Publicity

Accessible news articles about our research

• Our 2022 ACM Gordon Bell Prize finalist work on exascale protein homology search is described here.

• Our HUNTRESS method for tumor phylogeny reconstruction for cancer genomics using single-cell data is described here.

• Here is an article on GraphBLAST (accompanied with one pleasant picture), our GPU sparse matrix library for graph computations

• Here is an accessible article about the GraphBLAS effort (accompanied with three unpleasant pictures). More info, including a reference implementation by Tim Davis and the C language API can be found in the GraphBLAS Forum website (standard building blocks for graph algorithms in the language of matrices).

• HipMCL clusters protein similarity networks with 70 billion edges in a couple of hours. This highly-scalable implementation of the Markov Cluster algorithm is available open source. Here is an accessible press release.

• Communication-avoiding algorithms help accelerate graphical model estimation (a.k.a. inverse covariance matrix estimation): http://cs.lbl.gov/news-media/news/2018/scalable-machine-learning-with-hp-concord/

Several presentations with accompanying videos

• Sparse matrices powering three pillars of science: simulation, data, and learning, ACM ISSAC, Invited Tutorial, July 2022 [Video part 1] [Video part 2]

• 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]