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.
Our research is directed at the use of randomness and combinatorial techniques in concurrent algorithm and data-structure design. We also study parallelism in the brain and what we can learn from it towards the design of futuristic computing machines.
We are creating a concurrent data structure for 2D/3D points that supports efficient storage, range queries, and merging of disjoint datasets, motivated by highly-parallel algorithms for reconstructing neuron connections in the brain.
