Computational Connectomics Group

Group member Tony Wang’s research on adversarially exploiting superhuman Go AIs featured in the Financial Times! Lu Mi defended her thesis “Deep Learning Tools for Next-Generation Connectomics” on August 18, 2022. Check out her slides. Coming soon in Nature! “We are delighted to accept your manuscript entitled “Connectomes across development reveal principles of brain maturation” for publication in Nature. Thank you for choosing to publish your interesting work with us.” #worm, #ai, #connectome. To those who are curious about the details, here is a longer preprint format. July 2021: David Rolnick group ex phd named to the MIT Technology Review’s list of “Innovators Under 35”,Read More →

High Throughput Connectomics The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led to believe that this type of horizontal scaling can reduce or even eliminate the need for traditional algorithm development, careful parallelization, and performance engineering. This paper is a case study showing the contrary: that the benefits of algorithms, parallelization, and performance engineering, can sometimes be so vast that it is possible to solve “clusterscale” problems on a single commodity multicore machine. Connectomics is an emerging area of neurobiology that uses cutting edge machine learning and image processingRead More →

Li, Yicong, Meirovitch, Yaron, Kuan, Aaron T., Phelps, Jasper S., Allen Lee, Wei-Chung, Shavit, Nir, Mi, Lu. X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics. To appear in IEEE – ISBI 2023: International Symposium on Biomedical Imaging, April 2023. Nguyen, Tri, Narwani, Mukul, Larson, Mark, Li, Yicong, Xie, Shuhan, Pfister, Hanspeter, Wei, Donglai, Shavit, Nir, Mi, Lu, Pacureanu, Alexandra, Lee, Wei-Chung, Kuan, Aaron T. The XPRESS challenge: Xray Projectomic Reconstruction – Extracting Segmentation with Skeletons. To appear in IEEE – ISBI 2023: International Symposium on Biomedical Imaging, April 2023. Wang, Tony T., Zablotchi, Igor, Shavit, N., Rosenfeld, Jonathan S. Cliff-Learning. arXiv:2302.07348,Read More →

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