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 awarded research funding under the IARPA Machine Intelligence from Cortical Networks (MICrONS) project. October 2015: Brain-like chip.


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 →


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., Holmyard, Douglas, Schalek, Richard L., Cook, Steven J., Xian Koh, Wan, Neubauer, Marianna, Rehaluk, Christine, Wang, ZiTong, Kersen, David, Chisholm, Andrew D., Shavit, Nir, Lichtman, Jeffrey W., Samuel, Aravinthan, and Zhen, Mei.  Invariant, stochastic, and developmentally regulated synapses constitute the C. elegans connectome from isogenic individuals.  Poster Presentation at Cosyne 2019.

Meirovitch, Yaron, Mi, Lu, Saribekyan, Hayk, Matveev, Alexander, Rolnick, David, Wierzynski, Casimir, and Shavit, Nir. Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics. CoRR abs/1812.01157, 2018.

Santurkar, Shibani, Budden, David M., and Shavit, Nir.  Generative Compression. PCS 2018.  Also, CoRR.abs/1703.01467, 2017.

Budden, David, Matveev, Alexander, Santurkar, Shibani,  Chaudhuri, Shraman Ray, and Shavit, Nir.  Deep Tensor Convolution on Multicores.   ICML 2017.  Also, CoRR abs/1611.06565, 2016.

Matveev, A., Meirovitch, Y., Saribekyan, H., Jakubiuk, W., Kaler, T., Odor, G., Budden, D., Zlateski, A., and Shavit, N. A Multicore Path to Connectomics-on-Demand. PPoPP 2017 (Best Paper Nominee).

Rolnick, David, Meirovitch, Yaron, Parag, Toufiq, Pfister, Hanspeter,  Jain, Vien, Lichtman, Jeff W., Boyden, Edward S., and Shavit, Nir. Morphological error detection in 3d segmentationsCoRR.abs/1705.10882, 2017.

Rolnick, David, Veit, Andreas,  Belongie, Serge J., and Shavit, Nir. Deep Learning is Robust to Massive Label Noise. CoRR abs/1705.10694, 2017.

Santurkar, Shibani, Budden, David, Matveev, Alexander, Berlin, Heather, Saribekyan, Hayk, Meirovitch, Yaron, and Shavit, Nir. Toward Streaming Synapse Detection with Compositional ConvNetsCoRR abs/1702.07386, 2017.

Meirovitch, Y., Matveev, A., Saribekyan, H., Budden, D., Rolnick, D., Odor, G., Knowles-Barley, S., Thouis, R., Pfister, H., Lichtman, J., Shavit, N.A Multi-Pass Approach to Large-Scale Connectomics.  CoRR abs/1612.02120, 2016.

Shavit, Nir. A Multicore Path to Connectomics-on-Demand. SPAA 2016.

Lichtman, J., Pfister, H., and Shavit, N. The big data challenges of connectomics. Nature Neuroscience, 17, pp. 1448-1454, November 2014.

Allen-Zhu, Zeyuan, Gelashvili, Rati, Micali, Silvio, and Shavit, Nir.   Sparse sign-consistent Johnson-Lindenstrauss matrices: Compression with neuroscience-based constraints. Proceedings of the National Academy of Sciences USA; 111(47), pp. 16872-16876, October 2014.



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