Graph Data Science for the GPU Era: Hands-on Intro Through Case Studies, AI, Visualization, and the RAPIDS GPU OSS Ecosystem


This workshop is a hands-on introduction to the emerging end-to-end modern graph intelligence pipeline. It is quite different from what was common even 3 years ago because graph computing is undergoing several revolutions. On one side, the need is growing because the ubiquity of data platforms has meant that teams now have abundant data like logs and transactions that are finally ready for answering long-desired questions about behavior and relationships. At the same time, connecting the dots in meaningful ways has become much more effective: technology advances in the GPU computing and neural network communities are in the orders-of-magnitude levels. The workshop will overview the modern ecosystem and then take participants on a hands-on journey for going from raw data all the way through compute, neural search, AI, visualization, and automation... and all on GPUs.

Background Knowledge
Participants can either use instructor-provided GPU data science environments or their own. To use instructor-provided accounts, please use form X ahead of time. We encourage bringing your own data (CSV/Parquet), and if too sensitive for the shared Paperspace environments, you can one-click private AWS/Azure VMs via


Leo Meyerovich co-founded Graphistry in early 2014. Previously, he researched programming language design at UC Berkeley and Brown University. His PhD introduced the first multicore web browser (3 PLDI SRC awards) and led to browser parallelization at Mozilla, Samsung, Google, Microsoft Research, and Qualcomm. Leo also performed the largest scale analysis of programming language adoption and social underpinnings (OOPSLA best paper) and, with security researchers at Google, Microsoft, and Brown University, designed several secure web scripting languages. Earlier, he designed Flapjax, the first functional reactive language for highly concurrent web software (OOPSLA best paper). His research was supported by the first Qualcomm Innovation Fellowship (winner among 50 Ph.D. teams at Berkeley and Stanford), the NSF GRFP, and grants from Samsung, Nokia, Microsoft, NVIDIA, Intel, and others.

Open Data Science




Open Data Science
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