Issam Said is a Manager of solutions architecture and engineering at NVIDIA. Per his background, he mainly works with large Energy organisations to help them embrace the NVIDIA HPC and Deep Learning end-to-end solutions, in order to accelerate their critical and data-intensive workloads. He holds a PhD in computational science, funded by TOTAL, that he received from the University of Pierre and Marie Curie (Paris, France) in 2015. Prior to joining NVIDIA, Issam was a Postdoctoral Fellow at the University of Houston working with TOTAL E&P USA, where he focused on GPU programming models for seismic processing. His technical expertise includes applied geophysics, numerical methods, HPC and Deep Learning applied to the Energy segment.
In this talk we will cover in detail the software components of accelerated machine learning and deep learning that are useful for geoscience workflows. We will cover the NVDIA tools for data pre-processing such as the NVIDIA DALI api, the GPU accelerated frameworks for training and multi-GPU training, and the tools we use for inference like the NVIDIA Triton inference server. We will also describe our contributions to some useful machine learning paradigms that are used in geoscience such as our tools for federated learning, end-to-end training, and transfer learning. We will finish the talk with some examples of implementation of ML based seismic interpretation.