- 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”, 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, and Nir Shavit, ICLR 2020. This paper has been featured in Andrew Ng’s news, The Batch.
- February 2020: Predicting How Well Neural Networks Will Scale Written by Adam Conner-Simons, MIT CSAIL.
- 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 awarded research funding under the IARPA Machine Intelligence from Cortical Networks (MICrONS) project.