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”, and am thrilled to see increasing interest across the AI community in cross-disciplinary partnerships for climate action. March 2020: A Constructive Prediction of the Generalization Across Scales by Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov,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 →

Graduate Students

Lu Mi

Jonathan Rosenfeld


Shraman Ray Chaudhuri
Will Noble


David Budden
Jonathan Stoller
Gergely Odor
Victor Jakubiuk
Quan Nguyen
Robert Radway


Witvliet, Daniel, Mulcahy, Ben, Mitchell, James K., Meirovitch, Yaron, Berger, Daniel R., Wu, Yuelong, Liu, Yufang, Koh, Wan Xian, Parvathala, Rajeev, Holmyard, Douglas, Schalek, Richard L., Shavit, Nir, Chisholm, Andrew D., Lichtman, Jeff W., Samuel, Aravinthan D.T., and Zhen, Mei. Connectomes across development reveal principles of brain maturation in C. elegans. Nature, 596, pages 257–261, 2021. Also, bioRxiv 2020.04.30.066209 Rosenfeld, Jonathan S., Frankle, Jonathan, Carbin, Michael, and Shavit, Nir. On the Predictability of Pruning Across Scales. ICML 2021 Poster Session. Also, arXiv:2006.10621, June 2020. Mi, Lu, Wang, Hao, Tian, Yonglong, and Shavit, Nir. Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate. ICMLRead More →


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