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Zhuo Feng, PhD

Associate Professor, Electrical and Computer Engineering

Seminar Talk: Scalable Spectral Sparsification of Graph Laplacians, Integrated Circuits and Data Networks

Abstract: In this talk, Zhuo will first introduce his recent work on developing a practically efficient, nearly-linear complexity spectral graph sparsification approach for aggressively sparsifying large graph Laplacian matrices, integrated circuits, and as well as data networks. Next, he will talk about how to leverage the latest result for developing nearly-linear time numerical and graph-based algorithms for solving large partial differential equation (PDE) and sparse matrix problems, design automation of future nanoscale ICs, as well as graph partitioning and data clustering of large data networks. Lastly, he will discuss how to leverage graph sparsification techniques for optimally solving large PDEs and sparse matrices on emerging heterogeneous parallel computing platforms.