Collaborative Machine Learning & Federated Learning
The human brain is a complex network of functionally interconnected regions whose coordinated effort gives rise to different functions. Understanding what these regions are, how they interact, and how this interaction forms a wide range of behavior has long been an essential question for human neuroscience. Neuroimaging techniques have provided a unique opportunity to tackle this question in a data-driven way. Advances in neuroimaging techniques, such as functional Magnetic Resonance Imaging (fMRI), have allowed us to approximately measure the neural activity in the brain. However, fMRI data are not only massive in size but also spatially and temporally complex. One of the research directions in our group is to develop advanced machine-learning techniques to study brain function and its link to behavior.
Relevant Papers
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IEEE Conference on Information Sciences and Systems | 2023 | Stacking multiple optimal transport policies to map functional connectomes | Javid Dadashkarimi, Matthew Rosenblatt, Amin Karbasi, Dustin Scheinost |
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arxiv | 2022 | Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for any atlas when raw data is not available | Javid Dadashkarimi, Amin Karbasi, Qinghao Liang, Matthew Rosenblatt, Stephanie Noble, Maya Foster, Raimundo Rodriguez, Brendan Adkinson, Jean Ye, Huili Sun, Chris Camp, Michael Farruggia, Link Tejavibulya, Wei Dai, Rongtao Jiang, Angeliki Pollatou, and Dustin Scheinost |
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MICCAI | 2022 | Combining multiple atlases to estimate data-driven mappings between functional connectomes using optimal transport | Javid Dadashkarimi, Amin Karbasi, and Dustin Scheinost |
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MICCAI | 2021 | Data-driven mapping between functional connectomes using optimal transport | Javid Dadashkarimi, Amin Karbasi, and Dustin Scheinost |