MBZUAI is now ranked 24th globally in AI, computer vision, machine learning, and natural language processing, according to CSRankings. This ranking is attributed to the addition of faculty like Preslav Nakov, Hanan Al Darmaki, and Samuel Horvath. MBZUAI now ranks ahead of universities like the University of Michigan and Imperial College London in specific AI fields. Why it matters: This ranking establishes MBZUAI as the top CS institution in the Arab World and highlights the UAE's growing prominence in AI research.
MBZUAI has been ranked 127th globally among institutions conducting computer science research and ranks 30th globally in AI, computer vision, machine learning, and NLP. This places MBZUAI ahead of universities such as the University of Michigan, Georgia Tech, Imperial College London and others. MBZUAI is now the top-ranked CS institution in the Arab World and the Middle East and Africa according to CSRankings. Why it matters: This ranking highlights the rapid growth and increasing prominence of AI research in the UAE and the broader Middle East.
MBZUAI is now ranked 19th globally in AI, computer vision, machine learning, and natural language processing according to CSRankings. The university is also top-ranked for computer science in the MENA region. MBZUAI's faculty has grown significantly, with more than 20 new members added in 2022 and Q1 2023, totaling 52. Why it matters: This ranking highlights the rapid rise of MBZUAI as a leading AI research institution in the Middle East, contributing to the UAE's ambitions to become a global AI hub.
MBZUAI faculty, researchers, and students will present 34 papers at the 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). Fahad Khan is a co-author on 11 accepted papers, while Salman Khan and Shijian Lu have 10 and 9 papers, respectively. One paper focuses on person image synthesis via a denoising diffusion model, and another introduces PromptCAL for generalized novel category discovery. Why it matters: This large volume of acceptances at a top-tier conference highlights MBZUAI's growing prominence and research contributions in computer vision, with potential impact across various industries from online retail to autonomous driving.
MBZUAI researchers release JEEM, a new benchmark dataset for evaluating vision-language models on Arabic dialects. The dataset covers image captioning and visual question answering tasks using images from Jordan, UAE, Egypt, and Morocco. Results show models struggle with cultural understanding and relevance despite fluent language generation.
Dubai Police has stated that deepfake technology is becoming increasingly sophisticated. The force highlighted the growing difficulty in accurately detecting and distinguishing deepfake content from genuine material. This development poses significant challenges for law enforcement agencies in combating misinformation and digital crime. Why it matters: This official statement from a major regional law enforcement body underscores the urgent need for advanced detection technologies and robust public awareness to address the evolving threat of AI-generated malicious content.
AI technologies are increasingly being integrated into policing and public safety operations across the UAE to enhance efficiency and capabilities. These applications include predictive analytics for crime prevention, advanced surveillance systems using computer vision, and optimized resource deployment for emergency response. The aim is to leverage data and automation to create safer environments and improve response times for law enforcement agencies. Why it matters: This trend signifies the UAE's commitment to adopting cutting-edge technology for national security and its broader smart city initiatives, positioning it as a leader in tech-driven governance.
Researchers introduced NEO-Bench, a new multi-source benchmark designed for detecting faint streaks from Near-Earth Objects (NEOs) in long-exposure astronomical images. The benchmark comprises 8,376 images sourced from five diverse astronomical datasets, notably including data from the United Arab Emirates Meteor Monitoring Network. Evaluations of four detection approaches on NEO-Bench revealed inconsistent cross-source performance, indicating that reliable generalization across different astronomical imaging sources remains an open challenge. Why it matters: This benchmark provides a standardized tool to develop more robust and generalizable AI models for astronomical streak detection, which is crucial for space domain awareness and asteroid tracking.
Dubai Police are implementing AI-powered radars to enhance traffic law enforcement across the city. These advanced systems are capable of detecting eight different types of traffic offenses automatically. The initiative aims to improve road safety and streamline the process of identifying and fining violators. Why it matters: This deployment represents a practical and significant application of artificial intelligence in public safety and smart city initiatives within the UAE, demonstrating a clear trend towards technology-driven governance and urban management.
AI technology has been successfully implemented in a mango farm located in Saudi Arabia, resulting in a significant increase in the farm's productivity. The application of AI boosted the mango flowering rate to an impressive 98%. This advancement showcases the potential of artificial intelligence to optimize agricultural processes and enhance crop yields. Why it matters: This demonstrates a tangible application of AI in improving food security and agricultural efficiency within the Middle East region.
AI technology was successfully implemented in a mango farm in Saudi Arabia. This application led to a significant improvement in the flowering rate of the mango trees, which reached 98%. The initiative demonstrates a practical and effective use of artificial intelligence in agricultural practices. Why it matters: This showcases a concrete example of AI's potential to enhance crop yield and efficiency within the Saudi agricultural sector, contributing to food security efforts.
AI technologies are being increasingly utilized to identify and classify millions of plant species globally, marking a significant advancement in biodiversity research. These systems leverage algorithms, often based on computer vision, to rapidly recognize and catalogue flora, a task traditionally requiring extensive human expertise and time. This accelerated identification process is crucial for providing comprehensive biodiversity assessments and supporting global conservation strategies. Why it matters: This application of AI offers powerful tools for environmental sustainability, a priority area for many nations, including those in the Middle East investing in technology-driven conservation efforts.
Abu Dhabi has launched a new 'free flow' paid parking system, indicating an automated approach to managing urban parking. This system is designed to streamline vehicle identification and billing processes without traditional barriers. It aims to improve traffic flow and enhance the convenience for residents and visitors in the emirate. Why it matters: This initiative demonstrates the UAE's ongoing commitment to smart city development and the integration of advanced technologies like computer vision into public services.
AI filmmaker James Gerde is utilizing Abu Dhabi as a hub for developing and testing artificial intelligence tools for film production. He is leveraging the emirate's strategic focus on future technologies and its burgeoning AI ecosystem to push creative boundaries. Gerde's work explores the integration of AI into various stages of filmmaking, from concept generation to post-production. Why it matters: This highlights Abu Dhabi's growing role as an attractive destination for global AI talent and innovation, particularly in creative industries.
A research paper co-authored by Dr. Maxim Panov and Kirill Fedyanin from the AI and Digital Science Research Center (AIDRC) has been accepted for publication at NeurIPS 2022. The paper, titled “Nonparametric Uncertainty Quantification for Single Deterministic Neural Network”, proposes a fast and scalable method for uncertainty quantification in ML models. The method disentangles aleatoric and epistemic uncertainties and was validated on text classification and image datasets including MNIST and ImageNet. Why it matters: This demonstrates the growing AI research capabilities and contributions from the UAE to the global AI community, particularly in fundamental machine learning research.
Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.
KAUST researchers developed a low-cost, AI-powered drone system to recognize and track camels, addressing challenges faced by local herders. The system uses commercial drones, cameras, and machine learning to monitor camel herds in real time without expensive GPS collars. The AI model revealed insights into camel migration patterns, showing coordinated grazing and sensitivity to drone sounds. Why it matters: This system offers an affordable solution to preserve Saudi Arabia's camel herding tradition while providing valuable insights into camel behavior and contributing to the local economy.
This paper introduces BRIQA, a new method for automated assessment of artifact severity in pediatric brain MRI, which is important for diagnostic accuracy. BRIQA uses gradient-based loss reweighting and a rotating batching scheme to handle class imbalance in artifact severity levels. Experiments show BRIQA improves average macro F1 score from 0.659 to 0.706, especially for Noise, Zipper, Positioning and Contrast artifacts.
KAUST and the National Center for Wildlife (NCW) are collaborating on research to protect Saudi Arabia's coastal ecosystems and marine economy from invasive species. They are conducting biodiversity surveys along the Red Sea and Arabian Gulf coasts, having surveyed 34 sites and collected over 10,000 samples. So far, 200 species with potential marine invasive traits have been identified, expanding the national knowledge base of marine life. Why it matters: The partnership aims to develop early detection and monitoring systems, fortifying Saudi Arabia's marine biosecurity efforts and supporting its Vision 2030 blue economy goals.
KAUST held its Rising Stars in AI Symposium 2025, organized by the Center of Excellence for Generative AI, from April 7-10. The symposium hosted 25 emerging researchers to present their work in generative AI, machine learning, CV, and NLP. KAUST leadership emphasized the university's commitment to AI research and its role in fostering global collaboration and innovation in the field. Why it matters: The event highlights KAUST's ambition to become a central hub for AI research and talent development in Saudi Arabia, aligning with the Kingdom's broader AI strategy.
Researchers at KFUPM have developed a system for pothole detection and characterization using a YOLOv8-seg model and depth estimation. A new dataset of images and depth maps was collected from roads in Al-Khobar, Saudi Arabia. The system combines segmentation and depth data to provide a more comprehensive pothole characterization, enhancing autonomous vehicle navigation and road maintenance.
KAUST's Urban Lab is developing green infrastructure solutions to combat the urban heat island (UHI) effect in arid climates. Researchers are focusing on using vegetation for shading and evapotranspiration, as well as increasing albedo with reflective surfaces. The team is using machine learning to analyze heat patterns and urban expansion to inform sustainable architecture and landscape design. Why it matters: This research is crucial for creating more livable and sustainable urban environments in Saudi Arabia and other regions facing desertification and water scarcity.
KAUST Beacon Development is assisting NEOM in understanding and protecting marine megafauna in the Red Sea, utilizing AI to process drone footage of habitats. Researchers are surveying areas around Sindalah to study the distribution of species like dolphins, turtles, and dugongs. This data will help reduce risks to marine life from vessel traffic and human activities. Why it matters: The partnership showcases the use of AI and drone technology for marine conservation in the region, setting a benchmark for similar efforts and highlighting NEOM's commitment to sustainable ecosystem management.
This paper proposes a smart dome model for mosques that uses AI to control dome movements based on weather conditions and overcrowding. The model utilizes Congested Scene Recognition Network (CSRNet) and fuzzy logic techniques in Python to determine when to open and close the domes to maintain fresh air and sunlight. The goal is to automatically manage dome operation based on real-time data, specifying the duration for which the domes should remain open each hour.
KAUST alumna Justine Braguy co-founded Thya Technology, an AI startup that automates image and video analysis. The company's platform allows users to upload and label images to generate AI detection models without coding. Thya Technology was born out of a tool developed at KAUST to count plant seeds and won the TAQADAM showcase in 2022. Why it matters: This highlights KAUST's role in fostering AI entrepreneurship and translating research into practical applications, particularly in automating scientific processes.
A new brain tumor segmentation method based on convolutional neural networks is proposed for the BraTS-GoAT challenge. The method employs the MedNeXt architecture and model ensembling to segment tumors in brain MRI scans from diverse populations. Experiments on the unseen validation set demonstrate promising results with an average DSC of 85.54%.
The paper introduces a framework for camel farm monitoring using a combination of automated annotation and fine-tune distillation. The Unified Auto-Annotation framework uses GroundingDINO and SAM to automatically annotate surveillance video data. The Fine-Tune Distillation framework then fine-tunes student models like YOLOv8, transferring knowledge from a larger teacher model, using data from Al-Marmoom Camel Farm in Dubai.
A KAUST-led research team is deploying DNA sequencing technology originally used on the International Space Station to analyze DNA samples in extreme environments along the Saudi Red Sea coast. The portable technology enables on-site extraction, concentration, and sequencing of samples from coral reefs and mangrove forests, minimizing DNA deterioration. Preliminary results indicate a more diverse and complex microbiome than previously found. Why it matters: This application of space-based technology to marine conservation in the Red Sea could provide critical insights into how coral reefs and mangroves adapt to climate change, informing preservation and restoration efforts.
This paper proposes a machine learning method for early detection and classification of date fruit diseases, which are economically important to countries like Saudi Arabia. The method uses a hybrid feature extraction approach combining L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features. Experiments using a dataset of 871 images achieved the highest average accuracy using Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT) classifiers.
This paper introduces a multi-task learning approach for fetal biometric estimation from ultrasound images, classifying regions (head, abdomen, femur) and estimating parameters. The model, a U-Net architecture with a classification head, achieved a mean absolute error of 1.08 mm for head circumference, 1.44 mm for abdomen circumference, and 1.10 mm for femur length, with 99.91% classification accuracy. The researchers are affiliated with MBZUAI. Why it matters: This research demonstrates advancements in automated fetal health monitoring using AI, potentially improving prenatal care and diagnostics in the region.
KAUST's Visual Computing Center had two papers recognized at IEEE VIS 2023. One paper, from Prof. Markus Hadwiger's group, introduced a new method for detecting and visualizing vortex structures in 2D fluid flows, which was recognized as one of the best papers. The second paper, from Prof. Ivan Viola's team, presented Dr. KID, a visualization framework for physicalizing biological structures into 3D-printed models, receiving an honorable mention. Why it matters: These awards highlight KAUST's contributions to cutting-edge visualization techniques with potential applications in diverse scientific and engineering fields.
The Communications and Computing Systems Lab (CCSL) at KAUST received two awards in the International Telecommunication Union AI for Good Machine Learning Challenge and tinyML Hackathon Challenge 2023: Pedestrian Detection. The KAUST team's solution achieved high accuracy in pedestrian identification using event-based cameras, while consuming less power and achieving lower latency. They also received an award for innovative use of "Edge Impulse" for building datasets and training models. Why it matters: This recognition highlights KAUST's growing influence in AI research, particularly in edge computing and computer vision applications for public safety.
KAUST held its 15th convocation, welcoming 341 new students, 40% of whom are from Saudi Arabia. The event highlighted KAUST's renewed focus on sustainable and global impact, as well as strengthened partnerships within Saudi Arabia and abroad. Dr. Justine Braguy, a KAUST alumna and founder of Thya Technology (AI-based computer vision products), shared her entrepreneurial experience. Why it matters: KAUST's emphasis on translating innovation and fostering entrepreneurship, particularly in AI through ventures like Thya Technology, contributes to Saudi Arabia's Vision 2030 goals.
Qatar Computing Research Institute (QCRI) is undertaking cutting-edge AI research and development, including work on Arabic language technologies, computer vision, and data analytics. QCRI's projects span healthcare, disaster response, and cybersecurity. Why it matters: QCRI's work helps to build local AI expertise and address regional challenges.
Researchers at KAUST have developed a new method called Deep State Identifier for extracting information from videos for reinforcement learning. The method learns to predict returns from video-encoded episodes and identifies critical states using mask-based sensitivity analysis. Experiments demonstrate the method's potential for understanding and improving agent behavior in DRL.
KAUST researchers Anthony Cioppa and Silvio Giancola have developed SoccerNet, an open platform for AI-driven sports analysis. SoccerNet uses a large reference set of soccer game recordings (500 games, 850 hours) to provide a platform for research. It enables researchers to develop AI systems that understand and analyze soccer games. Why it matters: This platform addresses the challenge of limited datasets in sports AI research, fostering innovation and standardized performance comparison.
The paper introduces the Unscented Autoencoder (UAE), a novel deep generative model based on the Variational Autoencoder (VAE) framework. The UAE uses the Unscented Transform (UT) for a more informative posterior representation compared to the reparameterization trick in VAEs. It replaces Kullback-Leibler (KL) divergence with the Wasserstein distribution metric and demonstrates competitive performance in Fréchet Inception Distance (FID) scores.
This paper introduces a novel approach for monitoring and analyzing the evolution of complex geographic objects in satellite image time-series. The method uses a spatiotemporal graph and constraint satisfaction problems (CSP) to model and analyze object changes. Experiments on real-world satellite images from Saudi Arabian cities demonstrate the effectiveness of the proposed approach.
Researchers in Saudi Arabia are applying computer vision techniques to reduce Camel-Vehicle Collisions (CVCs). They tested object detection models including CenterNet, EfficientDet, Faster R-CNN, SSD, and YOLOv8 on the task, finding YOLOv8 to be the most accurate and efficient. Future work will focus on developing a system to improve road safety in rural areas.
This paper introduces a deep learning framework for automated pain-level detection, designed for deployment in the UAE healthcare system. The system aims to assist in patient-centric pain management and diagnosis support, particularly relevant in situations with medical staff shortages. The research assesses the framework's performance using common approaches, indicating its potential for accurate pain level identification.
Researchers are exploring computer vision models to mitigate Camel-Vehicle Collisions (CVC) in Saudi Arabia, which have a high fatality rate. They tested CenterNet, EfficientDet, Faster R-CNN, and SSD for camel detection, finding CenterNet to be the most accurate and efficient. Future work involves developing a comprehensive system to enhance road safety in rural areas.
KAUST researchers used the Shaheen XC40 supercomputer to simulate airflow around a McLaren 17D Formula One front wing endplate. They then 3D printed the wing with colored flow patterns to visualize key aerodynamic features. The team combined expertise from the Extreme Computing Research Center (ECRC), the Advanced Algorithm and Numerical Simulations Lab (AANSLab), and the Prototyping and Product Development Core Lab (PCL). Why it matters: This project showcases KAUST's supercomputing and 3D printing capabilities for advanced engineering applications, potentially impacting fields beyond Formula One aerodynamics.
KAUST researchers in the Sensors Lab are developing neuromorphic circuits for vision sensors, drawing inspiration from the human eye. They created flexible photoreceptors using hybrid perovskite materials, with capacitance tunable by light stimulation, mimicking the human retina. The team collaborates with experts in image characterization and brain pattern recognition to connect the 'eye' to the 'brain' for object identification. Why it matters: This biomimetic approach promises advancements in AI, machine learning, and smart city development within the region.
KAUST doctoral student Jose Filho won the 2022 InnovateFPGA Design Contest for his "Customized Medicine for Corals" project. The project uses an automatic feeder technology to deliver coral probiotics and monitor their efficacy via cloud connectivity, computer vision, and an FPGA. The system gathers data from cameras, temperature sensors, and luminosity sensors, using AI to determine the coral's bleaching stage and deploy beneficial microorganisms. Why it matters: This win highlights KAUST's innovative research in applying AI and cloud technology to address critical environmental challenges like coral bleaching, demonstrating the potential for technology to aid marine conservation efforts.
KAUST computer scientist Mohamed Elhoseiny and his VISION CAIR team developed Creative Walk Adversarial Networks (CWAN) for novel art generation. CWAN learns from existing art styles and deviates using 'random walk deviation' methods. Human evaluators preferred CWAN-generated art compared to other methods like StyleGAN2. Why it matters: The research demonstrates AI's potential as a valuable tool for artists, enabling the creation of unique and meaningful art, and explores more effective emotional language in image captioning.
KAUST and SABB awarded $1.1 million in non-dilutive funding to 11 startups at the TAQADAM Startup Accelerator Showcase. Ten startups each received $100,000 from a judging panel, and Autlient, a computer vision startup, won the People's Choice award and an additional $100,000. The TAQADAM accelerator aims to develop early-stage entrepreneurs and their concepts into high-potential startups from Saudi Arabia and beyond. Why it matters: This funding boosts the Saudi startup ecosystem, particularly in areas like fintech, health tech, and AI-driven solutions like computer vision, fostering innovation and economic diversification.
KAUST researchers from the Red Sea Research Center are studying mesophotic reefs (40-150m deep) as potential climate refuges for corals threatened by marine heatwaves. These deeper reefs experience less heat and light stress compared to shallow-water corals. Advanced tools like submarines and robots are now enabling the study of these previously neglected ecosystems. Why it matters: Understanding the resilience of Red Sea corals could provide crucial insights for global coral reef conservation strategies amid climate change.
Team NimbRo presented four UAVs tailored for the MBZIRC 2020 challenges, including target chasing, wall building, and fire fighting. The UAVs utilized onboard object detection, aerial manipulation, LiDAR, and thermal cameras to perform their tasks autonomously. The team's software stack, which is mostly open-source, includes tools for system configuration, monitoring, and agile trajectory generation. Why it matters: The work demonstrates advanced robotics capabilities developed in the context of a major regional competition, advancing machine vision and trajectory generation, and showcasing potential applications in various sectors.
KAUST researchers used cryogenic electron microscopy (cryo-EM) to study the 3D structure of protein complexes involved in DNA replication and repair. They investigated the interaction between the Y-family TLS polymerase Pol K and mono-ubiquitylated PCNA. The study revealed that DNA binding is required for Pol K to form a rigid, active complex with PCNA. Why it matters: Understanding these structural interactions may provide insights into cancer development and drug resistance mechanisms.
Dr. Alison Noble from the University of Oxford presented her work on smart medical ultrasound technology at the KAUST Research Open Week, focusing on automated image analysis and deep learning. Her research aims to improve data collection, patient-doctor relations, and accessibility of healthcare. Portable ultrasound technology can increase accessibility for patients in remote areas. Why it matters: AI-enhanced ultrasound has the potential to significantly improve healthcare delivery and diagnostics in Saudi Arabia and the broader region, especially in underserved communities.