“From image recognition in social media to self-driving cars and medical image processing, deep learning is everywhere in our daily lives. Learn about recent advancements in deep learning that have been made possible by improvements in algorithms, numerical methods, and the availability of large amounts of data for training, as well as accelerated computing solutions based on GPUs. With GPUs, great performance can be reached across a wide range of platforms, from model development on a workstation to training on HPC and data-center systems to embedded platforms, enabling new horizons for computing and AI applications.”
Julie Bernauer is Senior Solutions Architect 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 obtained her PhD from Université Paris-Sud in Structural Genomics studying Voronoi models for modelling 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.