The Team
Last but not least, these systems are supported by an experienced and knowledgeable team of Computational Scientists and System Administrators within the Supercomputing Core Laboratory. The Scientists team is multi-disciplinary and able to support various science areas and bring HPC related expertise to KSL users. Profiles of each team member are detailed as follows:
Computational Scientists Team for Shaheen and Ibex
Dr. Saber Feki
Saber Feki leads the computational scientists team at the KAUST Supercomputing Core Laboratory supporting and training users of the leadership supercomputer Shaheen. He is an HPC technology expert. He was one a key members in the procurement and leader of the acceptance of the world's 7th fastest supercomputer, Shaheen XC40. Since then he participated in numerous projects of small and mid-size clusters for KAUST (Ibex cluster), GAMEP, and AUS. 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. His research interests include parallel programming models and automatic performance tuning of MPI communications and OpenACC accelerated applications.
Dr. Rooh Khurram
Rooh Khurram is working as a Computational Scientist at Supercomputing Core Lab at KAUST. 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 15 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.
Dr. Zhiyong Zhu
Zhiyong Zhu received his PhD degree in Condensed Matter Physics from the Institute of Physics, Chinese Academy of Sciences, in 2009. After that, he spent 4 years conducting research in the area of Computational Material Science at KAUST and EPFL. In 2014, he joined KAUST Supercomputing Core Lab as a Computational Scientist to support Quantum Chemistry users on Shaheen. Owing to his 4 years' experience in using supercomputers for research and the knowledge about both computational codes and high performance computing, he has been able to help Shaheen users to use the computational resources more efficiently.
Dr. Bilel Hadri
Bilel Hadri is a Computational Scientist at the Supercomputing Core Lab at KAUST since July 2013. He contributes in benchmarking and performance optimization, helps in systems procurements, upgrades, and provides regular training to users. He received his Master in Applied Mathematics and his PhD in Computer Science from the University of Houston in 2008. He joined the National Institute for Computational Science at Oak Ridge National Lab as a computational scientist in December 2009 following a Postdoctoral Position in June 2008 at the University of Tennessee Innovative Computing Laboratory lead by Dr. Jack Dongarra. His expertise areas include performance analysis, tuning and optimization, System Utilization Analysis, Monitoring and Library Tracking Usage, Porting and Optimizing Scientific Applications on Accelerator Architectures (NVIDIA GPUs, Intel Xeon Phi), Linear Algebra, Numerical Analysis and Multicore Algorithms
Dr. Nagarajan Kathiresan
Nagarajan Kathiresan is a Computational Scientist at Supercomputing Core Lab at KAUST. He is working on application configuration, installation, support and performance optimization of diversified applications. Further, he is supporting and fulfilling diversified user’s requirements in Biological, Environmental Science, Computation biology, Machine learning domains & their open source applications. Before joining KAUST, he was a staff scientist at Sidra Medicine, Biomedical Informatics division, Qatar for more than 4 years in the support role of Next Generation Sequencing (NGS) Analysis. This includes: experimental genomics, NGS indexing, mapping & alignment of human genomes, sequence alignment. He contributed the configuration, optimization and parallelization of those applications in cluster based architecture. Some of the contributed are published in journal papers and patent in the area of NGS optimization. He has the working experience of parallel data mining services like MapReduce, Spark, Task Parallel Library, MPICH synchronization protocols etc. Prior to his research role, he worked as an advisory software engineer at IBM Corporations, for more than 8 years. He contributed performance optimization of various HPC domains like Quantum Chemistry, Molecular dynamics, Weather Modelling & Life Science applications in IBM hardware includes IBM Power and BlueGene. He obtained his Masters of Engineering in Computer Science from Jadavpur university, India in 2004 and his Ph.D. in Computer Science, Software fault tolerance in cluster computing, from National Institute of Technology, India in 2008.
Dr. Mohsin Ahmed Shaikh
Mohsin Ahmed Shaikh is a Computational Scientist at KAUST Supercomputing Lab (KSL). He has over 10 years of experience in designing, developing and supporting large scale HPC applications. He holds a PhD in Computational Bioengineering and a Post Doc, both from University of Canterbury, New Zealand. He worked previously at the Pawsey Supercomputing Centre as Supercomputing Applications Specialist before joining KAUST. As part of KSL's Application Support team, he provides support to users of Shaheen Supercomputer and GPGPUs in Ibex cluster, specially with questions related to Machine & Deep Learning at scale, high throughput Hyperparameter Optimization, portable workflows using containers and Kubernetes orchestration, and working with workflow engines e.g. Argo, Kubeflow and Ray.