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Deep Learning & HPC: New Challenges for Large Scale Computing

Julie Bernauer, Nvidia

In this video from the 2017 HPC Advisory Council Stanford Conference, Industry Insights: Julie Bernauer presents: Deep Learning & HPC: New Challenges for Large Scale Computing.

“In recent years, major breakthroughs were achieved in different fields using deep learning. From image segmentation, speech recognition or self-driving cars, deep learning is everywhere. Performance of image classification, segmentation, localization have reached levels not seen before thanks to GPUs and large scale GPU-based deployments, leading deep learning to be a first class HPC workload. In this talk, after a short introduction to Deep Neural Networks on GPUs, we will present NVIDIA’s platform for deep learning and how new advances in hardware and software integrate in large-scale computing environments.”

Julie Bernauer leads a Pursuit Engineering Solutions Architect team for Machine Learning and Deep Learning at NVIDIA Corporation. She joined NVIDIA in 2015 after fifteen years in academia as an expert in machine learning for computational structural biology. She also teaches GPU computing at Stanford University and DL courses for the NVIDIA Deep Learning institute. She obtained her PhD from Université Paris-Sud in Structural Genomics studying Voronoi models for modeling protein complexes. After a post-doc at Stanford University with Pr. Michael Levitt, Nobel Prize in Chemistry 2013, she joined Inria, the French National Institute for Computer Science. While Senior Research Scientist at Inria, Adjunct Associate Professor of Computer Science at École Polytechnique and Visiting Research Scientist at SLAC, her work focused on computational methods for structural bioinformatics, specifically scoring functions for macromolecule docking using machine learning, and statistical potentials for molecular simulations.

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