News

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 is selected for Best Paper Nominee. June 3, 2016, MIT Commencement: Congratulations to new graduates, Gregory Odor and Hayk Saribekyan! (Pictured: Hayk Saribekyan and Professor Nir Shavit.) February 2016: The Shavit Lab has been awardedRead More →

Projects

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

Undergraduates

Shraman Ray Chaudhuri
Will Noble

Alumni

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

Publications

Mi, Lu, Wang, Hao, Tian, Yonglong, and Shavit, Nir. Training-Free Uncertainty Estimation for Neural Networks. arXiv: 1910.04858, 2019. Rosenfeld, Jonathan S., Rosenfeld, Amir, Belinkov, Yonatan, and Shavit, Nir. A Constructive Prediction of the Generalization Error Across Scales. arXiv:1909.12673, September 2019. Meirovitch, Yaron, Mi, Lu, Saribekyan, Hayk, Matveev, Alexander, Rolnick, Rolnick, and Shavit, Nir. Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8425-8435, 2019. Witvliet, Daniel, Mulcahy, Ben, Mitchell, James K., Meirovitch,¬† Yaron, Berger, Daniel R., Holmyard, Douglas, Schalek, Richard L., Cook, Steven J., Xian Koh, Wan, Neubauer, Marianna, Rehaluk,Read More →

Resources

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