Profiles

Kuilian Yang received the B.S. degree in Integrated Circuits and Systems from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2019, and the M.S. degree in Electrical and Computer Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2021. He is currently pursuing the Ph.D. degree in Electrical and Computer Engineering at KAUST.

Kuilian's research focuses on neuromorphic computing and efficient hardware acceleration of Spiking Neural Networks (SNNs), including exploiting sparsity in streaming SNN accelerators and designing high-throughput, low-latency architectures for real-time edge intelligence applications.

Lama studied Information technology at King Saud University (KSU) in Riyadh and graduated with a Bachelor degree in 2011. After her bachelor degree, she joined KSU as a Teaching Assistant. Later on, she continued her studies and earned her Master degree in Computer Science from the School of Computing Science at the University of Glasgow, United Kingdom.

Lama first joined KAUST as an Intern with Professor Jeff Shamma until she was enrolled for the Ph.D. program in Computer Science working in Professor Bernard Ghanem research group Image and Video Understanding Lab (IVUL).

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.

• Generative Models: Advancing and applying generative models, such as diffusion models, flow matching, and autoregressive models, with a focus on improving their architectures and exploring innovative applications.
• Unified Models: Developing models that bridge generative and understanding tasks, with an emphasis on leveraging understanding capabilities to enhance generative performance.

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.

Luyao Yang is a Ph.D. candidate in Computer Science at the King Abdullah University of Science and Technology (KAUST) working in the Networking Research Lab (NETLAB) under Prof. Basem Shihada. She obtained her Bachelor's degree in Software Engineering at the University of Electronic Science and Technology of China and her Master's degree in Computer Science at King Abdullah University of Science and Technology.

Luyao's research interests focus on the intersection of emerging technologies, focusing on areas such as wearable computing in healthcare and sports.

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.

Optoelectronics & Photonics, Quantum Computing, Quantum Photonics

Mingchen Zhuge is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST), working on AI agents, world models, and recursive self-improvement under the supervision of Professor Jürgen Schmidhuber. He has authored more than 20 top-tier publications with over 6,300 citations, of which more than 70% are from first-author papers. His representative projects include MetaGPT, NLSOM, GPTSwarm, Agent-as-a-Judge, Kaleido-BERT, and NeuralComputer.

His work has been recognized through six oral presentations at leading AI conferences, including the International Conference on Learning Representations (ICLR 2024, 2025, 2026; acceptance rate <1.2%) and the International Conference on Machine Learning (ICML 2024; acceptance rate <1.5%). He also received the Best Paper Award at the NeurIPS Ro-FoMo Workshop and an Outstanding Paper Nomination at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2025). Beyond research, he was nominated for the WAIC Future Star Award, recognized as an Outstanding Reviewer at the Conference on Computer Vision and Pattern Recognition (CVPR 2023; 232 out of 7,000+ reviewers), served as Lead Organizer of ICLR RSI 2026, and served as an Area Chair for COLM 2026 and ACM CAIS 2026.

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 Ashhad is a Ph.D. student at King Abdullah University of Science and Technology (KAUST), where he completed his M.S. in Bioengineering with a specialization in Bioinformatics and Machine Learning. He holds a Bachelor's degree in Computer Science and Engineering from Jamia Hamdard, New Delhi, India, which he completed with a Gold Medal. His academic journey has taken him across leading research institutions — as a Mitacs Globalink Research Intern at Queen's University, Canada and a Visiting Research Scholar at Duke University, USA.

Ashhad's research interests include neurosymbolic AI, deep learning and machine learning for biomedical applications, survival analysis and applied statistics.