March 2020: A Constructive Prediction of the Generalization Across Scales by Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit, ICLR 2020. This paper has been featured in Andrew Ng’s news, The Batch. June 2019: Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics by Yaron Meirovitch, Lu Mi, Hayk Saribekyan, Alexander Matveev, David Rolnick, Nir Shavit; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 8425-8435. March 27, 2018: Blog on “Deep Learning to Study the Brain to Improve Deep Learning” is Live. January 2017: Shavit Lab’s PPoPP 2017 paper, A Multicore Path to Connectomics-on-Demand isRead 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


Mi, Lu, Wang, Hao, Meirovitch, Yaron, Schalek, Richard, Turaga, Srinivas C., Lichtman, Jeff W., Samuel, Aravinthan D. T. and Shavit, Nir. Learning Guided Electron Microscopy with Active Acquisition. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), October 2020. Presentation materials. Rosenfeld, Jonathan S., Frankle, Jonathan, Carbin, Michael, and Shavit, Nir. On the Predictability of Pruning Across Scales. arXiv:2006.10621, June 2020. Rosenfeld, Jonathan S., Rosenfeld, Amir, Belinkov, Yonatan and Shavit, Nir. A Constructive Prediction of the Generalization Across Scales by , ICLR 2020. This paper has been featured in Andrew Ng’s news, The Batch. Witvliet, Daniel, Mulcahy, Ben, Mitchell, James K., Meirovitch,Read More →


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