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NVIDIA Open Software Tools for Science Workshop

The NCSA Center for Artificial Intelligence Innovation calls all data scientists to participate in the NVIDIA Open Software Tools for Science Workshop. Taught by NVIDIA Data Scientists, this event will provide a comprehensive overview of end-to-end data science and analytics pipelines for data-driven discovery. This workshop is open to all. Attend the Zoom workshop.

Please note: participants will be required to sign in to their Zoom account before entering the meeting


Thursday, October 29, 2020

1:00-1:45pm CST

Tim Costa, NVIDIA Corporation
We’ll discuss the NVIDIA HPC SDK, a comprehensive, integrated suite of compilers, libraries and tools for the NVIDIA HPC Platform with new developments that continue to open GPU computing to a wider audience of developers and users, including automatic acceleration and tensor core programmability in standard languages and novel libraries for compute and communication.
2:00-2:45pm CST
High-Performance Data Science with RAPIDS

Zahra Ronaghi, NVIDIA Corporation
RAPIDS is a collection of open-source libraries for accelerating data science pipelines on GPUs, and is designed with familiar APIs for data scientists working in Python. We will present an overview of this platform, core libraries including cuDF (GPU-accelerated dataframes) and cuML (GPU-accelerated machine learning), and Dask on GPUs for large-scale data analytics.
3:00-3:45pm CST
AI for Science

Tom Gibbs and David Hall, NVIDIA Corporation
Explore how the latest breakthroughs in AI are being applied to problems in climate, weather, satellite remote sensing, material science, biological systems, high energy physics, and space. Deep learning may be viewed as a new method for building GPU-accelerated scientific software that was previously far beyond our reach. We will discuss some of the cutting-edge breakthroughs that have been achieved with these techniques, and examine recent progress addressing major science specific challenges such as interpretability, uncertainty quantification, and enforcing physical constraints. Then we will explore the latest AI trends and discuss the scientific opportunities they present.


Tim Costa

HPC Software Product Manager
NVIDIA Corporation, Santa Clara, CA

Tim Costa Tim is Product Manager for HPC Software at NVIDIA, responsible for the NVIDIA HPC SDK, HPC Compilers and Math Libraries in addition to programming model strategy. Prior to joining NVIDIA Tim worked as a performance library architect and HPC application engineer, and owned enabling efforts for CFD applications at Intel. Dr. Costa obtained his Ph.D. in Mathematics from Oregon State University on the development and analysis of numerical methods for fluid flow in stochastic or evolving porous media.

Zahra Ronaghi

Data Scientist
NVIDIA Corporation, Bowling Green, KY

Zahra Ronaghi Zahra Ronaghi is a senior data scientist and engineer at NVIDIA, working on GPU-accelerated machine learning. Prior to joining NVIDIA, Zahra was a postdoctoral fellow at Lawrence Berkeley National Laboratory (NERSC), where she worked on performance optimization of a tomographic reconstruction code and deep neural networks for neutrino telescopes. She graduated from Clemson University with a Ph.D. in Biomedical Engineering and received her M.S. and B.S. degrees in Electrical Engineering.

Tom Gibbs

Developer Relations
NVIDIA Corporation, Seattle, WA

Tom Gibbs Tom is currently responsible for strategy and implementation of programs to enable and promote developers to take full advantage of NVIDIA technology. Tom brings over 30 years of experience in HPC, and has applications expertise in industries ranging from Physics, Aerospace, Healthcare, Life Sciences, Energy and Financial Services. Prior to NVIDIA Tom held senior management positions for early stage cloud startup companies in the healthcare market segment. He spent 15 years with the Intel Corporation, where he managed a global team responsible for leading innovation programs at CERN, NCSA, British Petroleum and Morgan Stanley as Director of Strategy and Architecture in the Solutions Group. During his time at Intel Tom was part of the HPC Business Unit responsible for ASCI RED and other large-scale computing systems. Tom was a past Chairman of the Open Grid Forum and a member of the Center for Excellence in Supply Chain Management at MIT.

David Hall

Sr. Data Scientist
NVIDIA Corporation, Boulder, CO, USA

David Hall David Hall joined NVIDIA in January 2018 after working as an Assistant Professor of Research in Computer Science at CU Boulder. Dr. Hall is an expert in AI, deep learning and its applications to science. Dr. Hall has a broad technical background with expertise in theoretical physics, computational fluid dynamics, numerical methods, and climate modeling. Dr. Hall spent much of the previous decade developing non-hydrostatic dynamical cores for high resolution climate models in HPC environments. As a solution architect at NVIDIA, Dr. Hall’s primary role is to help scientist and engineers understand and translate the latest breakthroughs in artificial intelligence into practical solutions in the areas of weather, climate, and space. Dr. Hall earned his PhD in Physics from the University of Santa Barbara, CA and a BA in physics from CU Boulder.