Profiles

Rita Ferreira is a Research Scientist at KAUST in the research group of Prof. Diogo Gomes, within the CEMSE Division. Rita received her dual Ph.D. degree in Mathematics at Carnegie Mellon University (CMU), USA, and at the New University of Lisbon (UNL), Portugal, in 2011. She was supervised by Prof. Irene Fonseca (CMU) and Prof. Luísa Mascarenhas (UNL). Before joining KAUST in 2014, Rita was a Visiting Assistant Professor at the Department of Mathematics of the Faculdade de Ciências e Tecnologia of the New University of Lisbon, Portugal, and a Postdoctoral Research Fellow at Instituto Superior Técnico of the University of Lisbon, Portugal.

Rita research focuses on variational and asymptotic techniques to study minimization problems and systems of PDEs arising in problems within material sciences, propagation of waves, mean-field games, and imaging.

Dr. Sameh Abdulah is an HPC senior research scientist specializing in high-performance computing (HPC), and large-scale data analytics. He is a Senior Research Scientist at the Computer, Electrical and Mathematical Sciences and Engineering Division at KAUST. His work focuses on developing scalable algorithms and efficient software frameworks to address complex computational challenges across diverse scientific and engineering domains, including spatial statistics.

He serves as a key link between three major research groups within the extreme computing research at KAUST: the Hierarchical Computations on Manycore Architectures (HiCMA) group led by Professor David Keyes, the Spatio-Temporal Statistics & Data Science (STSDS) group led by Professor Marc Genton, and the Environmental Statistics (ES) group led by Professor Ying Sun. His primary role is to bridge advanced parallel linear algebra (LA) innovations with high-performance computing (HPC) in the spatial statistics field in the context of climate and weather applications.

Dr. Abdulah was honored with the ACM Gordon Bell Prize for Climate Modelling in November 2024. His team's pioneering work in climate simulation set new benchmarks in computational efficiency and resolution, transforming how climate data is modeled and analyzed. He was also part of the KAUST team nominated for the ACM Gordon Bell Prize in the general track for spatial data modeling/prediction in 2022.

He has significantly contributed to scalable matrix computations, particularly in designing numerical libraries that leverage modern hardware architectures. His expertise includes mixed-precision matrix computations, geostatistical modeling, and prediction. He has also developed cutting-edge methodologies for accelerating data-intensive simulations, enabling transformative weather/climate modeling advancements.

As a passionate advocate for open-source software, Dr. Abdulah is actively involved in collaborative research and software development, sharing tools and libraries that empower researchers globally. His work is driven by a commitment to innovation and interdisciplinary collaboration, harnessing the power of HPC to tackle some of the most pressing challenges in computational science.

Sameh's research includes HPC, large-scale spatial and spatio-temporal statistics, GPU acceleration, and mixed-precision and low-rank algorithms for AI-enabled scientific applications, with a particular focus on climate and environmental modeling. He is a core developer of ExaGeoStat, an open-source exascale geostatistical software framework widely used by the community for large-scale spatial modeling and benchmarking on modern supercomputers.

Adding the HPC capabilities to existing science is a big challenge. Statistics has a huge number of tools and methods that can be more attractive if they scaled up. Dr Abdulah is doing this by working through two different groups to transfer knowledge and experience between two different views of the same problem. In other words, he is moving the traditional statistical tools and methods to the HPC era.

Silvio Giancola is a Research Scientist at KAUST, where he leads initiatives at the intersection of computer vision, artificial intelligence, and sports. He co-founded the SoccerNet project, a large-scale benchmark for soccer video understanding now adopted by hundreds of research groups worldwide. His research spans video understanding, 3D perception, and sports analytics, with applications ranging from athlete biomechanics to broadcast automation. Dr. Giancola has published more than 25 peer-reviewed papers, co-authored a book on 3D camera technologies, and organized workshops and challenges at premier conferences such as CVPR. At KAUST, he plays a central role in the newly established FIFA Research Institute, collaborating with FIFA and industry partners to translate cutting-edge AI research into real-world football solutions.

Silvio is mainly interested in Computer Vision, Deep learning, Sports, and Robotics.

​Stefano Zampini earned his PhD in Computational Mathematics from the University of Milan in 2010. His work mainly focused on non-overlapping domain decomposition preconditioners of the dual-primal type (namely, BDDC and FETI-DP type methods) for solving large and sparse linear systems arising from finite elements discretizations and IsoGeometric Analysis. Before joining KAUST in 2014, he worked for the Italian Supercomputing center CINECA, with a specific interest in optimization and parallelization of oil and gas applications, and for the Italian weather forecast agency.

While a theorist by training, he spent his working career in the design and implementation of algorithms for the simulation of physical applications including electromechanical cardiology, computational fluid dynamics, electromagnetics, geophysics, chemistry, isogeometric analysis, fractional diffusion, and PDE constrained optimization. His contributions to the field of Domain Decomposition have been recognized by two plenary talk invitations at the sesquiannual International Conference on Domain Decomposition Methods.

Dr Zampini research interests revolve around the solution of large scale nonlinear-equations such as those arising in the solution of partial differential equations and optimization problems, He is one of the principal developers of the Portable and Extensible Toolkit for Scientific Computing (PETSc), which is a R&D 100 award-winning massively parallel framework for the solution of large scale nonlinear system of equations. His contributions to the open-source software community for Computational Science and Engineering extends to widely adopted frameworks in the US Department of Energy ecosystem for the numerical solution of partial differential equations, namely the MFEM and deal.II libraries for finite-element based simulations. He is also a member of the HPC technical committee of the CFD software package OpenFOAM.

Tien Khee Ng (Fellow of IET and InstP, Senior Member of SPIE, IEEE, Optica) is a principal research scientist and a laboratory-operation-manager of the Photonics Laboratory at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He co-established the Photonics Laboratory (2009-) and KACST Technology Innovation Center on Solid-State Lighting at KAUST (2013-2021), and currently focuses on molecular-beam-epitaxy-grown group-III-oxides photonics/electronics and quantum-devices. He was a research fellow and a member-of-technical-staff at Nanyang Technological University and Tinggi Technologies (Singapore), respectively, developing group-III-arsenide, -phosphide, -antimonide and - nitride semiconductor nanostructures and optoelectronic devices. He was an associate editor of digital Encyclopedia of Applied Physics (July 2018–June 2023).

Semiconductor photonics; Multistack membrane semiconductor; Hardware-rooted secure photonic devices; Optoelectronic devices for optical wireless communication; Molecular beam epitaxy

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.

Matthew Sainsbury-Dale is a Research Scientist in the Statistics Program at King Abdullah University of Science and Technology (KAUST), where he works in the Extreme Statistics (XSTAT) research group led by Associate Professor Raphaël Huser. His research sits at the interface of statistics and machine learning, with a focus on spatial and spatio-temporal statistics and the use of deep learning to make fast, simulation-based parameter inference in complex statistical models.

Matthew places a strong emphasis on software that makes new methods available in practice. He is the lead developer of the package NeuralEstimators, available in Julia and R for efficient simulation-based inference using neural networks. He is also a co-developer of the R package FRK for modeling big spatial and spatio-temporal data.

Matthew obtained his Bachelor of Mathematics (Honours First Class) in 2019 and his Ph.D. in Statistics in 2024 from the University of Wollongong (UOW), Australia, under the supervision of Associate Professor Andrew Zammit-Mangion and Distinguished Professor Noel Cressie. His thesis, "Efficient Inference for Spatial and Spatio-Temporal Statistical Models Using Basis-Function and Deep-Learning Methods", received the Examiners Commendation for Outstanding Thesis and the Faculty of Engineering and Information Sciences Postgraduate Thesis Award. The Statistical Society of Australia recognized his doctoral work with three awards: the JB Douglas Award for Excellence in Postgraduate Research, the Tjanpi Award for Best Student Paper in Environmental Statistics, and the Venables Award for New Developers of Open Source Software for Data Analytics.

Matthew Sainsbury-Dale's research focuses on statistical deep learning, simulation-based inference, spatial and spatio-temporal statistics, and the development of efficient statistical software.

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.

Zhang Zhiwen investigates the long-time behavior and stability of complex kinetic systems, particularly focusing on the phenomenon of Landau damping. He extends the theoretical understanding of Landau damping into 2-species contexts. His research provides critical insights into the stability and dispersive properties of kinetic models, advancing the broader understanding of how these intricate physical systems evolve and stabilize over time.

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.

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

Andres Villa received a bachelor's in Electronic Engineering at Universidad del Norte in Barranquilla, Colombia, in 2017. Moreover, he received his PhD in Computer Science at Pontifical Catholic University in 2023 in Santiago, Chile. Before joining KAUST, Andres was a teaching assistant for undergraduate and graduate students in the Engineering department at Pontifical Catholic University in Chile from 2019 until 2024. He was teaching subjects such as Deep Learning and AI Applications (VQA, Image Captioning, and Video Understanding). Andres was a Research Intern at IVUL from June 2021 to June 2022.

Andres focuses on Computer Vision, Continual Learning, Video Understanding, and Visual Language Models.