Computational journeys in a sparse universe

Abstract: Sparsity is a fundamental assumption that allows us to compute efficiently and find parsimonious solutions to science and engineering problems. Sparsity exists in all basic sciences such as physics, biology, and chemistry. I am going to give a sampling of our recent work on sparse computations, with an emphasis on large-scale parallelism. My talk will travel across diverse problem domains including randomized linear algebra, graph neural networks, protein family and structure discovery from metagenomic data, and tensor computations. The underlying theme will be the challenges posed by sparsity and the computational techniques we employ to overcome these challenges.

Bio: Aydın Buluç is a Senior Scientist at the Applied Math and Computational Research Division of the Lawrence Berkeley National Laboratory (LBNL) and an Adjunct Faculty at EECS department of UC Berkeley. His research interests include parallel computing, combinatorial scientific computing, high performance graph analysis and machine learning, sparse linear algebra, and computational genomics. He received his Ph.D. in Computer Science from the University of California, Santa Barbara in 2010. After that, he was a Luis W. Alvarez postdoctoral fellow at LBNL. Dr. Buluç is a recipient of the DOE Early Career Award in 2013 and the IEEE TCSC Award for Excellence for Early Career Researchers in 2015. He recently led a team that was chosen as a finalist for the 2022 ACM Gordon Bell Prize. He was a founding associate editor of the ACM Transactions on Parallel Computing. He is currently directing a DOE Mathematical Multifaceted Integrated Capabilities Center named Sparsitute.