Profiles

Qilong Pan is a Ph.D. candidate in Statistics at KAUST, supervised by Prof. Ying Sun. His research focuses on scalable Gaussian Process modeling, high-performance statistical computing, and GPU-accelerated inference for large-scale spatial and computer experiment data.

Qilong Pan's work bridges statistical modeling, optimization, and high-performance computing (HPC) to tackle complex challenges in geospatial analytics and machine learning. He develops efficient algorithms and software for Vecchia-based approximations, aiming to enable practical Gaussian Process applications on modern supercomputers.

Qizhou Wang is a Ph.D. candidate in Electrical and Computer Engineering (ECE) at King Abdullah University of Science and Technology (KAUST). During his Ph.D. studies, he received CEMSE Division Dean’s List Award. He received his B.S. degree in Communication Engineering from University of Electronic Science and Technology of China (UESTC) in 2020 and M.S. degree in Electrical and Computer Engineering from King Abdullah University of Science and Technology (KAUST) in 2021. He dedicated his doctoral work to integrating nanophotonics with machine learning to create smarter imaging hardware as a member of the Primalight Laboratory research group under the supervision of Professor Andrea Fratalocchi.

Qizhou’s vision is centered on providing the multimodal raw data necessary to fuel the next generation of artificial intelligence. This commitment to innovation extends beyond the lab; as a co-founder of a startup specializing in hyperspectral imaging solutions, he is actively translating complex scientific breakthroughs into practical, real-world tools. By merging inverse design with computational imaging, he is building the foundational hardware that will allow machine vision to see further, deeper, and more intelligently.

security, distributed systems

Rajat received his B.Tech. in Electronics and Communication Engineering from the National Institute of Technology (NIT) Hamirpur, India, in 2019, and his M.Tech. in Electrical Engineering with specialization in VLSI from the Indian Institute of Technology (IIT) Mandi, India, in 2021. He is currently pursuing his Ph.D. in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. At KAUST, his work centers on the design and development of energy-efficient wearable and body-integrated systems for digital health and Internet of Bodies applications.

Rajat’s research interests focus on low-power wearable and body-centric electronic systems for digital health applications. His current work centers on Human Body Communication for Internet of Bodies platforms, encompassing body area networks, mixed-signal and system-level design, sensing interfaces, data integrity in wearable devices, and energy-efficient hardware for continuous health monitoring. His broader technical background includes hardware security, spin-based circuits, and emerging technologies for sustainable and autonomous electronic systems.

Rawan Alghamdi is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), working with Prof. Mohamed-Slim Alouini and Prof. Tareq Y. Al-Naffouri. Her research focuses on wireless system optimization and waveform design to develop affordable and reliable communication solutions to address global digital inequality. She has published in leading IEEE journals and conferences, including IEEE Transactions on Communications and IEEE Information Theory Magazine.  

Rawan has received multiple awards, including first place in the 2025 IEEE PIMRC Three-Minute Thesis competition, the IEEE SPS scholarship, and being a runner-up in the 2023 CST and IEEE Future Networks Initiative Competition. 

Rawan earned her Master of Science (M.Sc.) from King Abdullah University of Science and Technology (KAUST) in 2022, and her Bachelor of Science (B.Sc.) in electrical and computer engineering from Effat University in 2020. 

In addition to her research, Rawan actively supports local talent and youth in science, engineering, and research. She is the co-chair of the IEEE Women in Engineering podcast. She has served as a teaching assistant for ten courses at KAUST, instructed over 300 undergraduate students in machine learning and deep learning, and tutored high-school research programs. Rawan leads an online community to empower local students to pursue research and graduate studies. 
 

Razan Shams is an MS student in Electrical and Computer Engineering at KAUST, joining the Integrated Intelligent Systems (I2S) Lab in 2025. Her research focuses on micro-electromechanical systems (MEMS) and biomedical devices, particularly magnetic membranes for electromagnetic pumping applications.

She previously worked as a research intern at KAUST, where she contributed to projects in the Nanofabrication Core Lab focused on semiconductor device fabrication and 2D material-based photodetectors. She presented her research on scalable MoS₂ photodetectors at the IEEE EDS Future of Semiconductors Forum 2025 among PhD students and researchers.

Razan holds a BS in Physics (Nanophysics) with first-class honours from the University of Jeddah, where her research received the Gold Award at the Student Scientific Forum.

 

• Semiconductor Device Fabrication
• 2D Materials for Optoelectronics
• Micropumps and Biomedical MEMS
• Magnetic Materials and Electromagnetic Actuation
  
   

I earned my Bachelor's degree in Artificial Intelligence from Imam Abdulrahman bin Faisal University, where I focused on applying machine learning to medical data. I then joined KAUST as an MS/PhD student, where I have published research in Natural Language Processing (NLP). Currently, my work centers on Multi-Modal Large Language Models, exploring the intersection of NLP and computer vision to advance AI systems that can understand both language and visual information.

My research focuses on Multi-Modal Large Language Models, Vision-Language Models, natural language processing, and computer vision. I am particularly interested in identifying and addressing the limitations of current AI models, exploring where they fail and developing methods to overcome these gaps. My work aims to improve model robustness, reliability, and performance, advancing the capabilities of AI systems to better understand and process both textual and visual information.

Roman received the B.S. and M.S. degrees from the Faculty of Physics, Saint Petersburg State University, Saint Petersburg, Russia, in 2012 and 2014, respectively. Since 2018, he has been pursuing a Ph.D. degree at the King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. His research mainly focuses on gas discharge plasma at atmospheric pressure and its application. Roman's academic advisor is Professor Deanna Lacoste.

Ruochen Zhu received his B.S. degree in Information Engineering from the University of Electronic Science and Technology of China in 2024. He is currently pursuing M.S. and Ph.D. degrees in the Electrical and Computer Engineering (ECE) program at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Ahmed Eltawil.

Salem Al-Saqaf is a graduate student at King Abdullah University of Science and Technology (KAUST), pursuing an M.S. in Electrical and Computer Engineering. He holds a B.S. in Electrical Engineering with First Class Honors from King Fahd University of Petroleum and Minerals (KFUPM). His academic journey integrates electronics, semiconductors, and artificial intelligence, with a strong focus on data-driven decision-making, intelligent systems, and next-generation computing. Alongside his academic research, Salem has gained valuable experience applying data science and AI in industry settings, bridging advanced research with practical innovation. He is also actively engaged in student leadership, mentorship, and outreach initiatives at KAUST.

• IoT and Intelligent Sensing
• Electronics and Semiconductor Devices
• IC Design
• Artificial Intelligence (AI) and Machine Learning

Salim Ksous is an MS/PhD student in the Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raúl F. Tempone at King Abdullah University of Science and Technology (KAUST).

Education Profile

• Master of Science in Applied Mathematics and Computational Sciences, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (January 2026 – Present)
• National Engineering Diploma in Multidisciplinary Engineering, Ecole Polytechnique de Tunisie, Tunis, Tunisia (September 2022 – June 2025)
• Undergraduate Degree in Mathematics and Physics, Preparatory Institute for Engineering Studies of Monastir, Monastir, Tunisia (September 2020 – June 2022)

Early Career and Awards

• Visiting Student in Applied Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (October 2025 – December 2025)
• Visiting Student in Applied Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (February 2025 – July 2025)
• EPT Scholarship, Ecole Polytechnique de Tunisie, Tunisia (2022 – 2025)

Salim's research interests include optimal control, optimization and numerical methods.

Salma Kharrat is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), where her research focuses on machine learning under limited information, spanning federated learning, multi-agent reinforcement learning, and large language models.

Her research has been published in leading AI and machine learning venues, including AISTATS, EMNLP, and ECAI, with contributions such as FilFL, DPFL, and ACING, which address client selection in federated learning, decentralized personalization, and instruction optimization for large language models. During her Ph.D., she was recognized with the KAUST Dean’s List Award.

Salma earned her M.S. in Computer Science from KAUST and her engineering degree from the National School of Computer Science in Tunisia, where she ranked among the top students.

In addition to her research, she has been actively involved in teaching and mentoring, serving as an instructor and teaching assistant for machine learning and AI courses at KAUST and across Saudi Arabia, and mentoring student research projects through KAUST Academy. 

Salma research focuses on developing principled algorithms for decentralized learning, personalization under heterogeneity, and black-box optimization, with the goal of advancing scalable and robust intelligent systems.

Salman Ghori is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), working in the Aerospace and Transportation Systems research group under the supervision of Professor Eric Feron. Before joining KAUST, he earned a master’s degree in Aerospace Engineering from Sapienza University of Rome and worked as a Senior Engineer at ZF Friedrichshafen AG.

Salman's research focuses on autonomous traffic management, optimization-based control, multi-agent coordination, safety-critical autonomy, and real-time robotic validation. His work combines Model Predictive Control, Mixed-Integer Linear Programming, and safety-critical control methods to study efficient and fair coordination of autonomous agents in constrained environments.

Samy Mounir recently graduated from UCLouvain in Belgium with a B.Sc. in Engineering, specializing in Software and Mathematical Engineering. His diverse experiences include AI internships at KAUST and Belixys, as well as optimization and data analysis projects using real-world data. His research focuses on leveraging cutting‐edge AI techniques to solve practical problems.

Samy’s research centers on applied AI solutions with a focus on computer vision and smart city applications. He is currently working on plant monitoring using UAVs and computer vision techniques.

Saravanan Yuvaraja is a Postdoctoral Research Fellow in the Advanced Semiconductor Laboratory at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. His research expertise lies in thin-film semiconductor devices, focusing on monolithic 3D integration, hybrid CMOS circuits, and large-area electronics (LAE). He pioneered the fabrication of record-breaking 10-stack In₂O₃ transistors published in Nature Electronics (2024) and developed hybrid CMOS circuits combining oxide and organic semiconductors, accepted in Nature Electronics (2025). These innovations address critical challenges in achieving high-density, low-power integrated circuits for flexible and wearable systems.

Dr. Yuvaraja has authored 43 peer-reviewed journal publications, including 12 first-author papers, with over 1,100 citations (h-index: 15). His high-impact publications span Nature Electronics, Nature Reviews Electrical Engineering, ACS Applied Materials & Interfaces, and Chemical Society Reviews. He holds multiple patents in oxide and organic TFT-based sensors and logic circuits. Beyond research, he actively mentors Master’s and PhD students in device fabrication, electrical characterization, and process integration, and contributes to graduate curriculum development in oxide semiconductor technologies. Dr. Yuvaraja’s interdisciplinary work bridges materials engineering, device physics, and system integration, aiming to advance scalable, energy-efficient electronics for AI, IoT, and neuromorphic applications.

Saravanan Yuvaraja is interested to develop futuristic nature olfaction inspired chemical sensors for accurate on-site detection with high sensing performance achieved at minimum cost and low power consumption.

• Organic electronics
• Gas sensors
• Silicon Nano-electronics
• Neuromorphic computing
• Perovskite electronics

Saud received his bachelor's degree in 2023 in Physics from King Fahad University of Petroleum and minerals (KFUPM). He is currently working towards a master’s degree under the supervision of Prof. Yating Wan at King Abdullah University of Science and Technology (KAUST).

Saud’s research interests are in applications of integrate photonics for optical computing and LiDAR.

Shahd Shami is a Ph.D. student in Electrical and Computer Engineering at KAUST, specializing in robotics and control systems. Before that, she worked as a Teaching Assistant at King Abdulaziz University, where she gained hands-on experience in AI projects and technology solutions. Shahd received her M.Sc. in Electrical and Computer Engineering from KAUST, focusing on machine learning and robot planning, and her B.Sc. in Electrical and Computer Engineering from King Abdulaziz University, Saudi Arabia.

Shahd's research focuses on developing adaptive and intelligent control mechanisms to enhance robotic systems' autonomy and efficiency in dynamic environments. She combines classical control theory with advanced machine learning algorithms to create models that improve robotic performance, adaptability, and interaction with uncertain environments. Areas of interest: • Adaptive control systems for robotics • Machine learning integration in control mechanisms • Advanced sensor technologies for robotics • System integration and implementation of AI solutions • Robotics education and training

Shaopeng is a Ph.D. student in Computer Science at KAUST. Before that, He was as an algorithm engineer at the trustworthy AI research group at JD Explore Academy, JD.com, Inc. He received MPhil in Computer Science from The University of Sydney, Australia, and B.Sc in Mathematics from the South China University of Technology, China.

His research lies in trustworthy machine learning, especially the security and privacy aspects of machine learning. He is interested in using mathematical principles to identify and mitigate security and privacy risks in real-world machine learning systems.

Shuai Lu is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), supervised by Professor Gabriel Wittum and working in the Modeling and Simulations Lab. He completed his B.Eng. in Mechanical Engineering from China University of Mining and Technology (CUMT) in 2015. He received his master’s degree in Solid Mechanics at Beihang University in 2018.

• Long-Form Video Understanding.
• Temporal Action Detection, Action Recognition.
• Video-Language Grounding.