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<> machine learning (10)

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

Visiting Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).
<> deep learning (6) <> artificial intelligence (5)

Najmeddine Dhieb

Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)
<> uncertainty quantification (5)

Omar Knio Research Group

Professor Omar Knio leads the so-called O-Knio research group, which stands for Omar Knio Research Group Research group areas of expertise and current scientific interests: bayesian inference, computational fluids mechanics, uncertainty quatification ‌Led by Prof. Omar Knio, the O-Knio research group. His research interests include uncertainty quantification, Bayesian inference, computational fluid mechanics, combustion, oceanic and atmospheric flows, turbulent flow, physical acoustics, energetic materials, microfluidic devices, dynamical systems, asymptotic techniques, multi-resolution

Saja Almohammadi

Saja Almohammadi is a PhD candidate in Applied Mathematics and Computational Sciences. She is studying under supervision of Professor Omar Knio. Research Interest Saja Almohammadi's research interest include working on Uncertainty Quantification (UQ) on chemical system. Development of methods for forward UQ as applied to chemical kinetic application relevant to Rapid compression machine on iso-octane. Education Profile M.Sc. in Applied Math, King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2014. BS Mathematics Department, King Abdelaziz University (KAU), Saudi Arabia

Ricardo Manuel Pinto de Lima

My mission is to develop computational models and methods based on mathematical optimization and data science for sustainable energy and chemical processes. I am deeply committed to decarbonization, climate change mitigation, and optimal operations management.

Omar Knio

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group
Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.
<> Computer Vision (4) <> Signal processing (4) <> AI (3) <> bayesian inference (3)

Omar Knio Research Group

Professor Omar Knio leads the so-called O-Knio research group, which stands for Omar Knio Research Group Research group areas of expertise and current scientific interests: bayesian inference, computational fluids mechanics, uncertainty quatification ‌Led by Prof. Omar Knio, the O-Knio research group. His research interests include uncertainty quantification, Bayesian inference, computational fluid mechanics, combustion, oceanic and atmospheric flows, turbulent flow, physical acoustics, energetic materials, microfluidic devices, dynamical systems, asymptotic techniques, multi-resolution

Omar Knio

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group
Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.
<> MATLAB (3) <> optimization (3)

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

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KAUST

In this work, we develop a new framework of trajectory planning for AUVs in realistic ocean scenarios. We divide this work into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. The latter are used to specify both the ocean current and the bathymetry. We apply a NLP to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we address uncertainty in the ocean current field. The uncertainty in the current is described in terms of a finite ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. Advanced visualization tools are used to amplify simulation results.

Ricardo Manuel Pinto de Lima

My mission is to develop computational models and methods based on mathematical optimization and data science for sustainable energy and chemical processes. I am deeply committed to decarbonization, climate change mitigation, and optimal operations management.
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