri\:\https://github.com/HazyResearch/safari\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\7-Code\,{\children\:\Code\}}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\8-.\,{\children\:\.\}},\$undefined\},\$\,\p\,null,{\id\:\block-d3429ea000f6480d82c133a61ef557cb\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Hungry Hungry Hippos: Towards Language Modeling with State Space Models. \,{\children\:\Hungry Hungry Hippos: Towards Language Modeling with State Space Models. \}},\$\,\$29\,\1-Tri Dao*, Daniel Y. Fu,* Khaled K. Saab,\,{\children\:\Tri Dao*, Daniel Y. Fu,* Khaled K. Saab,\},\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\2- \,{\children\:\ \}},\$\,\$29\,\3-Armin W. \,{\children\:\Armin W. \},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\4-Thomas, Atri \,{\children\:\Thomas, Atri \}},\$\,\$29\,\5-Rudra, Christopher Ré. \,{\children\:\Rudra, Christopher Ré. \},\$\,\em\,\0-i\,{\children\:\$\,\$29\,\6-International Conference on Learning Representations (ICLR)\,{\children\:\International Conference on Learning Representations (ICLR)\}},\$\,\$29\,\7-. Preprint: \,{\children\:\. 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Thomas, Felix Molter, Ian Krajbich. \,{\children\:\Armin W. Thomas, Felix Molter, Ian Krajbich. \},\$\,\em\,\0-i\,{\children\:\$\,\$29\,\2-Elife\,{\children\:\Elife\}},\$\,\$29\,\3-. \,{\children\:\. \},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://doi.org/10.7554/eLife.57012\,{\className\:\link\,\uri\:\https://doi.org/10.7554/eLife.57012\,\children\:\$\,\$29\,\4-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\5- \,{\children\:\ \}},\$\,\$L28\,\https://github.com/athms/many-item-choice\,{\className\:\link\,\uri\:\https://github.com/athms/many-item-choice\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\6-Code\,{\children\:\Code\}}},\$\,\$29\,\7-.\,{\children\:\.\},\$undefined\}}},\$\,\div\,null,{\id\:\block-55a93a78d25b4aaa96adc547d6bcd444\,\className\:\notion-text\,\children\:\$\,\p\,null,{\className\:\notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\span\,null,{\className\:\highlighted-color color-gray\,\children\:\$\,\$29\,\0-2020\,{\children\:\2020\}},\$undefined\},\$\,\div\,null,{\className\:\notion-text__children\,\children\:\$\,\p\,null,{\id\:\block-3bbd4d3c049842fdb715fdc95e190f82\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Machine learning methods for modeling gaze allocation in simple choice behavior and functional neuroimaging data on the level of the individual.\,{\children\:\Machine learning methods for modeling gaze allocation in simple choice behavior and functional neuroimaging data on the level of the individual.\}},\$\,\$29\,\1- Armin W. \,{\children\:\ Armin W. \},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\2-Thomas\,{\children\:\Thomas\}},\$\,\$29\,\3-. Technische Universität Berlin, Berlin. \,{\children\:\. Technische Universität Berlin, Berlin. \},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\https://doi.org/10.14279/depositonce-10932\,{\className\:\link\,\uri\:\https://doi.org/10.14279/depositonce-10932\,\children\:\$\,\$29\,\4-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\5-.\,{\children\:\.\}},\$undefined\}}},\$\,\div\,null,{\id\:\block-9a8e82cf3ea54e1e9f60db73d2e1f978\,\className\:\notion-text\,\children\:\$\,\p\,null,{\className\:\notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\span\,null,{\className\:\highlighted-color color-gray\,\children\:\$\,\$29\,\0-2019\,{\children\:\2019\}},\$undefined\},\$\,\div\,null,{\className\:\notion-text__children\,\children\:\$\,\p\,null,{\id\:\block-f08137c9a31643dea4febff30a7f8d53\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Gaze bias differences capture individual choice behaviour. \,{\children\:\Gaze bias differences capture individual choice behaviour. \}},\$\,\$29\,\1-Armin W. 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Mohr. \},\$\,\em\,\0-i\,{\children\:\$\,\$29\,\2-Nature human behaviour\,{\children\:\Nature human behaviour\}},\$\,\$29\,\3-. \,{\children\:\. \},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$L28\,\http://doi.org/10.1038/s41562-019-0584-8\,{\className\:\link\,\uri\:\http://doi.org/10.1038/s41562-019-0584-8\,\children\:\$\,\$29\,\4-Paper\,{\children\:\Paper\}}},\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\5- \,{\children\:\ \}},\$\,\$L28\,\https://github.com/glamlab/gaze-bias-differences\,{\className\:\link\,\uri\:\https://github.com/glamlab/gaze-bias-differences\,\children\:\$\,\span\,null,{\className\:\highlighted-color color-purple\,\children\:\$\,\$29\,\6-Code\,{\children\:\Code\}}},\$\,\$29\,\7-.\,{\children\:\.\},\$undefined\},\$\,\p\,null,{\id\:\block-d87ff87180604280af1e1131a933a1c7\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Analyzing Neuroimaging Data Through Recurrent Deep Learning Models.\,{\children\:\Analyzing Neuroimaging Data Through Recurrent Deep Learning Models.\}},\$\,\$29\,\1- Armin W. 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Find some examples of open source projects below:\},\$undefined\},\$\,\p\,null,{\id\:\block-512e3e6d037e4cc9a81391d062608a84\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Research:\,{\children\:\Research:\}},\$undefined\},\$\,\ul\,null,{\className\:\notion-bulleted-list\,\children\:\$\,\li\,null,{\id\:\block-8208172dec27480a8deeae606b29debc\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/athms/mad-lab\,{\className\:\link\,\uri\:\https://github.com/athms/mad-lab\,\children\:\$\,\$29\,\0-MAD: Mechanistic Architecture Design to build improved AI architectures\,{\children\:\MAD: Mechanistic Architecture Design to build improved AI architectures\}},\$undefined\},\$\,\li\,null,{\id\:\block-e2a843921a244caebc159a246c780327\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/evo-design/evo\,{\className\:\link\,\uri\:\https://github.com/evo-design/evo\,\children\:\$\,\$29\,\0-Evo: DNA foundation modeling from molecular to genome scale\,{\children\:\Evo: DNA foundation modeling from molecular to genome scale\}},\$undefined\},\$\,\li\,null,{\id\:\block-ad4678d10eb840d3af17c19ff1be4987\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/athms/learning-from-brains\,{\className\:\link\,\uri\:\https://github.com/athms/learning-from-brains\,\children\:\$\,\$29\,\0-Self-supervised learning from brains\,{\children\:\Self-supervised learning from brains\}},\$undefined\},\$\,\li\,null,{\id\:\block-196d5791284c42a7b3c561e98224672b\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/HazyResearch/hyena-dna\,{\className\:\link\,\uri\:\https://github.com/HazyResearch/hyena-dna\,\children\:\$\,\$29\,\0-HyenaDNA: long-context genomic models\,{\children\:\HyenaDNA: long-context genomic models\}},\$undefined\},\$\,\li\,null,{\id\:\block-6d7feb948e024420a12ef572d8362152\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/athms/xai-brain-decoding-benchmark\,{\className\:\link\,\uri\:\https://github.com/athms/xai-brain-decoding-benchmark\,\children\:\$\,\$29\,\0-Benchmarking explanation methods for neuroscience\,{\children\:\Benchmarking explanation methods for neuroscience\}},\$undefined\},\$\,\li\,null,{\id\:\block-16560de9e0a745d291457e2f13f580e5\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/athms/evaluating-deeplight-transfer/\,{\className\:\link\,\uri\:\https://github.com/athms/evaluating-deeplight-transfer/\,\children\:\$\,\$29\,\0-Evaluating deep transfer learning for neuroimaging\,{\children\:\Evaluating deep transfer learning for neuroimaging\}},\$undefined\},\$\,\li\,null,{\id\:\block-ff6e908ee8a240f2a4a94a7fc63ead7b\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/athms/gaze-bias-differences\,{\className\:\link\,\uri\:\https://github.com/athms/gaze-bias-differences\,\children\:\$\,\$29\,\0-Modeling individual choice behavior\,{\children\:\Modeling individual choice behavior\}},\$undefined\},\$\,\li\,null,{\id\:\block-3b5154d4268848808384454a33b0cccb\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/glamlab/glambox\,{\className\:\link\,\uri\:\https://github.com/glamlab/glambox\,\children\:\$\,\$29\,\0-A python toolbox for the gaze-weighted linear accumulator model (GLAM)\,{\children\:\A python toolbox for the gaze-weighted linear accumulator model (GLAM)\}},\$undefined\},\$\,\li\,null,{\id\:\block-bedbb4314ec3446f90d95d2a45385ee6\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/athms/many-item-choice\,{\className\:\link\,\uri\:\https://github.com/athms/many-item-choice\,\children\:\$\,\$29\,\0-Modeling individual choice behavior from many alternatives\,{\children\:\Modeling individual choice behavior from many alternatives\}},\$undefined\}},\$\,\p\,null,{\id\:\block-ce225d3b5f6e48e3ad5f7c4283e4b460\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\strong\,\0-b\,{\children\:\$\,\$29\,\0-Teaching \u0026 Tutorials:\,{\children\:\Teaching \u0026 Tutorials:\}},\$undefined\},\$\,\ul\,null,{\className\:\notion-bulleted-list\,\children\:\$\,\li\,null,{\id\:\block-72188f55b1c548f4b4a6e269d1f94a5d\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/athms/deep-learning-basics\,{\className\:\link\,\uri\:\https://github.com/athms/deep-learning-basics\,\children\:\$\,\$29\,\0-Introduction to deep learning basics\,{\children\:\Introduction to deep learning basics\}},\$undefined\},\$\,\li\,null,{\id\:\block-105f4bb8f404413ebf4c958720ccc1c0\,\className\:\notion-list-item notion-semantic-string\,\children\:\$undefined\,\$\,\$L28\,\https://github.com/athms/reproducible-modelling\,{\className\:\link\,\uri\:\https://github.com/athms/reproducible-modelling\,\children\:\$\,\$29\,\0-Introduction to reproducible computational modeling\,{\children\:\Introduction to reproducible computational modeling\}},\$undefined\}},\$\,\div\,null,{\id\:\block-7fc24fb1c95f4f40a80190e91e41cdb0\,\className\:\notion-divider\},\$\,\span\,null,{\className\:\notion-heading__anchor\,\id\:\a73301b97d284f4caf05d665b5713a0b\},\$\,\h3\,null,{\id\:\block-a73301b97d284f4caf05d665b5713a0b\,\className\:\notion-heading notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-Thanks to my mentors 🙏🏻 \,{\children\:\Thanks to my mentors 🙏🏻 \},\$undefined\},\$\,\p\,null,{\id\:\block-ad4bfd4023f64af1bc5666caad92e28d\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-I’ve been very fortunate to have been advised by brilliant mentors, among them \,{\children\:\I’ve been very fortunate to have been advised by brilliant mentors, among them \},\$\,\$L28\,\https://profiles.stanford.edu/russell-poldrack\,{\className\:\link\,\uri\:\https://profiles.stanford.edu/russell-poldrack\,\children\:\$\,\$29\,\1-Russell A. Poldrack\,{\children\:\Russell A. Poldrack\}},\$\,\$29\,\2- (Stanford), \,{\children\:\ (Stanford), \},\$\,\$L28\,\https://cs.stanford.edu/~chrismre/\,{\className\:\link\,\uri\:\https://cs.stanford.edu/~chrismre/\,\children\:\$\,\$29\,\3-Christopher Ré\,{\children\:\Christopher Ré\}},\$\,\$29\,\4- (Stanford), \,{\children\:\ (Stanford), \},\$\,\$L28\,\https://www.uni-hamburg.de/en/uhh/organisation/praesidium/praesident.html\,{\className\:\link\,\uri\:\https://www.uni-hamburg.de/en/uhh/organisation/praesidium/praesident.html\,\children\:\$\,\$29\,\5-Hauke R. Heekeren\,{\children\:\Hauke R. Heekeren\}},\$\,\$29\,\6- (Hamburg University), \,{\children\:\ (Hamburg University), \},\$\,\$L28\,\https://www.hss.caltech.edu/people/antonio-rangel\,{\className\:\link\,\uri\:\https://www.hss.caltech.edu/people/antonio-rangel\,\children\:\$\,\$29\,\7-Antonio Rangel\,{\children\:\Antonio Rangel\}},\$\,\$29\,\8- (Caltech), \,{\children\:\ (Caltech), \},\$\,\$L28\,\https://scholar.google.com/citations?userjplQac8AAAAJ\u0026hlen\u0026oiao\,{\className\:\link\,\uri\:\https://scholar.google.com/citations?userjplQac8AAAAJ\u0026hlen\u0026oiao\,\children\:\$\,\$29\,\9-Klaus-Robert Müller\,{\children\:\Klaus-Robert Müller\}},\$\,\$29\,\10- (TU Berlin), \,{\children\:\ (TU Berlin), \},\$\,\$L28\,\https://psychology.osu.edu/people/krajbich.1\,{\className\:\link\,\uri\:\https://psychology.osu.edu/people/krajbich.1\,\children\:\$\,\$29\,\11-Ian Krajbich\,{\children\:\Ian Krajbich\}},\$\,\$29\,\12- (UCLA), and \,{\children\:\ (UCLA), and \},\$\,\$L28\,\https://www.mpib-berlin.mpg.de/staff/ulman-lindenberger\,{\className\:\link\,\uri\:\https://www.mpib-berlin.mpg.de/staff/ulman-lindenberger\,\children\:\$\,\$29\,\13-Ulman Lindenberger\,{\children\:\Ulman Lindenberger\}},\$\,\$29\,\14- (Max Planck). I’m grateful to them for their advice and support through the years.\,{\children\:\ (Max Planck). I’m grateful to them for their advice and support through the years.\},\$undefined\},\$\,\div\,null,{\id\:\block-4a239082c6e84ecba9dca6bf74c9bc20\,\className\:\notion-divider\},\$\,\p\,null,{\id\:\block-020a78a2faf54620bc85a99036cf8d8c\,\className\:\notion-text notion-text__content notion-semantic-string\,\children\:\$undefined\,\$\,\$29\,\0-📩\,{\children\:\📩\},\$\,\span\,null,{\className\:\highlighted-color color-default\,\children\:\$\,\$29\,\1- gmail: athms.research\,{\children\:\ gmail: athms.research\}},\$undefined\},\$\,\div\,null,{\id\:\block-2d125284b3744badb7f9cd246c414ce4\,\className\:\notion-text\}}}}\n)/script>script>self.__next_f.push(1,c:\$\,\meta\,\0\,{\name\:\viewport\,\content\:\widthdevice-width, initial-scale1\},\$\,\meta\,\1\,{\charSet\:\utf-8\},\$\,\title\,\2\,{\children\:\Armin Thomas\},\$\,\meta\,\3\,{\name\:\description\,\content\:\LinkedIn Google Scholar GitHub\},\$\,\meta\,\4\,{\name\:\generator\,\content\:\Super\},\$\,\meta\,\5\,{\name\:\robots\,\content\:\index, follow\},\$\,\meta\,\6\,{\name\:\super-project\,\content\:\feat/app-router\},\$\,\meta\,\7\,{\property\:\og:title\,\content\:\Armin Thomas\},\$\,\meta\,\8\,{\property\:\og:description\,\content\:\LinkedIn Google Scholar GitHub\},\$\,\meta\,\9\,{\property\:\og:url\,\content\:\https://athms.me\},\$\,\meta\,\10\,{\property\:\og:site_name\,\content\:\Armin Thomas\},\$\,\meta\,\11\,{\property\:\og:locale\,\content\:\en-US\},\$\,\meta\,\12\,{\property\:\og:type\,\content\:\website\},\$\,\meta\,\13\,{\name\:\twitter:card\,\content\:\summary_large_image\},\$\,\meta\,\14\,{\name\:\twitter:title\,\content\:\Armin Thomas\},\$\,\meta\,\15\,{\name\:\twitter:description\,\content\:\LinkedIn Google Scholar GitHub\}\n4:null\n)/script>/body>/html>
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