Accessible news articles about our research

A few presentations with accompanying videos

  • Parallel Sparse Matrix Algorithms for Data Analysis and Machine Learning, ETH Zurich, March 2022 [Video][Slides]

  • Large-scale graph representation learning and computational biology through sparse matrices, NJIT Institute for Data Science, April 2021. [Video][Slides]

  • The COVID-19 reality encouraged us to create a Youtube channel for our research presentations.

  • Communication-Avoiding Sparse Matrix Algorithms for Large Graph and Machine Learning Problems, New Architectures and Algorithms Workshop at IPAM (UCLA), November 2018. [Video] [Slides]

  • Communication-Avoiding Sparse-Matrix Primitives for Parallel Machine Learning, Sparse Days, Toulouse, September 2018. [Video] [Slides]

  • Genomics, Graphs and the GraphBLAS, GraphXD: Graphs Across Domains, Berkeley, April 2018. [Video] [Slides]

  • Reducing Communication in Parallel Graph Computations, MMDS, Berkeley, June 2014. [Video] [Slides]

  • Three Goals in Parallel Graph Computations: High Performance, High Productivity, and Reduced Communication, Simons Institute for Theory of Computing, October 2013, [Video] [Slides]