Chief Data Science Officer of the San Diego Supercomputer Center
“Composable Systems and Convergence Research at the Digital Continuum from Edge to HPC”
Thursday 16 February, 1.30pm – 2.10pm
Abstract
Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence (AI) driven methods which require specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of an ecosystem for container coordination and integration. New approaches for dynamic composability of heterogeneous systems are needed to further advance the data-driven and AI-integrated scientific practice by multidisciplinary teams of researchers. This talk presents a novel approach for using composable systems for team science at the intersection of scientific computing, artificial intelligence (AI), and remote sensing at the edge, including the first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster and Sage, a reconfigurable edge AI infrastructure. It will also overview scientific workflow case studies that compose the insights from edge sensing, scientific instrumentation, AI, computing capabilities and physics-driven simulations in the wildland fire domain.
Bio
Dr. İlkay Altıntaş, a research scientist at the University of California San Diego, is the Chief Data Science Officer of the San Diego Supercomputer Center as well as a Founding Fellow of the Halıcıoğlu Data Science Institute. She is the Founding Director of the Workflows for Data Science (WorDS) Center of Excellence and the WIFIRE Lab. The WoRDS Center specializes in the development of methods, cyberinfrastructure, and workflows for computational data science and its translation to practical applications. The WIFIRE Lab is focused on artificial intelligence methods for an all-hazards knowledge cyberinfrastructure, becoming a management layer from the data collection to modeling efforts, and has achieved significant success in helping to manage wildfires. Since joining SDSC in 2001, she has been a principal investigator and a technical leader in a wide range of cross-disciplinary projects. With a specialty in scientific workflows, she leads collaborative teams to deliver impactful results through making computational data science work more reusable, programmable, scalable, and reproducible. Her work has been applied to many scientific and societal domains including bioinformatics, geoinformatics, high-energy physics, multi-scale biomedical science, smart cities, and smart manufacturing. She is also a popular MOOC instructor in the field of “big” data science and reached out to more than a million learners across any populated continent. Among the awards she has received are the 2015 IEEE TCSC Award for Excellence in Scalable Computing for Early Career Researchers and the 2017 ACM SIGHPC Emerging Woman Leader in Technical Computing Award. Ilkay received a Ph.D. degree from the University of Amsterdam in the Netherlands.
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