For many years his main interests were techniques for designing, implementing, and reasoning about multiprocessor algorithms. These days he is interested in understanding the relationship between deep learning and how neural tissue computes and is part of an effort to do so by extracting connectivity maps of brain, a field called connectomics. Nir is the principal investigator of the Multiprocessor Algorithmics Group and the Computational Connectomics Group.
We develop algorithms, systems and software architectures for automating reconstruction of accurate representations of neural tissue structures, such as nanometer-scale neurons' morphology and synaptic connections in the mammalian cortex.
The goal of the Theory of Computation CoR is to study the fundamental strengths and limits of computation as well as how these interact with mathematics, computer science, and other disciplines.
Our group’s goal is to create, based on such microscopic connectivity and functional data, new mathematical models explaining how neural tissue computes.
We develop techniques for designing, implementing, and reasoning about multiprocessor algorithms, in particular concurrent data structures for multicore machines and the mathematical foundations of the computation models that govern their behavior.
Theory research at CSAIL covers a broad spectrum of topics, including algorithms, complexity theory, cryptography, distributed systems, parallel computing and quantum computing.
