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Learning and Algorithms on Graph-Structured Data, Aydin Buluc, Berkeley Lab

Abstract:

Graphs provide a rich abstraction for modeling interactions in science and society. Graph algorithms have been instrumental in solving some of the hardest combinatorial problems in science. Recently machine learning on graph structured data is becoming a key component of artificial intelligence, with applications in drug discovery, transportation networks, power-grid analysis, and protein design. This Guided Affinity Group strives to actively participate in all non-conflicting minisymposiums, minitutorials, invited talks, and contributed talks related to computations on graphs.

What are the relevant conference themes?

  • AI and machine learning for science and engineering
  • Computation with discrete structures and graphs
  • Data assimilation, challenges in data science, math of AI and ML
  • High-performance computing, emerging architectures and programming paradigms

Short Biography:

Aydın Buluç is a Staff Scientist and Principal Investigator at the Lawrence Berkeley National Laboratory (LBNL) and an Adjunct Assistant Professor of EECS at UC Berkeley. His research interests include parallel computing, combinatorial scientific computing, high performance graph analysis and machine learning, sparse matrix computations, and computational biology. Previously, he was a Luis W. Alvarez postdoctoral fellow at LBNL and a visiting scientist at the Simons Institute for the Theory of Computing. He received his PhD in Computer Science from the University of California, Santa Barbara in 2010 and his BS in Computer Science and Engineering from Sabanci University, Turkey in 2005. 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 is a founding associate editor of the ACM Transactions on Parallel Computing.

Motivation:

I strongly believe in the important of increasing the diversity in applied mathematics and computational sciences in general. I have been able to work on increasing diversity in my own research group but this GAG allows me to expand the impact of my efforts to a broader audience.