Profiles

Talha Ariff was awarded his BEng/MEng degree in Biomedical Engineering from City, University of London in 2022, where he focused on optoelectronic devices for use in medical and chemical sensing.
He is now a Ph.D. student in the Photonics Lab at KAUST, researching further applications for optoelectronic sensors in desert agriculture and undersea communication cables.

Marine Ecology & Desert agriculture, Sensors, Photonics & Optoelectronics

Tingang graduated from University of Electronic Science and Technology of China (UESTC) majoring in electro-magnetic field and wireless technology on 2022.

Tingang focuses on ultrawide bandgap semiconductors, especially AlN and AlGaN epitaxy by MOCVD and device fabrications

• MIMO Radar signal processing.
• Wireless communications..
• Deep Learning.

I am a PhD student in computer science specializing in Riemannian geometry and flow visualization.
In my free time and during my bachelor stage,  I am working  in graphics engines and computer vision.

Research interest

He is interested in observer relative flow visualization,  extreme-scale data, visualization, computer graphics.

• Signal processing.
• Learning method over complex.
• Dynamic structures
• Low-complexity and energy-efficient VLSI signal processing system design.

Yang Liu is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). His primary research interests involve uncertainty quantification, Monte Carlo methods, and finite element methods. He obtained a Master's Degree in Applied Mathematics and Computational Science from KAUST in 2019.

I am currently pursuing a Ph.D. in Computer Science at King Abdullah University for Science and Technology (KAUST), under the supervision of Prof. Bernard Ghanem. My research focuses on applying machine learning to solve real-world challenges, with an emphasis on efficient training and inference of models. This includes improving data, training, and model efficiency to create more scalable and effective solutions. I hold an M.S. in Computer Science from KAUST and a B.S. in Electrical and Computer Engineering from Virginia Tech. I have over 7 years of experience in automation systems prior to starting my M.S. degree.

I am interested in exploring techniques for efficient training and inference of machine learning models, with a focus on improving data efficiency, training efficiency, and model efficiency.

She holds a Master’s Degree in Data Science from the University of Jeddah and a Bachelor's Degree in Computer Science with a specialization in Software Engineering from King Abdulaziz University. She began her career as a Digital Repository Coordinator at King Abdullah University for Science and Technology (KAUST), where she was responsible for developing data analysis algorithms and building data models. Later, she transitioned to the role of Data Science Coordinator and Teaching Assistant at the SDAIA-KAUST Center of Data Science & AI, where she focused on AI content development, automation, and machine learning training initiatives.

Her research interests focus on generative AI, vision/text multimodality, and deep learning