Iyad Katib
Papers
3
Total Citations
54
H-Index
3
About
Iyad Katib is a leading researcher at the intersection of high-performance computing, machine learning, and cyber-physical systems (CPS). His work is pivotal in enabling the intelligent infrastructure behind smart cities and complex scientific applications. Katib is best known for developing the ZAKI and ZAKI+ frameworks, innovative tools that leverage machine learning to automatically optimize the performance of sparse matrix-vector multiplication (SpMV) on distributed memory architectures. These contributions directly address critical bottlenecks in large-scale scientific computing, with his foundational papers on the topic accumulating over 43 citations. Demonstrating the breadth of his expertise, Katib has also ventured into computational finance, pioneering a deep transformer reinforcement learning model for stock trading strategies. This work, published in 2022, showcases his ability to apply advanced AI techniques to real-world, data-driven decision-making. Through his research, Katib provides essential tools and methodologies that enhance the efficiency and intelligence of modern computing systems, from scientific simulations to financial markets.
Research Focus
Key Achievements
Top Papers
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