Research Groups
Applied Mathematics and Computational Sciences (AMCS)
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The Advanced Algorithms and Numerical Simulations Lab (AANSLab) focuses on developing stable and efficient high-order numerical methods for solving hyperbolic and mixed hyperbolic-parabolic PDEs, addressing multi-scale industrial flow problems, and leveraging advanced computing architectures to handle complex simulations.
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The Applied Partial Differential Equations Group focuses on mathematical modeling, analysis and the development of numerical methods for evolutionary partial differential equations in fluid and solid mechanics.
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The TREES research group, led by Prof. Mikhail Moshkov, focuses on extensions of dynamic programming, machine learning and data mining, discrete optimization, and applied healthcare analytics.
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Led by Prof. Miguel Urbano, the FBIP research group specializes in free boundary problems, focusing on applications like ocean-atmosphere interactions, porous media flows, fire propagation, and financial option pricing. The group studies weak solutions and the regularity and geometry of associated interfaces.
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Led by Prof. David Keyes, HiCMA at KAUST focuses on optimal-complexity algorithms for high-intensity computations. Their software toolkit supports optimization applications like matrix-free methods and is integrated into major vendor libraries.
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The MMDE group focuses on the modeling and analysis of PDEs that describe phenomena in biological growth, mechanical engineering, and fluid dynamics. Key areas of research include well-posedness, existence and nonexistence of solutions, and qualitative properties. The group employs methods from PDE theory, calculus of variations, operator semigroup theory, and geometric measure theory.
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Professor Gomes's Mean-field Games and Nonlinear PDE Research Group focuses on partial differential equations, including viscosity solutions and mean-field models, with applications in population modeling, price formation, and computer vision.
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Led by Prof. Gabriel Wittum, the Modelling and Simulation research group focuses on a general approach to modelling and simulation of problems from empirical sciences, in particular using high performance computing (HPC).
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Led by Prof. David I. Ketcheson, the Numerical Mathematics research group focuses on designing, analyzing, and implementing numerical methods for ordinary and partial differential equations, with applications in nonlinear wave propagation.
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Led by Prof. Daniele Boffi, the Numerical Methods for PDEs research group focuses on numerical approaches grounded in rigorous mathematical analysis of approximation schemes, including well-posedness, stability, and convergence, alongside numerical validation of theoretical results.
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Led by Omar Knio, the O-Knio research group develops advanced methods and algorithms for simulating complex multiscale systems, focusing on applications such as renewable energy systems, uncertainty quantification, Bayesian inference, computational fluid mechanics, turbulent flows, and optimization under uncertainty. The group's work spans diverse areas, including combustion, oceanic and atmospheric dynamics, microfluidic devices, and data-enabled predictive science.
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Led by Prof. Peter Markowich, the P-Markowich research group focuses on the mathematical and numerical analysis of partial differential equations (PDEs) and their applications in physics, biology, and engineering.
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Professor Krause's research group focuses on numerical simulation, machine learning, and optimization, designing efficient algorithms for large-scale problems on supercomputers like KAUST’s Shaheen III, with applications in medicine and geology.
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Professor Jinchao Xu's research group focuses on developing and analyzing fast methods for finite element discretization, large-scale equation solutions, and deep learning, with applications in scientific computing and big data.
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Led by Prof. Raul F. Tempone, the Stochastic Numerics (Stochnum) research group focuses on developing efficient and robust numerical methods for solving problems involving stochastic models and differential equations in engineering and the sciences through numerical analysis.
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Led by Prof. Ying Wu, the Waves in Complex Media (WCM) research group focuses on multidisciplinary areas related to waves, spanning mathematics, material sciences, theory, simulation, modeling, and algorithms.
Computer Science (CS)
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Accelerated Connected Computing Lab focuses on hardware acceleration and connected computing, exploring how innovation at this intersection can enable more efficient, performant, and secure systems
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The Adaptive Machine Learning (AdaML) focuses on practical and theoretical machine learning, with key interests in online learning, optimization, statistical learning theory, and developing "parameter-free" algorithms that eliminate the need for hand-tuned parameters.
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The Bio-Ontology Research Group uses bio-ontologies for integrating and analyzing biological data across scales, with applications in disease research, personalized medicine, phenotype prediction, and biodiversity.
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The Computational Imaging Group (VCCIMAGING), led by Professor Wolfgang Heidrich, focuses on computational imaging and display, combining methods from computer graphics, machine vision, and optics to develop advanced sensing and display technologies. The group's key approach is hardware-software co-design to create high-performance imaging systems.
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The Computational Sciences Group (CSG) at KAUST develops efficient methods for the accurate simulation of natural phenomena, processes, and technical procedures to solve practically relevant problems in scientific and visual computing.
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The Computer Vision, Core AI Research Group (Vision-CAIR), led by Professor Mohamed Elhoseiny, focuses on computer vision and creative AI, with applications in imagination-inspired vision, affective language for visual art, and biodiversity research.
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The Cyber Security and Resilience Technology (CyberSaR) research group aims to build cutting-edge technologies to redefine Cyber Security and Resilience in theory and practice. This is done through a unique mix of expertise that is essential to revolutionize Cyber Security and Resilience by Design, namely: Secure Distributed / Decentralized systems, Hardware Security (TEE, FPGA, GPU, DPU, ...), and Trustworthy AI. The group embraces applied research that leaves a real impact on society, both national and international. Application areas of interest are SOC, Automotive, New Space, Fintech, and others.
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Led by Prof. Markus Hadwiger, the High Performance Visualization Group (VCCVIS) focuses on visualizing extreme-scale data, with expertise in volume and flow visualization, large-scale image processing, GPU algorithms, and interactive techniques for scientific computing.
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KAUST Assistant Professor of Computer Science Jian Weng actively seeks to reform software and hardware interfaces to improve computer systems' energy efficiency and performance.
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Professor Schmidhuber is a founding leader in artificial intelligence (AI) and machine learning. At KAUST, he leads and works with many current faculty members with research interests in AI. He spearheads the research focus of the Univeristy’s AI Initiative; the Initiative focuses on AI applications in all fields, including health care, drug design, chemistry, materials science, speech recognition and natural language processing, automation, robotics, soft robotics and other areas.
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The Nanovisualization Research Group, led by Professor Ivan Viola, focuses on developing next-generation computer graphics and visualization technologies to depict life forms across all scales, from atoms to organisms.
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The Networking Lab Research Group, led by Professor Basem Shihada, focuses on networking and distributed systems, end-to-end QoS, security, and resource management in cloud and autonomous computing, with key projects in high-speed networks, wireless full-duplex LAN design, software-defined networking, IoT architecture, and IoT security.
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Professor Peter Richtárik's Optimization and Machine Learning Research Group focuses on developing algorithms for large-scale optimization, machine learning, and high-performance computing, with an emphasis on randomized, parallel, and distributed methods.
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Professor Peter Wonka's research group focuses on computer graphics, deep learning, and computer vision, with a strong emphasis on modeling and analyzing urban and geospatial data, as well as developing generative models, 3D reconstruction techniques, and neural fields for visual computing applications.
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Led by Di Wang, the Privacy Awareness, Responsibility and Trustworthy Lab (PART) research group.
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Professor Di Pietro's research group focuses on AI-driven cybersecurity, privacy and security for distributed systems, including UAVs, Blockchain, Cloud, and IoT. Key areas include applied cryptography, FinTech, Quantum Computing, and data science, with an emphasis on critical infrastructure protection, online social networks (OSN), and cloud security.
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Led by Prof. Marc Dacier, the Security Research Bearing Experimental Results (SeRBER) research group activities are mostly centered around network security problematics and, as the name of the group implies, the emphasis is put on the experimental validation of the novel solutions derived from our research.
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The Software-Defined Advanced Networked and Distributed Systems (SANDS) research group, led by Professor Marco Canini, develops techniques and algorithms to build scalable, dependable, and deployable network systems, focusing on improving the modern computing environment where distributed systems and networks are integral components.
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Led by Professor Xin Gao, the Structural and Functional Bioinformatics (SFB) research group focuses on bioinformatics, computational biology, machine learning, and big data, developing algorithms and techniques for protein sequence analysis, 3D structure determination, and functional prediction to address key biological challenges.
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Led by Prof. Panagiotis Kalnis, InfoCloud specializes in advanced information management for expansive infrastructures like clusters, supercomputers, GPUs, and the Cloud. CLOUD research spans extremely large databases, Cloud Computing, scientific data, graphs (including RDF), very long strings, parallel and distributed systems, data mining, knowledge extraction, and Bioinformatics.
Electrical and Computer Engineering (ECE)
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ASL is a group of motivated and curious researchers working on cutting-edge interdisciplinary wide bandgap semiconductor research.
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Led by Prof. Eric Feron, the Aerospace and Transportation Systems Research Group focuses on robotics and embedded intelligence, exploring advanced autonomous systems for complex real-world applications.
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The Communication and Computing Systems Lab (CCSL) at KAUST is led by Professor Ahmed Eltawil. The research focus of the group lies in the area of efficient architectures for mobile computing and communications systems. Our philosophy is to employ a multidisciplinary approach to the design and development of mobile systems spanning algorithm, architecture and implementation
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The Communication Theory Lab (CTL) research is focusing on the modeling, design, optimization, and performance analysis of wireless communication systems with current emphasis on global connectivity, spectrum sharing, optical wireless communications, and underwater/maritime wireless communication.
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The KAUST Computational Electromagnetics Research Group, led by Professor Hakan Bagci, specializes in developing computational methods for electromagnetics, focusing on wave propagation, antenna design, and electromagnetic compatibility, with applications in communication systems, radar, and biomedical devices.
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The Distributed Systems and Autonomy (DSA) research group, led by Professor Shinkyu Park, explores fundamental research questions in multi-agent systems, robotics, and control systems. The group focuses on creating novel models and computational methods for multi-agent coordination and developing robotic and control systems for monitoring real-world environments.
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The Image and Video Understanding Lab (IVUL) at KAUST focuses on solving research problems in computer vision, including activity recognition, object tracking, scene understanding from 3D data, and image annotation, using machine learning and optimization techniques.
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Led by Prof. Al-Naffouri, the Information Science Lab, focuses on adaptive, sparse, and statistical signal processing, along with their diverse applications.
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The Integrated Intelligent Systems Lab (I2S), led by Professor Gianluca, focuses on multidisciplinary research in areas such as nonlinear circuits, statistical signal processing, electromagnetic compatibility, biomedical systems, IoT node design, and machine learning for anomaly detection and predictive maintenance.
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Led by Prof. Atif Shamim, the Integrated Microwave Packaging Antennas and Circuits Technology (IMPACT) lab is focused on cutting-edge research in innovative antenna designs, RF circuits, and sensors which are then realized and integrated through additive manufacturing techniques to demonstrate flexible, wearable and disposable wireless sensors targeting various biomedical, environmental and industrial applications.
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The Integrated Photonics Lab (IPL), led by Professor Yating Wan, focuses on silicon photonics, particularly the integration of on-chip light sources for short-reach communication links, with additional applications in biosensing, energy harvesting, and quantum information processing.
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The Integrated Telecommunication and Sensing Systems (ITASS) lab at KAUST focuses on the design and development of high-frequency integrated circuits and systems that enable next-generation communication and sensing technologies. Our research spans scalable phased-array transceivers for ultra-high-speed data links, high-resolution radar systems for industrial and autonomous platforms, and energy-efficient architectures for advanced wireless networks. By addressing fundamental challenges in high-speed circuit design and system integration, the lab aims to deliver practical, high-performance solutions for both wireless and wired applications.
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Led by Prof. Shehab Ahmed, the Mechatronics and Energy Systems Research Group (MERGE) develops novel software and electromechanical systems through synergetic integration of physical modeling, mechanics, electronics and computing technologies.
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Led by Boon Ooi, the Photonics Laboratory aims at delivering compact and energy saving integrated laser-diode based devices and solutions for applications requiring light spanning the ultraviolet to the visible and near-infrared regime.
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The Primalight Laboratory Research Group, led by Professor Andrea Fratalocchi, focuses on developing advanced photonic devices and materials inspired by the complexity of natural systems, with research interests in photonics, energy harvesting and imaging technologies.
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Led by Prof. Charalambos Konstantinou, the Secure Next Generation Resilient Systems Lab (SENTRY) focuses on cyber-physical energy systems, cybersecurity, resilience, embedded IoT devices, renewable energy integration, and real-time simulation.
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The Sensors Research Group, led by Professor Khaled Salama, focuses on interdisciplinary aspects of electronic circuit design and semiconductor fabrication, developing cost-effective analytical platforms for industrial, environmental, and biomedical applications, with recent work on neuromorphic circuits for brain emulation.
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The Smart, Advanced Memory Devices and Applications Lab (SAMA), led by Professor Nazek El-Atab, focuses on the design, simulation, and fabrication of advanced memory devices for in-memory computing and sensing, aiming to enhance scalability, power consumption, speed, retention, and endurance through innovative architectures and materials.
Statistics (STAT)
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The Bayesian Computational Statistics and Modeling Research Group, led by Professor Håvard Rue, focuses on computational Bayesian statistics and methodology, developing practical tools like R-INLA for approximate Bayesian analysis of latent Gaussian models, with applications including spatial statistics using stochastic partial differential equations.
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Professor Filippone's current research interests include the development of tractable and scalable Bayesian inference techniques for Gaussian processes and Deep Learning models, with applications in life and environmental sciences.
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The KAUST Biostatistics Group develops novel statistical methods and models for biological processes with complex dependence structures.
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Led by Prof. Ying Sun, the Environmental Statistics research group is dedicated to developing statistical models and methods for space-time data to solve important environmental problems. We focus on statistical inferences of spatio-temporal processes, including developing informative graphical tools for complex space-time datasets, building realistic models for natural spatio-temporal processes and finding computationally efficient methods for estimating and assessing the fit of such models.
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Led by Prof. Raphaël Huser, the Extreme Statistics (XSTAT) research group develops specialized statistical models for low-probability, high-impact extreme events. These models are designed for effective tail extrapolation and the assessment of future, potentially more extreme, risks.
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Led by Prof. Paula Moraga, the GeoHealth research group is primarily focused on the development of frontier geospatial methods and computational tools to solve challenging problems in public health and make a positive impact on the world.
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Led by Prof. Marc Genton, the Spatio-Temporal Statistics and Data Science (STSDS) research group focuses on the statistical analysis, modeling, prediction, and uncertainty quantification of spatio-temporal data, with applications in environmental and climate science, renewable energies, geophysics, and marine science.
Spatio-Temporal Statistics and Data Science (STSDS)
machine learning spatio-temporal statistics data science Spatial extremes geostatistics large datasets non-Gaussian random fields copulas multivariate spatial statistics forecasting solar power wind power multivariate analysis data analysis visualization skew-elliptical distributions Robustness data assimilation data mining
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Led by Prof. David Bolin, the Stochastic Processes and Applied Statistics group develops methodology for statistical models involving stochastic processes and random fields. A main focus is the development of statistical methods based on stochastic partial differential equations.