X + AI

Are you interested in applying Machine Learning and Artificial Intelligence methods to X, where X is some other field of your expertise, e.g., healthcare, materials science, chemistry, biology, drug design, physics? Are you interested in working with KAUST faculty on X+AI? Would you even consider applying for a KAUST position in X+AI? If so, then this website is for you. It lists KAUST Faculty from a variety of scientific X disciplines other than AI who are already experimenting with AI and Machine Learning in their own specializations, briefly summarized in their respective summaries. 

A typical KAUST X+AI position (e.g., faculty, postdoc) could be partially financed by KAUST’s X department and by the KAUST AI Initiative. To potential applicants: please first find a professor below who’d like to work with you, and we’ll go from there! 


Associate Professor, Mechanical Engineering
aamir.farooq [at] kaust.edu.sa
Aamir Farooq is a Professor of Mechanical Engineering, affiliated with the Clean Combustion Research Center (CCRC) at KAUST. Professor Farooq’s group focuses on high-precision spectroscopy, laser-based sensors and fuel chemistry. Miniaturized semiconductor lasers are used to design highly sensitive and selective sensors that are portable and can be applied in industrial settings. They apply ML/AI methods to predict physical and chemical properties of fuels from molecular spectroscopic data. They have also used ML/AI to do spectral denoising and multi-species sensing. Professor Farooq’s group also develops laser-based sensors for biomedical and environment–monitoring applications.
Principal Investigator,
andrea.fratalocchi [at] kaust.edu.sa
Andrea Fratalocchi is an Associate Professor of Electrical and Computer Engineering (ECE) and Affiliate Professor of Applied Mathematics and Computational Science (AMCS). He is one of the founders of the Division, the Principal Investigator of the Primalight Lab (www.primalight.org), and the cofounder of Pixeltra (www.pixeltra.com). Andrea Fratalocchi has outstanding experience studying complex systems to engineer technologies with advanced functionalities. His research advances energy harvesting, material science, and artificial intelligence hardware for machine vision and sensing applications.
deanna.lacoste [at] kaust.edu.sa
Deanna Lacoste is an Associate Professor of Mechanical Engineering, in the PSE division, at KAUST, affiliated with the Clean Combustion Research Center. Her research focus on combustion of carbon free fuels, non-equilibrium plasma physics, and plasma-assisted combustion. She is using AI to solve some of the difficult problems found in industrial combustion systems such as gas turbine engines. For example, she is currently using AI to develop new strategies of control of thermoacoustic instabilities, or to extract more information from measurements signals such as optical emission spectroscopy or flame imaging.
david.keyes [at] kaust.edu.sa
David Keyes is Professor of Applied Mathematics and Computational Science (AMCS) and the Director of the Extreme Computing Research Center (ECRC). He served as founding Dean of the CEMSE Division at KAUST from 2009-2012. He is also an Adjunct Professor and former Fu Foundation Chair Professor of Applied Physics and Applied Mathematics at Columbia University, and a faculty affiliate of several laboratories of the U.S. Department of Energy.
Assistant Professor, Computer Science
di.wang [at] kaust.edu.sa
Di Wang is an Assistant Professor of Computer Science and the Principal Investigator of the KAUST Privacy-Awareness, Responsibility and Trustworthy (PART) Lab. Prior to joining KAUST, he obtained his Ph.D. degree in computer science and engineering ('20) from the State University of New York (SUNY) at Buffalo, U.S.; his M.S. in mathematics ('15) from the University of Western Ontario, Canada; and his B.S. in mathematics and applied mathematics ('14) from Shandong University, China. He is currently a core member of the AI Initiative. Professor Wang's research interests include privacy-preserving machine learning, learning theory, trustworthy machine learning, AI for science, digital healthcare and bioinformatics.
diogo.gomes [at] kaust.edu.sa
Professor Gomes is interested in understanding the interplay between partial differential equations and machine learning, and its applications to a diverse range of fields such as reinforcement learning, stochastic optimization, and mean-field games. His current research focuses on the development and implementation of algorithms for non-convex optimization, based on stochastic sampling methods, as well as applications of machine learning methods to mean field game (MFG) models with common noise. In existing works, he has assessed the efficacy of this approach on price models, and established rigorous error estimates through a posteriori estimates to gauge convergence during the training process. Moving forward, Professor Gomes seeks to extend this research to other MFGs with common noise and investigate the variational structure of these models and their suitability for machine learning techniques. He is also interested in exploring the MFG interpretation of reinforcement learning, and how it may contribute to a better understanding of these problems.
Interim Director, KAUST Solar Center
frederic.laquai [at] kaust.edu.sa
Frédéric Laquai is a Professor of Applied Physics, and the Interim Director of the KAUST Solar Center. The Laquai group develops new and advances existing spectroscopic techniques to study (photo)physical processes across a wide dynamic and frequency range in photochemical transformations, photocatalytic reactions, and energy-conversion materials and devices including solar cells and light-emitting diodes that use novel organic and hybrid emerging semiconductors for light-to-electricity and electricity-to-light conversion.
​​​​​​​​​​​Associate Professor, Environmental Science and Engineering
himanshu.mishra [at] kaust.edu.sa
Himanshu Mishra is an Associate Professor of Enviromental Science and Engineering, with the Water Desalination and Reuse Center (WDRC) and the Center for Desert Agriculture (CDA) at KAUST. Himanshu’s Research Interests include harnessing biomimetics and AI to create non-wetting surfaces and global food–water security.
Assistant Professor, Chemical Engineering
gyorgy.szekely [at] kaust.edu.sa
Professor Szekely's research focuses on sustainable separations through the synergistic combination of materials science and chemical engineering. Sustainable production of chemicals, pharmaceuticals, and clean water is largely impacted by the efficiency of separation processes in product supply chains. The conventional separation processes can account for as much as 80% of the total manufacturing costs, contributing to approx. 10% of the world's energy consumption. In particular, the group's research investigates the potential of advanced membrane materials for efficient purification and sustainable processing of fine chemicals and water. They use applied machine learning and deep learning techniques to bridge the gap between material science and artificial intelligence. The primary focus is to solve challenging separation problems, which are considered impossible with traditional approaches. His group also combines wet-lab and in-silico data generation with building databases used in downstream prediction tasks or generative models. They have been using machine learning related to organic solvents, solvent-resistant nanofiltration, metal–organic frameworks, and covalent organic frameworks.
hong.im [at] kaust.edu.sa
Hong G. Im is a professor of Mechanical Engineering at KAUST. Computational methods for reacting flows; direct numerical simulation; large eddy simulation; turbulent combustion modeling; high performance computing; AI-assisted accelerated simulation; internal combustion engines; spray and multi-phase flows; alternative fuels and utilization; pollutant reduction and control; combustion chemistry; combustion at extreme conditions; low grade fuel combustion; plasma and electric field effects on flames, cryogenic carbon capture.
ibrahim.hoteit [at] kaust.edu.sa
Our research involves the effective use and integration of dynamical models and observations to simulate, understand and predict the weather and the climate of the Arabian Peninsula. This involves developing and implementing oceanic and atmospheric models and data inversion, assimilation, and uncertainty quantification techniques suitable for large scale applications. We are currently focusing on developing integrated data-driven modeling systems to study and forecast the circulation and the climate of the Saudi marginal seas: the Red Sea and the Arabian Gulf, and to understand their impact on the ecosystem productivity. We have developed large historical datasets of the state of the regional ocean and atmosphere, which we are using to investigate advanced AI techniques for enhanced forecasting skills, efficient downscaling, and improved parameterizations and for addressing various applications , e.g. marine ecosystem and renewable energy.
ikram.blilou [at] kaust.edu.sa
Prof. Ikram Blilou is Professor of Plant Science at KAUST. Her research focuses on studying mechanisms regulating stem cell specification and maintenance in model plant species like Arabidopsis and tomato. She also studies strategies of adaptation of desert plants (date palm, mangroves and Sodom apple) within their native harsh environment, through analyzing their root system architecture. Some of her research questions aim to unravel how cell-cell communication though hormones signalling and protein movement regulate stem cells in the context of growth and defense against pathogens. Her team uses multidisciplinary approaches to understand these processes including fluorescence lifetime imaging, tissue culture and plant transformation, transcriptional assays, high resolution microscopy, non-invasive imaging technologies. The team implements deep learning and computer vision to analyze and quantify dynamic processes in vivo ranging from protein associations to pathogen invasion and disease detection and growth quantification.
jinchao.xu [at] kaust.edu.sa
Jinchao Xu is a Professor of Applied Mathematics and Computational Science (AMCS) at KAUST. He works on theoretical analysis, algorithmic development, and practical applications of numerical methods and machine learning for computational and data sciences, especially finite element methods, multigrid methods, and deep neural networks. In recent years, he has spent much time research of deep learning, focusing on approximation theory, deep learning model, training algorithms, and application to image classification and numerical PDEs. In particular, he has observed close connection between ReLU-DNN and linear FEM, CNN and multigrid method and developed MgNet that can outperform the corresponding CNN for various applications such as image classification. He has written a series of papers in which he solved several open problems and obtained optimal results in regard to the approximation properties of neural network functions, especially those using ReLU and its power as activation functions. In addition, he has contributed to the improvement and application of greedy algorithms to optimize neural networks for solving numerical PDEs.
Professor, Chemistry
Associate Director of KAUST Catalysis Center,
magnus.rueping [at] kaust.edu.sa
Prof. Magnus Rueping is the Associate Director of the KAUST Catalysis Center and ​a Professor of Chemical and Biological Sciences. Research interests include the development and simplification of synthetic catalytic methodology and technology and their application to the synthesis of diverse functional natural and unnatural molecules by applying new heterogeneous and homogenous ( bio-, organo-, metal-, photo- and electro-) catalysis concepts​. In addition, he is working towards the design and implementation of a Lab-of-the-Future, an automated synthesis and catalysis high throughput discovery platform which integrates science, engineering, artificial intelligence and machine learning.
mani.sarathy [at] kaust.edu.sa
Professor Sarathy's research interest is in developing sustainable energy technologies with decreased net environmental impact. A major thrust of research is improving the operation of power generation technologies. The goal of Professor Sarathy's research is study conventional and alternative fuels (e.g., biofuels, synthetic fuels, etc.), so the environmental impact of combustion systems can be reduced. He applies AI and ML techniques to study complex energy related problems such as fuel design, renewable hydrogen production, and process control and optimization in energy intensive operations.
mario.lanza [at] kaust.edu.sa
Professor Lanza’s research focuses on the development novel (beyond CMOS) hardware for the implementation of artificial neural networks. His team fabricate crossbar arrays of memristors that can be used for fast and low-power vector matrix multiplication via Kirchhoff’s law, which provides a very high parallelism and could overcome the limitations of the von Neuman computing architecture. One innovative factor of Professor Lanza’s work is that he uses two-dimensional (2D) materials to construct his crossbar arrays of memristors. Hence, his work also contributes to the introduction of 2D materials in the semiconductors industry, and he is a pioneer on the development of hybrid 2D/CMOS microchips.
Associate Director,
mark.tester [at] kaust.edu.sa
Mark Tester loves understanding how plants work, especially in tough conditions where it is hot, dry and salty. He is a professor at KAUST, and has also worked at the Universities of Adelaide and Cambridge. He co-Founded Red Sea Farms, and is now enjoying developing saltwater-based agricultural systems, to improve the sustainability of food production. AI is used as one of many tools in our programs of work, such as for using image analysis to recognize features of biological relevance (e.g. growth and development, nutritional disorders, and pests and diseases); and for analyzing large datasets (e.g. from environmental monitoring, to facilitate decision-making). However, we only use existing AI platforms, and currently do not develop new AI models or techniques.
matthew.mccabe [at] kaust.edu.sa
Matthew McCabe is a Professor of Remote Sensing and Water Security and the Director of Climate and Livability Initiative (CLI) at KAUST. Prof. McCabe’s research focuses on issues related to water and food security, climate change impacts, precision agriculture, water resources monitoring and modeling, and the novel use of technologies for enhanced Earth system observation. The research undertaken in his group combines models and observations to answer questions on the distribution, variability and exchanges of water at local, regional and global scales, as well as the interactions with vegetation. CubeSats, unmanned aerial vehicles (UAVs) and in-situ monitoring techniques are all employed to monitor terrestrial processes, while a range of modeling and statistical approaches are used to understand and predict system behavior. Improved description and understanding of the water-food nexus is a key objective of his research.​​​​
Assistant Professor, Computer Science
mohamed.elhoseiny [at] kaust.edu.sa
Mohamed Elhoseiny is an assistant professor of Computer Science at the Visual Computing Center (VCC) and a Core Member of the AI Initiative in King Abdullah University of Science and Technology (KAUST). Since Fall 2021, he has become a senior member of IEEE and a member of the international Summit community. Previously, he was a visiting Faculty at Stanford Computer Science department (2019-2020), Visiting Faculty at Baidu Research (2019), Postdoc researcher at Facebook AI Research (2016-2019). Dr. Elhoseiny did his Ph.D. in 2016 at Rutgers University where he was part of the art & AI lab and spent time at SRI International in 2014 and at Adobe Research (2015-2016). His primary research interest is in computer vision and especially in efficient multimodal learning with limited data in zero/few-shot learning and Vision & Language. He is also interested in Affective AI and especially to understand and generate novel visual content (e.g., art and fashion). He received an NSF Fellowship in 2014, the Doctoral Consortium award at CVPR’16, best paper award at ECCVW’18 on Fashion and Design. His zero-shot learning work was featured at the United Nations, and his creative AI work was featured in MIT Tech Review, New Scientist Magazine, Forbes Science, and HBO Silicon Valley. Overall, Dr. Elhoseiny has published more than 53 publications in top research venues and he has served as an Area Chair at major AI conferences, including CVPR21, ICCV21, IJCAI22, ECCV22,ICLR23, CVPR23, and organized CLVL workshops at ICCV’15, ICCV’17, ICCV’19, and ICCV’21.
nazek.elatab [at] kaust.edu.sa
Nazek El-Atab is an Assistant Professor of Electrical and Computer Engineering and the Principal investigator of the Smart, Advanced Memory devices and Applications lab (SAMA) at King Abdullah University of Science and Technology, Saudi Arabia. Her research interest focuses on the design and fabrication of smart memory and electronic devices and their applications in AI including neuromorphic computing.
noreddine.ghaffour [at] kaust.edu.sa
The current desalination technologies are energy-intensive compared to the thermodynamic limits, despite recent technical improvements. Furthermore, membrane technology development is hampered by low recovery and membrane (bio)fouling. My research work focuses on: i) reducing the cost and energy requirements of desalination for water supply sustainability; ii) improving the performance of the existing desalination technologies; iii) development of chemical-free membrane cleaning techniques; iv) digitalization of desalination systems for early (bio)fouling detection; and v) developing innovative energy-efficient desalination processes which could be driven by low-grade renewable energy in an integrated system.
Associate Director, Visual Computing Center
Principal Investigator, Peter Wonka Research Group
peter.wonka [at] kaust.edu.sa
Peter Wonka is a professor of Computer Science Program (CS) at King Abdullah University of Science and Technology (KAUST). He is also the Interim Director of the Visual Computing Center (VCC) and a Core member of the AI Initiative at KAUST. Professor Wonka's main research interests lie in deep learning, computer vision, and computer graphics. His work in deep learning spans a wide range of topics analyzing visual data, such as images, point clouds, volumes and 3D surface models. Examples of recent work are 3D reconstruction, image segmentation, depth estimation, unsupervised learning, generative modeling, domain transfer, and synthetic data generation for deep learning.
qiaoqiang.gan [at] kaust.edu.sa
Qiaoqiang Gan is a professor of Material Science and Engineering in KAUST. Gan's research focuses on applying the power of nanomaterial and structure processing and engineering for investigating fundamental light-matter interaction within extreme dimensions, developing advanced manufacturing methods, and demonstrating smart biomedical sensing, energy and environmental sustainability applications enabled by these unique optical phenomena and advanced machine learning algorithms. In particular, his research activities aimed to bridge the gaps between fundamental investigation, application development and technology transfer.
Interim Associate Director, Computational Bioscience Research Center
Associate Professor, Computer Science
Principal Investigator, Bio-Ontology Research Group
robert.hoehndorf [at] kaust.edu.sa
Robert is an Associate Professor in Computer Science and the PI of the Bio-Ontology Research Group (BORG). Prior to joining KAUST, Robert held research positions at the Max Planck Institute for Evolutionary Anthropology, the European Bioinformatics Institute, University of Cambridge, and Aberystwyth University. He earned his PhD degree in Computer Science from the University of Leipzig. Robert's research focuses on the development and application of knowledge-based algorithms in biology and biomedicine. In bioinformatics, Robert focuses on bio-ontologies and integration of massive datasets, protein function prediction, metagenomics, pathogen informatics, systems biology, and investigating genotype-phenotype relations. In computer science, his interests lie in Semantic Web technologies and neuro-symbolic methods that combine formal logic and machine learning. Robert has published over 150 papers in journals and international conferences. Robert is Editor in Chief of the Journal of Biomedical Semantics and Associate Editor of BMC Bioinformatics, Applied Ontology, and PLoS ONE, and regularly organizes and co-organizes workshops and conferences in the area of bioinformatics, data integration, and bio-ontologies.
shinkyu.park [at] kaust.edu.sa
Shinkyu Park is an Assistant Professor of Electrical Engineering and the PI of Distributed Systems and Autonomy Lab. Park’s research group focuses on learning, planning, and control in multi-robot systems. They aim to build individual robots’ core capabilities of sensing, actuation, and communication and to train them to learn the ability to work as a team and attain high-level of autonomy in distributed information processing, decision making, and manipulation. The group applies ML/AI methods to process sensor data to allow robots to perceive and interact with surrounding environments, and to implement computational models for robot decision making demonstrating high-level autonomy and social behavior. They are applying research outcomes to applications in environmental monitoring, factory automation, and service robotics.
Interim Associate Director of KAUST Catalysis Center,
stefaan.dewolf [at] kaust.edu.sa
Stefaan De Wolf is a Professor of​ Material Science and Engineering, and the Interim Associate Director for the KAUST Solar Center. ​Stefaan De Wolf's expertise lies in the science and technology of photovoltaics for terrestrial applications. His research focuses on the fabrication of high-efficiency silicon-and perovskite based solar cells, with specific attention to the fundamental understanding of interface structures and electrical contact formation, relevant to solar cells and electronic devices in general. He is also interested in new device architectures and applications, such as multi-junction solar cells, aimed at the improved utilization of the full solar spectrum for electricity generation, and the development of photovoltaic solutions for hot and sunny climates. He is also interested in new device architectures and applications, such as multi-junction solar cells, aimed at the improved utilization of the full solar spectrum for electricity generation, the development of photovoltaic solutions for hot and sunny climates, as well as exploring machine-learning methods to accelerate photovoltaic research.
volker.vahrenkamp [at] kaust.edu.sa
Volker Vahrenkamp is a Professor of Carbonate Reservoirs Studies, with the Ali Naimi Petroleum Engineering Research Center (ANPERC) at KAUST. Professor Vahrenkamp‘s research aims at securing future energy supply with three main thrusts: i) Refine the understanding of modern and ancient carbonate depositional environments and associated diagenesis for improved perception of reservoir heterogeneities on multiple scales. ii) Advance the quantification of the complex pore networks of Arabian carbonate reservoirs and its impact on reservoir performance. Iii) Explore and develop geothermal energy in Arabian countries with special focus on desalination and cooling applications and suitable drilling techniques. Dr. Vahrenkamp leads the Carbonate Reservoirs Studies research group.
Principal Investigator, Computational Imaging Group
wolfgang.heidrich [at] kaust.edu.sa
Wolfgang Heidrich is a Professor of SC and ECE, and a member of the KAUST Visual Computing Center (VCC). His research is in Computational Imaging and Display, which ic concerned with the hardware-software co-design of camera and display devices. which aims to optically encode information about the real world in such a way that it can be captured by image sensors. The resulting images represent detailed information such as scene geometry, motion of solids and liquids, multi-spectral information, or high contrast (high dynamic range), which can then be computationally decoded using machine learnign and AI techniques to interpret the raw measurements and reconsturct the encoded intrinsic information. A particular emphasis in on the end-to-end design of such systems, in which the optical hardware design is "learned" at the same time as the reconstruction techniques.
xin.gao [at] kaust.edu.sa
Dr. Xin Gao is a Professor of Computer Science, Interim Director of Computational Bioscience Research Center (CBRC), Deputy Director of Smart Health Initiative (SHI), Lead of the Structural and Functional Bioinformatics Group at KAUST, and a core member of the AI Initiative at KAUST. Prof. Gao's group works on the intersection between computer science and biology. In the AI side, they work on developing theories and methods for deep learning, graphical models, kernel methods, matrix factorization, optimization and graph algorithms. In the application side, they collaborate closely with experimental scientists to develop novel computational methods to solve key open problems in biology and medicine. The biological problems they are working on range from analyzing biomolecular sequences to determining their 3D structures to annotating their functions and understanding and controlling their behaviors in complex biological networks. His group also works on methodology development for a wide range of medicine and healthcare problems.
Associate Professor, Statistics
Principal Investigator, Environmental Statistics
ying.sun [at] kaust.edu.sa
Associate Professor Ying Sun is leading a research group on environmental statistics. Most of Professor Sun's research centers on developing statistical models and methods as well as machine learning/deep learning algorithms for complex data to address important environmental problems. Her scientific research has contributed greatly to spatio-temporal statistics and environmental applications. She has also greatly contributed to the development of artificial intelligence-driven techniques for time-series forecasting, anomaly detection, and process monitoring. The developed techniques are utilized in many applications to improve the operation of various renewable energy systems, healthcare systems, and environmental processes. She is the co-author of two books: "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches: Theory and Practical Applications" (Elsevier, 2020), and Advanced Road Traffic Modelling and Management Using Statistical and Deep Learning Methods, (Elsevier, 2021).