<p>Talk title: High throughput connectomics: the making of a brain scope</p>

<p>Abstract: Connectomics is an emerging field of neurobiology that uses cutting edge machine learning and image processing to extract brain connectivity graphs from electron microscopy images. It has long been assumed that the processing of connectomics data, and achieving human-like reconstruction accuracy, will require mass storage and farms of CPUs and GPUs and will take months if not years. This talk will discuss the feasibility of designing a high-throughput connectomics-on-demand system that runs on a multicore machine with less than 100 cores and extracts connectomes at the terabyte per hour pace of modern electron microscopes.</p>

<p>Bio: Nir Shavit is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology and a professor in the Computer Science Department at Tel Aviv University. He received B.Sc. and M.Sc. degrees in Computer Science from the Technion - Israel Institute of Technology in 1984 and 1986, and a Ph.D. in Computer Science from the Hebrew University of Jerusalem in 1990. Shavit is a co-author of the book The Art of Multiprocessor Programming, is a winner of the 2004 Gödel Prize in theoretical computer science for his work on applying tools from algebraic topology to model shared memory computability, and a winner of the 2012 Dijkstra Prize for the introduction and first implementation of software transactional memory. He is a recipient of an honorary doctorate from the University of Neuchatel and is an ACM fellow, a past program chair of the ACM Symposium on Principles of Distributed Computing (PODC) and a past program chair of the ACM Symposium on Parallelism in Algorithms and Architectures (SPAA).</p>