KAUST scientists are developing models to predict extreme weather events like the 2009 Jeddah flood, which caused significant damage. Prof. Ibrahim Hoteit's team is using data from satellites, international sources, and local entities like PME and the Jeddah Municipality to build high-resolution models. The aim is to improve predictions of extreme rain events by one or two days and issue timely warnings. Why it matters: Improving extreme weather prediction is crucial for mitigating the impact of climate change in vulnerable regions like the GCC.
KAUST researchers have developed a dual-use wireless sensor system that monitors both traffic congestion and flood incidents in cities. The system combines ultrasonic range finders and infrared thermal sensors to provide real-time, accurate data on traffic flow and roadway flooding. Data is sent to central servers and assimilated with satellite data to form real-time maps and forecasts. Why it matters: This technology can provide up-to-the-minute warnings for flash floods and traffic, enabling rapid emergency response and potentially saving lives in urban environments.
KAUST researchers used 3D mapping technology via remote control helicopter to survey and create detailed renderings of Jeddah's Al Balad, a UNESCO World Heritage Site. The team, from KAUST's Visual Computer Center and FalconViz, captured high-definition images from about 50 meters above street level. This enabled the creation of accurate 3D models, showing building shifts and potential problems for urban planners. Why it matters: This method provides a rapid and accurate way to document and preserve historical landmarks, especially in areas where traditional surveying is difficult or infeasible, aiding in cultural heritage preservation efforts.
KAUST researchers and international collaborators tagged 15 Chilean devil rays in the Atlantic, revealing unexpected deep-diving behavior. The tags recorded dives reaching almost 2,000 meters at speeds of 6 meters per second and temperatures below 4 degrees Celsius. The rays exhibited two distinct diving patterns, including long periods at 1,000 meters and stepwise returns to the surface, possibly for thermoregulation. Why it matters: This research challenges previous assumptions about devil ray behavior and highlights KAUST's contribution to marine biology and understanding of ocean ecosystems.
A proposed recognition system aims to identify missing persons, deceased individuals, and lost objects during the Hajj and Umrah pilgrimages in Saudi Arabia. The system intends to leverage facial recognition and object identification to manage the large crowds expected in the coming decade, estimated to reach 20 million pilgrims. It will be integrated into the CrowdSensing system for crowd estimation, management, and safety.
MBZUAI alumnus Numan Saeed is applying machine learning to medical imaging and cancer research as a research scientist at the University. He collaborates with UAE hospitals like Sheikh Shakhbout Medical City and Cleveland Clinic to build datasets and gather clinical feedback. Saeed's team is developing a model focused on head and neck cancer using the HECKTOR dataset and local data. Why it matters: This research contributes to the UAE's healthcare ambitions by enabling earlier diagnosis and personalized care through AI-driven analysis of medical images.
MBZUAI alumnus Abdelrahman Shaker reflects on his evolving definition of impact in AI research, now valuing real-world usefulness over publication count. His work on efficient models like SwiftFormer (ICCV'23) and EdgeNext saw unexpected adoption, with millions of downloads and integration into real-world applications. Shaker chose MBZUAI for its faculty alignment, resulting in 10 publications and over 2,500 citations by graduation. Why it matters: This highlights the increasing focus on practical applications and real-world impact within the AI research community in the GCC region, as opposed to purely academic metrics.
An MBZUAI team led by Ph.D. student Dmitry Demidov won the Best Student Paper Award at VISAPP 2023 for their work on fine-grained visual classification. Their paper, 'Salient Mask-Guided Vision Transformer for Fine-Grained Classification,' introduces SM-ViT, a technique using a salient mask to improve Vision Transformer accuracy. The model focuses on defining characteristics of objects, outperforming standard ViT architecture, even with fewer or lower-resolution images. Why it matters: This award recognizes MBZUAI's contribution to advancing computer vision, particularly in applications requiring nuanced object recognition, such as robotics and automated systems.
MBZUAI researchers presented "TransRadar," a study at WACV proposing new uses for radar in object identification. The study, led by Yahia Dalbah, explores fusing radar with other technologies to identify objects, particularly for autonomous vehicles. The "TransRadar" approach uses an adaptive-directional transformer for real-time multi-view radar semantic segmentation. Why it matters: This research addresses the limitations of radar by enhancing its object recognition capabilities, potentially improving the reliability of autonomous systems in adverse conditions.
MBZUAI researchers are presenting a new approach to open-world object detection at the AAAI conference. The method enables machines to distinguish between known and unknown objects in images, and then learn to classify the unknown objects. PhD student Sahal Shaji Mullappilly is the lead author of the study, titled "Semi-Supervised Open-World Detection". Why it matters: This research addresses a key limitation in current object detection systems, allowing for more adaptable and robust AI in real-world applications.
MBZUAI researchers demonstrated a low-latency, multilingual multimodal AI system at GITEX that integrates speech, text, and visual capabilities for more lifelike human-machine conversation. The demo, led by Dr. Hisham Cholakkal, includes a mobile app where users can point their camera at an object and ask questions, receiving spoken answers in multiple languages. They are also integrating the model into a robot dog that can respond to voice commands. Why it matters: This work addresses key challenges in deploying LLMs to real-world applications in the Middle East, such as multilingual support and real-time responsiveness.
Hisham Cholakkal has received MBZUAI’s inaugural Award for Teaching Excellence, launched by the University’s Center for Teaching and Learning. Cholakkal, an Assistant Professor of Computer Vision who joined MBZUAI in 2020, was recognized for his innovative teaching methods and positive impact on students. The award considers course evaluations and student feedback to recognize impactful, student-centered teaching. Why it matters: This award highlights MBZUAI's commitment to recognizing and promoting excellence in AI education within the region.
MBZUAI researchers have developed a new action tokenization method called LipVQ-VAE to improve in-context robot learning. LipVQ-VAE combines VQ-VAE with a Lipschitz constraint to generate smoother robotic motions, addressing limitations of traditional methods. The technique was tested on simulated and real robots, showing improved performance in imitation learning. Why it matters: This research advances robot learning by enabling more fluid and successful robot actions through improved action representation, drawing inspiration from NLP techniques.
MBZUAI recognized seven faculty members for outstanding contributions in research, teaching, and mentorship at its annual Faculty Recognition and Welcome Reception. Associate Professor Salman Khan received the Distinguished Research Award for his work on multimodal models for remote Earth observation, including projects like AI4Weather and the AI Global Agriculture Advisory. Assistant Professor Alham Fikri Aji received the Early Career Researcher Award for his contributions to low-resource NLP and international collaborations. Why it matters: The awards highlight MBZUAI's focus on advancing AI for global challenges and recognizing faculty contributions to research and education.
MBZUAI PhD student Salem AlMarri, also a Dubai Police officer, has been appointed to the Dubai Youth Council for 2023-2025. AlMarri's research focuses on using AI to combat crime and improve emergency response times, aiming to contribute to the UAE's AI strategy. In 2019, he was recognized as one of the UAE’s up and coming scientist at the Her Highness Sheikha Fatima Bint Mubarak Program for Excellence and Community Intelligence. Why it matters: This appointment highlights the UAE's focus on empowering young talent in AI to drive innovation and address local challenges in public safety and smart city development.
MBZUAI is developing AI-powered applications to help reduce malaria's impact in Indonesia, supported by Sheikh Mohamed bin Zayed Al Nahyan's Reaching the Last Mile initiative. The applications use sensory data fusion to create "digital twins" for precise weather forecasting and real-time environmental representation. AI and clustering analysis identify recurring features contributing to malaria outbreaks, enabling preventative measures and early treatment. Why it matters: This project demonstrates AI's potential in combating climate-sensitive diseases and improving public health in vulnerable regions.
Salem AlMarri, the first Emirati Ph.D. graduate from MBZUAI, developed a video anomaly detection (VAD) system for his thesis. The VAD system can detect subtle anomalies in video, such as suspicious interactions, to help police prevent crimes and save lives. AlMarri's work was carried out under the guidance of Karthik Nandakumar, Affiliated Associate Professor of Computer Vision at MBZUAI. Why it matters: This research showcases the potential of AI in enhancing public safety and security in the UAE, demonstrating practical applications of computer vision in law enforcement.
MBZUAI alumnus Ikboljon Sobirov is using AI to develop new diagnostic tools for cardiovascular disease at the University of Oxford. His research focuses on building imaging biomarkers by integrating transcriptomic data with medical scans. The goal is to predict how a patient will respond to specific medications using only images. Why it matters: This work showcases the potential of AI and multi-modal data to personalize medicine and improve healthcare outcomes in the region and globally.
The International Olympiad in AI (IOAI), a competition for high school students in NLP, CV, and robotics, will be hosted in Abu Dhabi by MBZUAI in 2026. MBZUAI faculty members Yova Kementchedjhieva and Maxim Panov are part of IOAI's International Scientific Committee and helped bring the event to the UAE. The IOAI 2025 in Beijing included individual contests, team challenges, and the GAITE program, testing participants with AI tasks and generalization challenges. Why it matters: Hosting IOAI in Abu Dhabi highlights the UAE's commitment to fostering AI talent and positions MBZUAI as a key player in global AI education.
MBZUAI concluded its 2025 Undergraduate Research Internship Program (UGRIP), hosting 57 students from 24 countries. The four-week program, held in Abu Dhabi, allowed undergraduates to collaborate with faculty on AI research projects in machine learning, computer vision, and NLP. The 2025 edition had a 4% acceptance rate, with nearly 2,000 applications received. Why it matters: The program highlights MBZUAI's role in attracting global AI talent to the UAE and fostering research collaborations.
MBZUAI alumnus Kane Lindsay is using AI to digitize and analyze plant specimens at Kew Gardens, including handwritten notes and preserved plants dating back centuries. He developed an AI model to recognize handwriting (OCR) and extract phenotype data from plant images, and another to redact sensitive location data. This work began as an internship and evolved into a fully funded Ph.D. Why it matters: AI is accelerating the digitization and preservation of plant collections, making them more accessible to researchers and aiding in conservation efforts.
MBZUAI doctoral student Umaima Rahman is researching domain adaptation and generalization in deep learning for medical imaging to improve AI model performance across diverse hospitals and equipment. Her work focuses on building models that learn consistent features across different data sources to ensure reliability in various healthcare settings. Rahman emphasizes that generalization in healthcare AI is a necessity, especially in resource-limited settings, and aims to develop AI that assists clinicians rather than replaces them. Why it matters: This research addresses a critical challenge in deploying AI in healthcare, ensuring that models can be reliably used in diverse settings, particularly benefiting developing countries and improving global healthcare accessibility.
MBZUAI researchers presented a new causal representation learning framework at ICLR for identifying latent causal variables in multimodal biological data. The framework addresses the challenge of uncovering underlying causal factors from lab tests, genetic information, and medical images. The new approach can identify latent causal variables and their influence on observed biological outcomes across modalities. Why it matters: The model's ability to analyze causal mechanisms between modalities can lead to more complete insights in biomedical research.
MBZUAI researchers developed GroupMamba, a new set of state-space models (SSMs) for computer vision that addresses limitations in existing SSMs related to computational efficiency and optimization challenges. GroupMamba introduces a new layer called modulated group mamba, improving efficiency and stability. In benchmark tests, GroupMamba performed as well as similar SSM systems, but more efficiently, offering a backbone for tasks like image classification, object detection, and segmentation. Why it matters: This research aims to bridge the gap between vision transformers and CNNs by improving SSMs, potentially leading to more efficient and powerful computer vision models.
MBZUAI is developing AI technologies to improve understanding and conservation of marine environments. AI algorithms analyze data from satellite imagery, ROVs, and sensors to identify patterns and trends, with machine learning models predicting oceanographic phenomena. Computer vision automates the identification of marine organisms, aiding in biodiversity assessments and ecosystem health evaluations. Why it matters: This research supports sustainability goals, such as improving biodiversity assessments and enabling faster responses to environmental events in the region's sensitive marine ecosystems.
MBZUAI alumnus Hanan Gani, a 2024 master's graduate in machine learning, is now a research associate at MBZUAI working on a meteorological project with the UAE government. He also focuses on multimodal and embodied intelligence research, mentors AI students, and has published nine papers during his time at MBZUAI. His research includes work on vision transformers, text-to-image generation, and large multimodal models. Why it matters: Showcases MBZUAI's role in attracting and developing AI talent within the UAE, contributing to the nation's AI research capabilities.
MBZUAI hosted a talk by Visiting Associate Professor Adrian Bors on continuously streaming AI and the challenge of catastrophic forgetting. The talk covered approaches to continual learning like expanding mixtures of models and generative replay mechanisms. Results were presented on image classification and generation tasks. Why it matters: Continual learning is crucial for AI systems to adapt to new environments and real-world data without forgetting previous knowledge.
Laurent Najman presented the Power Watershed (PW) optimization framework for image and data processing. The PW framework enhances graph-based data processing algorithms like random walker and ratio-cut clustering, leading to faster solutions. It can be adapted for graph-based cost minimization methods and integrated with deep learning networks. Why it matters: This framework could enable more efficient and scalable image and data processing algorithms relevant to computer vision and related fields in the Middle East.
Yoshihiko Nakamura from the University of Tokyo discusses the computational challenges of humanoid robots, extending beyond sensing and control to understanding human movement, sensation, and relationships. The talk covers recent research on mechanical humanoid robots with a focus on actuators and computational problems related to human movements. Nakamura highlights the need for humanoid robots to interpret human actions and interactions for effective application. Why it matters: Addressing these computational challenges is crucial for developing more sophisticated and human-compatible robots for use in various human-centered applications within the region and globally.
Thamar Solorio from the University of Houston presented preliminary work on multimodal representation learning for detecting objectionable content in videos at MBZUAI. The research investigates two multimodal pretraining mechanisms, finding contrastive learning more effective than unimodal representation prediction. The study also assesses the value of common multimodal corpora for this task. Why it matters: This research contributes to the development of AI techniques for content moderation, an important issue for online platforms in the Middle East and globally.
Cyrill Stachniss from the University of Bonn presented recent work on agricultural robotics and self-driving cars. The talk covered autonomous field robots and their ability to perceive, model, and predict future developments in complex farming environments. The presentation also included developments in supervised and unsupervised learning for autonomous car perception systems. Why it matters: This highlights the growing interest in robotics research at MBZUAI and the potential for AI to transform key sectors in the GCC region like agriculture and transportation.
Manling Li from UIUC proposes a new research direction: Event-Centric Multimodal Knowledge Acquisition, which transforms traditional entity-centric single-modal knowledge into event-centric multi-modal knowledge. The approach addresses challenges in understanding multimodal semantic structures using zero-shot cross-modal transfer (CLIP-Event) and long-horizon temporal dynamics through the Event Graph Model. Li's work aims to enable machines to capture complex timelines and relationships, with applications in timeline generation, meeting summarization, and question answering. Why it matters: This research pioneers a new approach to multimodal information extraction, moving from static entity-based understanding to dynamic, event-centric knowledge acquisition, which is essential for advanced AI applications in understanding complex scenarios.
The article discusses the importance of sample correlations in computer graphics, vision, and machine learning, highlighting how tailored randomness can improve the efficiency of existing models. It covers various correlations studied in computer graphics and tools to characterize them, including the use of neural networks for developing different correlations. Gurprit Singh from the Max Planck Institute for Informatics will be presenting on the topic. Why it matters: Optimizing sampling techniques via understanding and applying correlations can lead to significant advancements and efficiency gains across multiple AI fields.
Dr. Xiaoming Liu from Michigan State University discussed computer vision techniques for 3D world understanding at a talk hosted by MBZUAI. The talk covered 3D reconstruction, detection, depth estimation, and velocity estimation, with applications in biometrics and autonomous driving. Dr. Liu also touched on anti-spoofing and fair face recognition research at MSU's Computer Vision Lab. Why it matters: Showcasing international experts and research directions helps to catalyze computer vision and 3D understanding research efforts within the UAE's AI ecosystem.
A presentation discusses the evolution of Vision-and-Language Navigation (VLN) from benchmarks like Room-to-Room (R2R). It highlights the role of Large Language Models (LLMs) such as GPT-4 in enabling more natural human-machine interactions. The presentation showcases work using LLMs to decode navigational instructions and improve robotic navigation. Why it matters: This research demonstrates the potential of merging vision, language, and robotics for advanced AI applications in navigation and human-computer interaction.
A Caltech researcher presented at MBZUAI on memory representation and retrieval, contrasting AI and neuroscience approaches. Current AI retrieval systems like RAG retrieve via fine-tuning and embedding similarity, while the presenter argued for exploring retrieval via combinatorial object identity or spatial proximity. The research explores circuit-level retrieval via domain fine-tuned LLMs and distributed memory for image retrieval using semantic similarity. Why it matters: The work suggests structured databases and retrieval-focused training can allow smaller models to outperform larger general-purpose models, offering efficiency gains for AI development in the region.
A new neural network architecture called Orchid was introduced that uses adaptive convolutions to achieve quasilinear computational complexity O(N logN) for sequence modeling. Orchid adapts its convolution kernel dynamically based on the input sequence. Evaluations across language modeling and image classification show that Orchid outperforms attention-based architectures like BERT and Vision Transformers, often with smaller model sizes. Why it matters: Orchid extends the feasible sequence length beyond the practical limits of dense attention layers, representing progress toward more efficient and scalable deep learning models.
This article discusses the reliability of Deep Neural Networks (DNNs) and their hardware platforms, especially regarding soft errors caused by cosmic rays. It highlights that while DNNs are robust against bit flips, errors can still lead to miscalculations in AI accelerators. The talk, led by Prof. Masanori Hashimoto from Kyoto University, will cover identifying vulnerabilities in neural networks and reliability exploration of AI accelerators for edge computing. Why it matters: As DNNs are deployed in safety-critical applications in the region, ensuring the reliability of AI hardware is crucial for safe and trustworthy operation.
Dr. Zeke Xie from HKUST(GZ) presented research on noise initialization and sampling strategies for diffusion models. The talk covered golden noise for text-to-image models, zigzag diffusion sampling, smooth initializations for video diffusion, and leveraging image diffusion for video synthesis. Xie leads the xLeaF Lab, focusing on optimization, inference, and generative AI, with previous experience at Baidu Research. Why it matters: The work addresses core challenges in improving the quality and diversity of generated content from diffusion models, a key area of advancement for AI applications in the region.
Vicky Kalogeiton from École Polytechnique discussed the importance of multimodality for story-level recognition and generation using video, audio, text, masks and clinical data. She presented on multimodal video understanding using FunnyNet-W and Short Film Dataset. She further showed examples of visual generation from text and other modalities (ET, CAD, DynamicGuidance). Why it matters: Multimodal AI research is growing globally, and this talk highlights the potential of combining different data types for enhanced understanding and generation, which could have implications for various applications, including those relevant to the Middle East.
Researchers have developed robotic path-planning and control algorithms for minimally invasive surgery (MIS) that steer flexible needles, incorporating teleoperation and haptic feedback. An AI algorithm was designed to predict target motion due to respiratory movement, improving needle placement accuracy. GANs were used to generate synthetic images visualizing organ and tumor motion. Why it matters: This research demonstrates the potential of AI and robotics to enhance precision and adaptability in MIS, potentially reducing patient trauma and improving recovery times in the region and beyond.
The article discusses immersive analytics, which uses VR and AR to visualize data in 3D and embed it into the user's environment, and reviews systems and techniques from the Data Visualisation and Immersive Analytics lab at Monash University. It explores the concept of "embodied sensemaking" and its potential to improve how people work with complex data. Professor Tim Dwyer directs the Data Visualisation and Immersive Analytics Lab at Monash University. Why it matters: Immersive analytics could significantly enhance data comprehension and decision-making across various sectors in the Middle East, where large-scale projects and smart city initiatives generate vast datasets.
Pascal Fua from EPFL gave a talk at MBZUAI on physics-based deep learning for medical imaging. The talk covered how self-supervision and knowledge of human anatomy and physics can improve deep learning algorithms when training data is limited. Applications discussed included endoscopic heart surgery, colonoscopy, and intubation. Why it matters: This highlights the growing importance of domain knowledge and self-supervision in overcoming data scarcity challenges for AI in healthcare applications within the region.
A professor from Nanyang Technological University (NTU), Singapore gave a talk at MBZUAI about "Just-Noticeable Difference (JND)" models in visual intelligence. The talk covered visual JND models, research and applications, and future opportunities for JND modeling. JND can help tackle big data challenges with limited resources by focusing on user-centric and green systems. Why it matters: Exploring JND could lead to advancements in AI applications related to visual signal processing, image synthesis, and generative AI in the region.
This research introduces a novel method using the Lateral Accretive Hybrid Network (LEARNet) to capture and analyze micro-expressions for mental health applications. The method refines both broad and subtle facial cues to detect mental health conditions like anxiety or depression. The authors also propose a neural architecture search (NAS) strategy to design a compact CNN for micro-expression recognition, improving performance and resource use. Why it matters: By integrating micro-emotion recognition with mental health estimation, the approach enables more accurate and early detection of emotional and mental health issues, potentially leading to improved well-being.
Video motion magnification amplifies subtle movements in video footage, making the imperceptible visible across various fields. In healthcare, it allows non-invasive monitoring of vital signs and micro-expressions. In engineering, it helps detect structural vibrations in infrastructure, while also being used in sports science, security, and robotics. Why it matters: The technology's ability to reveal hidden details has the potential to revolutionize diagnostics, monitoring, and decision-making in diverse sectors across the Middle East.
Marc Pollefeys from ETH Zurich and Microsoft Spatial AI Lab will discuss building 3D environment representations for assisting humans and robots. The talk covers visual 3D mapping, localization, spatial data access, and navigation using geometry and learning-based methods. It also explores building rich 3D semantic representations for scene interaction via open vocabulary queries leveraging foundation models. Why it matters: Advancements in spatial AI and 3D scene understanding are critical for enabling more capable robots and AI assistants in various applications within the region.
Gregory Chirikjian presented an overview of research on robot navigation in unstructured environments, using computer vision, sensor tech, ML, and motion planning. The methods use multi-modal observations from RGB cameras, 3D LiDAR, and robot odometry for scene perception, along with deep RL for planning. These methods have been integrated with wheeled, home, and legged robots and tested in crowded indoor scenes, home environments, and dense outdoor terrains. Why it matters: This research pushes the boundaries of robotics in complex environments, paving the way for more versatile and autonomous robots in the Middle East.
A talk explores multimodal approaches inspired by user behavior for detecting deepfakes, considering user studies on multicultural deepfakes and the ACM Multimedia 2024 benchmark. The research leverages insights into how different audiences perceive manipulated media. Abhinav Dhall from Flinders University will present findings and future directions in deepfake analysis at MBZUAI. Why it matters: Addressing deepfakes is crucial for maintaining trust in digital content, especially with the increasing sophistication and accessibility of AI-driven manipulation tools.
Song Chaoyang from the Southern University of Science and Technology (SUSTech) presented research on Vision-Based Tactile Sensing (VBTS) for robot learning, combining soft robotic design with learning algorithms to achieve state-of-the-art performance in tactile perception. Their VBTS solution demonstrates robustness up to 1 million test cycles and enables multi-modal outputs from a single, vision-based input, facilitating applications such as amphibious tactile grasping and industrial welding. The talk also highlighted the DeepClaw system for capturing human demonstration actions, aiming for a universal interaction interface. Why it matters: This research advances embodied intelligence by improving robot dexterity and adaptability through enhanced tactile sensing, which is crucial for complex manipulation tasks in various sectors such as manufacturing and healthcare within the region.