Saudi HPC/AI Conference 2022:
Using HPC & AI to accelerate and improve medical research
(September 27-29, 2022)

 

Workshops

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Saber Feki,

Sr. Computational Scientist Lead, KAUST

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David Pugh,

Saff Scientist, KAUST

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Mohsin Shaikh,

Computational Scientist

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Rooh Khurram

Saff Scientist, KAUST

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Data Science on HPC platforms

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Workshop introducing the DPC++ technology, targeting educating the audience about the ambitions behind it, how to write simple programs using it and some of the advanced features it offers. Data Parallel C++ is a high-level language designed for data-parallel programming. The intent is to provide developers with a higher-level language to use other than OpenCL and other languages, making programs portable across different architectures while keeping the ability to write hardware-specific kernels to optimize performance on different platforms.

Speaker(s) profile:

Saber Feki leads the computational and data science and engineering at the KAUST Supercomputing Core Laboratory, providing support, training, advanced services and research collaborations with users of the leadership supercomputer Shaheen II Cray XC40 and a heterogeneous cluster “Ibex” with over 600 GPUs.

Saber is passionate about technology, and enjoys working with users and technology vendors to plan and execute refreshes to KAUST HPC and AI infrastructure with the latest hardware and software technologies. He is leveraging his expertise to support and consult for several similar deployments for local and regional organizations such as the American University of Sharjah, and the National Center of Meteorology of Saudi Arabia.

Saber received his MSc and Ph.D. degrees in computer science from the University of Houston in 2008 and 2010, respectively. He then joined the oil and gas company TOTAL in 2011 as an HPC Research Scientist. Saber has been working at KAUST since 2012.

David Robert Pugh is an experienced research software engineer and data scientist who loves to teach.

David Robert Pugh just finished developing training materials to help data scientists get started managing their virtual environments with Conda and Docker. David Robert Pugh is currently developing data engineering solutions to accelerate distributed training of deep neural networks on HPC resources.

David Robert Pugh has a deep knowledge of the core data science Python stack: NumPy, SciPy, Pandas, Matplotlib, NetworkX, Jupyter,

Scikit-Learn, PyTorch, TensorFlow.

Mohsin Ahmed Shaikh is a Computational Scientist at King Abdullah University of Science and Technology.

Rooh Khurram is working as a Staff Scientist at KAUST Supercomputer Lab at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. He has conducted research in finite element methods, high performance computing, multiscale methods, fluid structure interaction, detached eddy simulations, in-flight icing, and computational wind engineering. He has over 20 years of industrial and academic experience in CFD. He specializes in developing custom made computational codes for industrial and academic applications. His industrial collaborators include: Boeing, Bombardier, Bell Helicopter, and Newmerical Technologies Inc. Before joining KAUST in 2012, Rooh worked at the CFD Lab at McGill University and the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. Rooh received his Ph.D. from the University of Illinois at Chicago in 2005. In addition to a Ph.D. in Civil Engineering, Rooh has degrees in Mechanical Engineering, Nuclear Engineering, and Aerospace Engineering.

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Dr Valerio Rizzo

AI Lead & Solution Architect, Lenovo

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Enabling and democratizing MLops in Healthcare

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The implementation of AI-based systems is having increasing success in the Healthcare Industry, enabling technological advances for both diagnosis and treatment of clinical conditions, as well as for the optimization and improvement of the efficiency of healthcare facility management.

ML-based systems’ R&D and deployment have seen the emergence of the so-called MLOps, a framework that aims to solve many of the organizational challenges related to the training and deployment phases.

The implementation of MLops framework also requires the development of SW platforms, which provide tools for development teams to simplify and optimize workflows by reducing potential bottlenecks due, for example, to the management and use of a complex HW and SW infrastructure for the prototyping and development of AI models.

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Valerio is the AI Lead & Solution Architect for Lenovo, he is key member of an expert team of Artificial Intelligence, Machine Learning and Deep Learning specialists operating within the EMEA field sales organization and its business development team. He is a recognized expert in the fields of neuroscience and neurophysiology with 10 years of track record in brain research made between Italy and USA.