Topics

<> machine learning (11)

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).

Circuits and Systems for Efficient Machine Learning and Artificial Intelligence

Machine Learning (ML) and Artificial Intelligence (AI) applications require the use of more and more advanced algorithms for extracting meaningful information from increasingly (and sometimes incredibly) large sets of data. While the algorithmic part has recently seen significant advances (as for instance through the adoption of Deep or Convolutional Neural Networks), it sometimes comes at the cost of high computational complexity which hinders their straightforward implementability. This activity aims at advancing in this direction by: proposing architectures to reduce the computational cost
<> artificial intelligence (8)

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)
COEfoodsecurity

AI-enabled UAVs and Multimodal Monitoring for Protected and Controlled Environment (PACE)

This project aims to utilize advanced technologies for UAVs to enhance the efficiency, accuracy, and responsiveness of crop monitoring and management within the diverse PACE settings. The primary objectives revolve around employing a fleet of micro-UAVs equipped with cameras and LiDAR sensors to capture high-resolution images and 3D point clouds and perform multi-layered AI analysis. While cameras offer visual insights into surface-level plant health and characteristics, LiDAR provides deeper insights about plant physical attributes for a more comprehensive crop assessment. This project is

Circuits and Systems for Efficient Machine Learning and Artificial Intelligence

Machine Learning (ML) and Artificial Intelligence (AI) applications require the use of more and more advanced algorithms for extracting meaningful information from increasingly (and sometimes incredibly) large sets of data. While the algorithmic part has recently seen significant advances (as for instance through the adoption of Deep or Convolutional Neural Networks), it sometimes comes at the cost of high computational complexity which hinders their straightforward implementability. This activity aims at advancing in this direction by: proposing architectures to reduce the computational cost

Hakim Ghazzai

Senior Research Scientist, Electrical and Computer Engineering
Hakim is a researcher focusing on designing multi-disciplinary innovative solutions for diverse smart city applications and services.
<> Deep learning (7) <> Computer Vision (6)

Abdelrahman Eldesokey

Postdoctoral Research Fellow
I am a Postdoctoral Fellow at KAUST, Saudi Arabia, specializing in Generative AI and conducting research on advanced topics in Generative and Large Language Models (LLMs). I bring over 11 years of combined academic and industrial experience in Computer Vision and Deep Learning across Sweden, Egypt, and Saudi Arabia.

Najmeddine Dhieb

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.

Aznaur Aliev

M.S. Student

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.
<> IoT (5)
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).
COEfoodsecurity

AI-enabled UAVs and Multimodal Monitoring for Protected and Controlled Environment (PACE)

This project aims to utilize advanced technologies for UAVs to enhance the efficiency, accuracy, and responsiveness of crop monitoring and management within the diverse PACE settings. The primary objectives revolve around employing a fleet of micro-UAVs equipped with cameras and LiDAR sensors to capture high-resolution images and 3D point clouds and perform multi-layered AI analysis. While cameras offer visual insights into surface-level plant health and characteristics, LiDAR provides deeper insights about plant physical attributes for a more comprehensive crop assessment. This project is

Circuits, Systems, and Algorithms for Low-power Signal Processing in IoT Nodes Implementation

In the IoT paradigm the low-power signal processing, either analog of digital, is a key-enabling technology. Many unconventional processing techniques, either based on a statistical analysis or not, have been introduced in the effort of being able to complete a task with the lowest possible energy. A first example is given by the Compressed Sensing, an acquisition technique which relies on the sparsity of the underlying signals, to enable sampling below the classical Nyquist rate. The advantages with respect to the above “classical” technique is to transfer complexity from the acquisition

Hakim Ghazzai

Senior Research Scientist, Electrical and Computer Engineering
Hakim is a researcher focusing on designing multi-disciplinary innovative solutions for diverse smart city applications and services.
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