Profiles

Li Zhang is a Ph.D. candidate in the Sensors Lab of the Electrical Engineering Department, CEMSE Division, King Abdullah University of Science and Technology (KAUST). He earned his B.S. in Microelectronic Science and Engineering from the University of Electronic Science and Technology of China in 2018, and his M.S. in Electrical Engineering from KAUST in 2019. His research interests include quantized neural networks, neural-network accelerator architectures, and software–hardware co-design. He has technical expertise in optimization algorithms, machine-learning methods, FPGA-based digital circuit design, and experimental measurement techniques.

Li Zhang is interested in quantized neural networks, neural network accelerator and software/hardware co-design.

Lijie Hu is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST), with a Master’s degree in Mathematics from Renmin University of China. Her research focuses on responsible AI, particularly in explainable AI (XAI) and privacy-preserving machine learning. Lijie’s recent research emphasizes making XAI more accessible and practical. Her work centers on developing Usable XAI-as-a-Service systems (Usable XAI) and Useful Explainable AI toolkits (Useful XAI), bridging the gap between theoretical innovation and real-world application. Her research was recognized as “Best of PODS 2022”. She has received several prestigious honors, including the KAUST Dean’s List Award in 2022, 2024, and 2025, and was recognized as a Top Reviewer at AISTATS 2023. Beyond her research, Lijie actively contributes to the academic community as a member of the AAAI Student Committee.

Luca is currently a PhD candidate in the Applied Mathematics and Computational Sciences department at KAUST. Additionally, he is pursuing a dual PhD degree in Aeronautical Engineering at Politecnico di Milano. He obtained his M.Sc. in Aeronautical Engineering and his B.Sc. in Aerospace Engineering from Politecnico di Milano.

Luca's research focuses on developing computational techniques for aeroacoustics and aerodynamics. In particular, he investigates high order methods for the noise prediction of Urban Air Mobility vehicles in complex scenarios.

Lucas graduated in Electrical Engineering at the Federal University of Rio Grande do Norte (UFRN), Natal, Brazil, on December 2019. From January to July 2019, he was a visiting student at the Information Systems Lab/KAUST, when he worked on indoor localization systems using acoustic waves. In the following year, he joined KAUST as a MSc. student under the supervision of Dr. Tareq Al-Naffouri. He successfully obtained his MSc. degree in December 2021. His Master's thesis is titled A Bayesian Approach to D2D Proximity Estimation using Radio CSI Measurements, and the continuation of this work led to a journal publication at the IEEE Open Journal of the Communications Society. He is currently a Ph.D. student at the Distributed Systems and Autonomy Group/KAUST, under the supervision of Dr. Shinkyu Park.

Lucas' research interests are multi-agent systems, robotics, and deep learning (especially Multi-Agent Reinforcement Learning).

Luis Vazquez obtained a Bachelor Degree on Robotics and Telecommunication from Universidad de las Americas Puebla, his final thesis work was a review on autonomous control and Natural Language Processing for the Robotics human-machine collaboration and interconnection between digital and physical components.

My research is focused on the effects of physical attacks to a robot sensor in the digital processing of the autonomous process more focused on Autonomous control algorithms for 2D vehicles and drones.

Madi Makin (Graduate Student Member, IEEE) received both his B.Sc. and M.Sc. degrees in Electrical and Computer Engineering from Nazarbayev University, Astana, Kazakhstan. He is currently pursuing a Ph.D. degree in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

His research interests include wireless communication systems, with a particular focus on reconfigurable intelligent surfaces (RIS), non-orthogonal multiple-access (NOMA) networks, and physical-layer security.

Madi's research interests include wireless communication systems, with a particular focus on reconfigurable intelligent surfaces, non-orthogonal multiple-access (NOMA) networks, and physical-layer security.

Manal A. Alshehri is a Doctoral Candidate in Computer Science at King Abdullah University of Science and Technology (KAUST). She holds a B.S. and M.S. degree in Computer Science from King Abdulaziz University, where she also serves as a lecturer. Her research has been published in leading international conferences, including IEEE Big Data and CIKM.

Her research spans a broad range of artificial intelligence applications, with a focus on enhancing recommendation systems and advancing text mining techniques. She employs cutting-edge AI methodologies to address key challenges such as cold-start problems, user privacy, diversity, and filter bubbles. She is also interested in analyzing user behavior across digital platforms and in leveraging generative large language models to create realistic simulations and automate labor-intensive tasks.

Mario Soto Martinez earned his Bachelor’s degree in Nanotechnology Engineering from ITESO, Guadalajara, Mexico, in 2020. During his studies, he completed an internship at CIIDEP, ITESM Monterrey (2018), where he worked on smart materials for environmental applications. His undergraduate research focused on the fabrication of nanostructured electrodes for biosensing. He later obtained his M.Sc. in Bioengineering from King Abdullah University of Science and Technology (KAUST), Saudi Arabia, in 2022, where he specialized in the microfabrication of thin-film transistors for biosensing applications. Mario’s interdisciplinary background in nanotechnology, materials engineering, and bioelectronics underpins his current research on multimodal sensing systems for point-of-care diagnostics

Mario’s research focuses on the development of multimodal sensing systems for point-of-care disease screening. His approach combines electrochemical and gas sensors to detect biomarkers that indicate disease presence or stage. He integrates materials engineering, microfabrication, electronics, and prototyping to design and optimize these platforms for practical implementation, aiming to advance accessible and reliable healthcare technologies.

Mattia Soldan is a final-year Ph.D. candidate in Electrical and Computer Engineering at KAUST, where he is advised by Prof. Bernard Ghanem. His research lies at the intersection of computer vision and natural language processing, with a focus on scalable and efficient algorithms for semantic video understanding and retrieval. His work spans task-specific deep learning architectures, dataset creation, and efficient visual encoding pipelines. Mattia is passionate about building intelligent systems that connect visual content with language and advancing research that bridges fundamental understanding with practical impact.

Mattia's research focuses on video and language understanding and especially on how to leverage mutual information to solve specific tasks as Single Video Moment Retrieval and Video Corpus Moment Retrieval.

Małgorzata Forystek is a Statistics Master's student at the Bayesian Computational Statistics & Modeling research group  (BAYESCOMP) under the supervision of Professor Håvard Rue. She received her B.Sc. degree in Applied Mathematics from AGH University of Science and Technology in Kraków, Poland, in 2023.

Her research interest is mainly in Bayesian and computational Statistics and Data Science.

Michał Forystek received his B.Sc. degree in Information and Communication Technology from AGH University of Science and Technology in Kraków, Poland, in 2023.

He has a 2 years of experience as a Java Developer working for IG Group in Kraków.

Currently he is a Computer Science Master's student at the Secure Next Generation Resilient Systems Lab (SENTRY) under the supervision of Professor Charalambos Konstantinou.

Michał's research involves using Load Altering Attacks to exploit the Load Frequency Control in Power Systems and developing the appropriate countermeasures.

Mohamed Aziz Benhouichet is pursuing his M.S./Ph.D. in Electrical and Computer Engineering at KAUST, where he works on embedded artificial intelligence, IoT, and multimodal perception systems. Before starting his graduate studies, he gained hands-on research experience at KAUST as a visiting student, contributing to projects on multimodal fusion between event-based cameras and LiDAR sensors. Prior to joining KAUST, Mohamed earned his Engineering Diploma in Signals and Systems from Ecole Polytechnique de Tunisie (EPT), graduating with first-class honors. He ranked among the top 1% of students in Tunisia
in the 2022 national entrance exam for engineering schools. His academic path reflects a strong multidisciplinary foundation, bridging theory and application in intelligent systems, robotics, and digital hardware.

• Artificial Intelligence (AI)
• Embedded AI and TinyML
• Internet of Things (IoT)
• Multimodal sensor fusion
• Signals and Systems
• Robotics

Mohammad Alharbi is a master student in Electrical Engineering department within CEMSE division at King Abdullah University of science and Technology (KAUST).  He received his bachelor's degree in Electrical Engineering from King Abdul Aziz University in Saudi Arabia and joined KAUST in Fall 2017.  Currently, he is working on experimental implementation of robust control strategies for parabolic solar collectors.

Nowadays, the decrease in fossil resources along with the increase of their environmental impact is driving the world to renewable energy to meet the energy demand. Solar energy represents one promising alternative clean energy source. The heat transfer mechanism of the solar collector is concentrating the sunlight by the parabolic shaped mirrors to the central tube which contain the fluid to be heated.

However, Solar energy is affected by the environmental changes such as solar irradiance.  Therefore, the aim is to design controller to force the outlet temperature to track the desired level by changing the fluid velocity.  A small-scale set-up is built to mimic the outdoor set-up for testing the controller in a controlled environment before going to the harsh environment outside.  Also, understanding the dynamic of the system will be easier inside controlled environment.