Profiles

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

Xuhao Wu is a Ph.D. student at the Integrated Photonics Laboratory under the supervision of Prof. Yating Wan at King Abdullah University of Science and Technology (KAUST). He received his B.E degree in Optoelectronic Information Science and Engineering from Harbin Institute of Technology and M.S. degree in Photonics from Ghent University. After graduation from Ghent University, Xuhao joined a spin-off company of IMEC, Luceda Photonics, and worked as an application and PDK development engineer. During his time there, Xuhao honed his programming skills and provided exceptional customer support, assisting over 50 individuals with their design projects. Additionally, he conducted training sessions, including two workshops with 100 attendees each, and delivered lectures at universities, including Shanghai Jiaotong University and Zhejiang University. Xuhao's contributions also include building IPKISS PDK for various foundries on the SOI/SiN/SiON platform.

Xuhao’s research interests are in applications of integrated photonics for optical computing, LiDAR, and optical communication.

Yan Wang is a M.S./Ph.D. student in the Division of Computer, Electrical and Mathematical Sciences & Engineering (CEMSE) at King Abdullah University of Science and Technology (KAUST). She obtained her bachelor's degree of Communication Engineering from the University of Electronic, Science, and Technology of China (UESTC) in 2024.

Photonics and optoelectronics, Ultrawide bandgap semiconductor

Yang Xiao is a Ph.D. candidate in Statistics at King Abdullah University of Science and Technology (KAUST). With a background that bridges rigorous mathematical theory and industrial application, his work focuses on improving the accuracy of real-time predictive modeling in high-stakes environments.

Before joining KAUST, Yang spent several years as a Statistician in the pharmaceutical industry, where he specialized in experimental design, protocol development, and ensuring 100% numerical reproducibility for core research frameworks under strict regulatory standards. His academic journey began with a dual-degree background in Applied Statistics and Actuarial Science, followed by an MSc in Statistics with Data Science from the University of Edinburgh, where he focused on multi-modal signal extraction and latent pattern recognition in epidemiological data.

Yang’s research focuses on the intersection of Bayesian Hierarchical Modeling and high-performance algorithmic optimization. He is particularly interested in leveraging latent Gaussian processes and signal decomposition to drive superior predictive outcomes in both public health and quantitative finance.

Yara Banda is an M.S./Ph.D. student at the Photonics Lab in the Electrical and Computer Engineering Department (CEMSE) at King Abdullah University of Science and Technology (KAUST). She received her B.Sc. degree from King Abdulaziz University (Jeddah, Saudi Arabia) in 2021.

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

Yasmine is a Ph.D. candidate in Electrical and Computer Engineering, supervised by Prof. Eric Feron and Prof. Taous-Meriem Laleg-Kirati. Before joining KAUST as a doctoral student, she was an intern in the Estimation, Modeling, and Analysis Group (EMANG). Her research focuses on developing hybrid model-based and learning-based estimation algorithms for diverse classes of nonlinear systems with convergence guarantees. During her Ph.D., Yasmine completed an internship at the University of California, Berkeley, under the supervision of Prof. Alexandre Bayen, and visited several universities, including Stanford, UC Santa Barbara, and UC Irvine.

Yasmine's research interests include controller design, observer design, virtual sensors, scientific machine learning, UAV control in non-inertial frames.

Yidan is a Ph.D. student in Computer Science at King Abdullah University of Science and Technology since August 2022, under the supervision of Prof. Wolfgang Heidrich in Computational Imaging Group. Yidan obtained her bachelor’s degree in Measurement and Control Technology and Instrument from University of Science and Technology of China in 2020.

Yidan is interested in developing novel computational imaging systems in both hardware and software.

Yousef Awad received his B.Sc. degree in Communication and Networks Engineering from Prince Sultan University, Riyadh, Saudi Arabia, in 2020, and his M.Sc. degree in Telecommunication Engineering from King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, in 2024. He is currently pursuing a Ph.D. in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

His research interests span various areas of wireless communication systems, including Integrated Sensing and Communications (ISAC), Unmanned Aerial Vehicle (UAV) detection, and applications of Reconfigurable Intelligent Surfaces (RISs). During his M.Sc. studies, he also worked as a Telecommunication Engineer at King Fahd International Airport (KFIA), Dammam, Saudi Arabia, with Enova by Veolia.

Yousef’s research interests span various topics in wireless communication systems, including Integrated Sensing and Communications (ISAC), Unmanned Aerial Vehicle (UAV) detection, and applications of Reconfigurable Intelligent Surfaces (RISs).

Yulian Wu is a Ph.D. candidate in Computer Science at KAUST, advised by Francesco Orabona. She has published in top venues including NeurIPS, ICML, COLT, Science Advances, and AISTATS, and was recognized as a NeurIPS Top Reviewer. She received the Best Presentation Award at the Free Rein Global Youth AI Forum. She is also actively involved in the research community, serving as an Organizing Committee Member and Session Chair for the KAUST Rising Stars in AI Symposium 2025 and 2026.

Yulian's research investigates trustworthy machine learning and interactive decision-making, with a focus on differential privacy, robustness, and heavy-tailed feedback in bandits, reinforcement learning, and RLHF.

• Optical wireless communication.
• Embedded systems.
• Hardware & software co-design.

Zahrah Alnasser obtained her bachelor's degree from Dammam College for Women and her master's degree in pure mathematics from the University of Illinois - Urbana-Champaign.

Real analysis and applied PDEs.

Zhandos Zhakipov received his Bachelor’s and Master’s degrees in Electrical and Computer Engineering from Nazarbayev University, Kazakhstan, in 2022 and 2024, respectively. He is currently pursuing a Ph.D. in the Communications and Computing Systems Laboratory (CCSL) at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Ahmed Eltawil.

Zhandos Zhakipov's research interests include reconfigurable intelligent surfaces (RIS), millimeter-wave (mmWave) communication, the Internet of Things (IoT), and digital twins for wireless communication systems.

Zhican Zhou is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), where he is advised by Prof. Yating Wan, and conducting research in Integrated Photonics Laboratory (IPL) research group. Before joining KAUST, Zhican received his bachelor's degree (majoring in materials engineering) from Chongqing University (CQU) in 2019 and his master's degree (majoring in photonics) from the joint-cultivation program of Nankai University (NKU) and the National Center for Nanoscience (NCNST) in 2022, where he ranked in the top ~5% for both his undergraduate and postgraduate studies. He obtained numerous honors for his outstanding performance, including the First Price Scholarship, Outstanding Graduate of Chongqing University, and the Nankai University Freshman Scholarship.

He has contributed to 24 journal and conference papers, with representative first-/co-first-authored works in Nature Electronics, Nature Communications, Advanced Photonics, and eLight. He was also awarded the 2026 Tingye Li Memorial Scholarship for his contributions to integrated photonic computing and hardware-rooted security.

Zhican's research focuses on integrated optoelectronic computing systems, hardware-aware neural network frameworks, and privacy-preserving hardware architectures. 

Zichun earned his Bachelor's degree in Microelectronics Science and Engineering from Sun Yat-sen University in 2023 and is currently pursuing a Master of Philosophy degree in The Hong Kong University of Science and Technology (Guangzhou). He has been honored with several prestigious awards that highlight his exceptional achievements, including the Excellent Graduation Thesis Award from SYSU (Top 5%), the Meijiayin Scholarship, and the Red Bird Full Postgraduate Scholarship at HKUST(GZ). 

During his postgraduate studies, Zichun has developed strong expertise in the modeling, design and testing of photonic integrated circuits and optoelectronic systems.

Zihang Xiang is a 4th-year Ph.D. candidate at King Abdullah University of Science and Technology(KAUST), advised by Di Wang. His current research is on privacy-preserving data analysis. He is interested in pushing the boundaries of differential privacy via principled approaches in broad machine learning applications.

Differential privacy

Ziling is a doctoral student in Statistics at King Abdullah University of Science and Technology (KAUST), where she is conducting her research under the mentorship of Professor Hernando Ombao in the Biostatistics research group and Professor Ying Sun in the Environmental Statistics research group. Ziling Ma earned her Bachelor's degree in Mathematics and Applied Mathematics from Tianjin Normal University in China. She then furthered her education at KU Leuven in Belgium, where she was awarded a Master's degree in Mathematics. Ziling embarked on her Ph.D. journey in Statistics in August 2023.

Ziling Ma's research interests are situated at the crossroads of time series analysis, robust statistics, and functional data analysis.

Zinan XIAO is a Ph.D. student at Photonics Laboratory at King Abdullah University of Science and Technology (KAUST). 
Before he joined the Photonics Laboratory, he worked as an optical hardware engineer in the industry, focusing on designing and testing optical splitters. 
He received his Master of Science degree at Imperial College London, UK. 
He obtained his bachelor's degree in optoelectronic information engineering at Chongqing University. 

Sensors, Photonics and optoelectronics

Bachelor's degree from Wuhan University.

Edge computing, federated learning, machine learning systems, and resilient computing.

Abdul-Lateef Haji-Ali is an associate professor in the Maxwell Institute for Mathematical Sciences and the School of Mathematical and Computer Sciences, Heriot-Watt University. He obtained his PhD in Applied Mathematics in 2016 from King Abdullah University of Science and Technology (KAUST).

Selected Publications

• A.-L. Haji-Ali, H. Hoel, and R. Tempone​, Weak convergence analysis in the particle limit of the McKean–Vlasov equations using stochastic flows of particle systems, The IMA Journal of Applied Mathematics (2025).​​ DOI: 10.1093/imamat/hxaf015.
• M. B. Giles and A.-L. Haji-Ali. Multilevel Path Branching for Digital Options In: Annals of Applied Probability 34.5 (2024), pp. 4836–4862. ISSN: 1050-5164. DOI: 10.1214/24-AAP2083.
• M. B. Giles and A.-L. Haji-Ali. Multilevel Nested Simulation for Efficient Risk Estimation. In: SIAM/ASA Journal on Uncertainty Quantification 7.2 (Jan. 2019), pp. 497–525. ISSN: 2166-2525. DOI: 10.1137/18m1173186.

Abdul-Lateef's research interests include Uncertainty Quantification, Numerical Analysis, Machine Learning, Stochastic Differential Equations, Numerical methods for SDEs and SPDEs, Multilevel Monte Carlo methods, Particle systems, Crowd modeling, Mean-field theory, Sparse Grids, Combination techniques, Multi-index techniques, and Inverse problems.