Profiles

PhD from Peking University in 2021; Research Scientist in JD Explore Academy from 2021 to 2023.

machine learning, large language/vision models, trustworthiness

Dr. Gao Yang received his Ph.D. degree from the University of Birmingham, Birmingham, United Kingdom. Before joining KAUST, he served as an Academic Scholar at the Southern University of Science and Technology (SUSTech), Shenzhen, China. He participated in several academia projects focused on the development of RF/microwave devices in UK and China. He is currently a Research Specialist in the Computer, Electrical, and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. His research focuses on RF and microwave circuit and device design. He has authored or co-authored more than 30 publications in refereed journals and conferences.

Najmeddine Dhieb received the Diplôme d’Ingénieur in Telecommunication Engineering—with honors— the École Supérieure des Communications de Tunis (SUP’COM), Tunisia, in 2019. That same year, he worked as a Research Assistant at the Smart City Lab within the School of Systems and Enterprises at Stevens Institute of Technology in Hoboken, NJ, USA.
Currently, Najmeddine contributes his expertise as a Research Engineer at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, after serving as a Senior Data Scientist at Bdeo Technologies S.L. in Madrid, Spain. His work focuses on the intersection of Artificial Intelligence, Computer Vision, Internet-of-Things, Edge-Computing, and Blockchain, where he develops practical solutions for emerging digital challenges.

Najmeddine's research focuses on the following areas:

• Artificial intelligence
• Data science
• Computer vision
• Edge computing
• Blockchain

Dr. Samar Aseeri has more than 15 years of experience in supercomputing and scientific computing. She received her Ph.D. in Applied Mathematics from Umm Al-Qura University, where she developed a strong analytical foundation in mathematical modeling and computational science. During her higher education, she studied quantum mechanics on two separate occasions, which contributed to her long-term interest in quantum computing and its computational foundations.

She later received advanced training in supercomputing at IBM’s Thomas J. Watson Research Center in Yorktown Heights, New York, and subsequently supported the Shaheen user community at the KAUST Supercomputing Laboratory (KSL), focusing on scalable performance analysis and HPC optimization.

She has been actively engaged with the international scientific computing community, including early participation in major supercomputing conferences such as ISC High Performance and SC, where she followed emerging quantum computing tracks and sessions during the formative stages of the field.

At King Abdullah University of Science and Technology (KAUST), she continues to advance HPC research while contributing to hybrid quantum–classical computing workflows, including instructional support for KAUST’s early quantum algorithms course.

Dr. Aseeri's research interests span high-performance computing, applied computational mathematics, scalable scientific applications, and quantum computing technologies. Current focus areas include FFT algorithms, performance tooling, application profiling, parallel benchmarking, and hybrid quantum–classical methods for scientific and optimization problems.

Abdelhay Ali is a researcher specializing in the hardware implementation of advanced
algorithms, particularly in the realms of machine learning, embedded systems, and IoT,
utilizing both FPGA and ASIC design flows. Abdelhay Ali has led and contributed to the design
and tapeout of multiple ASIC chips, focusing on low-power and application-specific
hardware. With a strong interest in the Internet of Bodies (IoB), he is exploring innovative
ways to use the human body as a communication medium for medical applications. His
work includes building healthcare systems and enhancing their efficiency through novel
hardware solutions. Overall, his research bridges the gap between cutting-edge technology
and practical healthcare solutions, advancing medical technology and enhancing the
integration of machine learning in hardware systems.

Abdelrahman Eldesokey is a Postdoctoral Fellow at King Abdullah University of Science and Technology (KAUST), specializing in Generative AI and Computer Vision. His research explores diffusion models, multimodal large language models, and vision foundation models, bridging perception and generation. He holds a Ph.D. in Computer Vision and Deep Learning from Linköping University, Sweden, and has over a decade of combined academic and industrial experience across Sweden, Egypt, and Saudi Arabia. His work has been published in leading venues including CVPR, ICCV, SIGGRAPH, NeurIPS, and ICLR, and focuses on advancing the controllability, interpretability, and reliability of modern generative systems.

My research focuses on Generative AI, particularly diffusion models, vision-language models, and agentic multimodal systems. I am interested in improving the controllability, interpretability, and reliability of generative models, bridging perception and generation. Additional interests include uncertainty-aware learning, 3D scene understanding, and AI evaluation for generative models in real-world settings.

Aelson Sobral is a postdoctoral researcher in the Free Boundary and Interface Problems group, led by Prof. Miguel Urbano at KAUST. He earned his PhD in Mathematics from the Federal University of Paraíba (UFPB), Brazil, before joining KAUST in September 2024. His research explores geometric frameworks to advance the regularity theory of fully nonlinear PDEs and Free Boundary Problems that are often born in phenomenon related to superconductivity and congested traffic dynamics.

Aelson Sobral's research centers on developing geometric tools to analyze Free Boundary Problems, focusing on regularity theory for degenerate and singular partial differential equations (PDEs) and free boundary regularity. These problems are intrinsically linked to flame propagation and congested traffic dynamics.

Sheikh received his PhD in Computer Engineering from King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, KSA in 2016. Before coming to KAUST, he was serving as an Assistant Professor in the Department of Electrical & Computer Engineering, Air University, Islamabad, Pakistan.

His research interests lie in designing fault tolerant digital circuits, hardware security and computer architecture.

Ahmed Nasser received his M.Sc. degree in Electronics and Communications Engineering from the Egypt-Japan University of Science and Technology (E-JUST), New Borg El Arab, Egypt, in 2016. He earned a double Ph.D. degree from Kyushu University, Fukuoka, Japan, and E-JUST, Egypt, in 2020. He is currently a Postdoctoral Fellow at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. His research interests lie in the broad areas of next-generation wireless communication systems and networks.

Ahmed's research interests focus on both learning and non-learning based techniques for enhancing the performance of emerging technologies in 6G wireless networks.

Aigerim Nurbayeva is a Postdoctoral Fellow at the Robotics, Intelligent Systems, and Control (RISC) Lab at KAUST, where she works under the supervision of Professor Shinkyu Park. She received her Ph.D. in Robotics from Nazarbayev University in 2024, focusing her doctoral research on Model Predictive Control (MPC) and Imitation Learning for human-robot interaction.

She has authored five peer-reviewed publications in prestigious journals, including IEEE Transactions on Industrial Informatics and Control Engineering Practice.

Aigerim's research at the RISC Lab focuses on designing robot arm control systems for the KAUST Coral Restoration Initiative and 3D reconstruction techniques for date palm trees. Her technical background includes nonlinear MPC, Deep Imitation Learning. She has worked extensively with platforms such as Isaac Sim, ROS, and UR manipulators.

Aijaz H. Lone is a PhD candidate Integrated Intelligent Systems I2S group, under the guidance of Professor Gianluca Setti at King Abdullah University of Science and Technology (KAUST), I'm dedicated to pushing the boundaries of spintronic innovation. With a background in electrical and computer engineering from the Indian Institute of Technology (IIT-M), I've developed a passion for advancing spintronic devices for data storage and neuromorphic applications.

As a PhD candidate Integrated Intelligent Systems I2S group, under the guidance of Professor Gianluca Setti at King Abdullah University of Science and Technology (KAUST), I'm dedicated to pushing the boundaries of spintronic innovation. With a background in electrical and computer engineering from the Indian Institute of Technology (IIT-M), I've developed a passion for advancing spintronic devices for data storage and neuromorphic applications.

During my Ph.D., I focused on simulating and experimentally realizing spintronic devices tailored for neuromorphic (brain-inspired) computing. This includes advanced spintronic memories that emulate synapse and spiking neuron functionalities. By integrating these devices, I have explored their potential in implementing artificial neural networks (ANNs) and spiking neural networks (SNNs) at both circuit and system levels, aiming to improve the computational paradigms.

As a researcher, I bring expertise in modeling, simulations, and experimental realization to the table. My focus
lies in pioneering spintronic technologies like magnetic tunnel junctions (MTJs), domain wall devices, and
magnetic skyrmionic devices – all with the goal of revolutionizing AI and machine learning.

Through my research, I aim to bridge the gap between theoretical understanding and practical implementation, driving the development of novel spintronic-based solutions for AI.

Anass ElYaagoubi is a statistician, data scientist, and researcher currently based at King Abdullah University of Science and Technology. He earned his Ph.D. in Statistics from KAUST under the supervision of Hernando Ombao, focusing on topological and statistical analysis of brain time-series data. His academic work spans machine learning, topological data analysis, neuroscience, and high-dimensional statistical modeling, with publications in journals and conferences across statistics, AI, and computational neuroscience.

Before joining KAUST, he studied information systems engineering and data science in France at National Institute of Applied Sciences of Rouen and University of Rouen Normandy. Over the years, he has worked on projects involving biomedical signal analysis, natural language processing, search systems, and AI-enabled educational platforms. He has also taught statistics, machine learning, and programming to large academic and industry audiences, including collaborations with Saudi institutions and industry partners.

His broader vision is to bridge rigorous mathematical research with impactful technological tools that can improve scientific discovery, learning, and human understanding.

• Topological Data Analysis
• Time-Series Analysis and Dynamical Systems
• Computational Neuroscience and Brain Connectivity
• High-Dimensional Statistics and Network Science
• Biomedical Signal Processing (EEG, LFP, ECG, Hi-C)
• Large Language Models and AI for Education
• Scientific AI and Interdisciplinary Data Science

Dr. López Oriona received his bachelor’s degree in Mathematics and his master’s degree in Statistics both from the University of Santiago de Compostela (Spain), his master’s degree in Big Data Analytics from the European University of Madrid (Spain), and his PhD in Statistics from the University of A Coruña (Spain). He was a Visiting Researcher at the Sapienza University of Rome (Italy), the University of Sydney (Australia), the Helmut Schmidt University of Hamburg (Germany), and Lancaster University (United Kingdom). Since September 2023, he is a Postdoctoral Fellow in the Environmental Statistics Research Group at KAUST, under the supervision of Professor Ying Sun.

Dr. López Oriona research interests include Computational Statistics, Directional Statistics, Fuzzy Set Theory, and Time Series Data Mining, among others.

Dr. Apala’s research focuses on the development of RF sensors for real time medical and environmental applications like industrial, biomedical and humanitarian applications. Her recent research area focuses on the development of RF biosensors for detecting human metabolic and genomic activities paving the way for wearable sensing with higher sensitivity.  His expertise spans RF sensors, RF biosensing, RF passive and active devices, THz receiver development and dielectric characterization of materials.

Dr. Aram Mkrtchyan is currently a Postdoctoral Fellow at the Integrated Photonics Laboratory (IPL) at King Abdullah University of Science and Technology (KAUST), where he works under the supervision of Prof. Yating Wan. He earned his Ph.D. in Photonics from the Skolkovo Institute of Science and Technology (Skoltech), where he specialized in nonlinear optics, ultrafast fiber lasers, and photonic integrated circuits. He received his M.Sc. degrees in parallel from Skoltech and the Moscow Institute of Physics and Technology (MIPT), and holds a B.Sc. degree in Applied Physics and Mathematics from MIPT.

Before joining KAUST in 2025, Dr. Mkrtchyan served as a Senior Research Scientist at Skoltech, contributing to both academic and industry-driven initiatives, including the Huawei Innovation Research Program and Skoltech's Translational Research and Innovation Program. His achievements include the development and commercialization of an ultrafast all-fiber laser at 920 nm, used as a pump source for single-photon quantum emitters in quantum computing systems, advanced studies on carbon nanotube-based photonic devices, and the development of a hybrid microresonator-based frequency comb system. 

In 2024, Dr. Mkrtchyan was nominated for the Skoltech Educational Leadership Excellence Award, honoring for demonstrating leadership and best practices in making Skoltech the top choice for international education in Russia.
Dr. Mkrtchyan has authored over 15 peer-reviewed publications, including articles in Nano Letters, Nanophotonics, Carbon, Journal of Power Sources, Applied Physics Letters, Journal of Lightwave Technology, etc and holds several patents in the fields of ultrafast optics and integrated photonics. His research focuses on nonlinear optics, integrated photonics, ultrafast fiber lasers, Quantum nanomaterials including carbon nanotubes for photonic applications.
 

Dr. Mkrtchyan’s research focuses on nonlinear optics, integrated photonics, ultrafast fiber lasers, Quantum nanomaterials including carbon nanotubes for photonic applications.

Arved Bartuska obtained his bachelor's and master's degrees at the University of Vienna. He received his Ph.D. in 2025 at RWTH Aachen University and is currently a postdoctoral fellow at KAUST.

Arved Bartuska's research interests include applied mathematics, stochastic analysis, Bayesian optimal experimental design, and uncertainty quantification.

Azimkhon Ostonov is currently a Postdoctoral Research Fellow at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. He received his PhD from KAUST in May 2025, specializing in Computer Science under the supervision of Professor Mikhail Moshkov. Azimkhon obtained his Bachelor’s degree in Applied Mathematics and Informatics in 2012 and completed his Master’s degree in Computer Systems and their Software in 2014, both from the National University of Uzbekistan. He has made considerable contributions to the field, with publications including works on Machine Learning and Complexity Analysis.

Before joining KAUST, he worked as a teacher at the National University of Uzbekistan for four years. Prior to that, he began his programming career as a junior programmer at Fido-Biznes in Tashkent.

Azimkhon's research focuses on complexity of decision trees for decision tables.