MBZUAI and TII have formed a partnership to advance AI research and strengthen the AI ecosystem in the UAE. The collaboration framework enables joint fundamental and applied research in key domains of AI, including neuromorphic computing and autonomous vehicles. The two organizations will share research resources and exchange students, faculty, and researchers. Why it matters: This partnership strengthens Abu Dhabi's position as a regional AI hub and promotes economic growth through AI-based knowledge.
Researchers from MBZUAI have developed MMRINet, a Mamba-based neural network for efficient brain tumor segmentation in MRI scans. The model uses Dual-Path Feature Refinement and Progressive Feature Aggregation to achieve high accuracy with only 2.5M parameters, making it suitable for low-resource clinical environments. MMRINet achieves a Dice score of 0.752 and HD95 of 12.23 on the BraTS-Lighthouse SSA 2025 benchmark.
MBZUAI researchers introduce TerraFM, a scalable self-supervised learning model for Earth observation that uses Sentinel-1 and Sentinel-2 imagery. The model unifies radar and optical inputs through modality-specific patch embeddings and adaptive cross-attention fusion. TerraFM achieves strong generalization on classification and segmentation tasks, outperforming prior models on GEO-Bench and Copernicus-Bench.
Researchers from MBZUAI introduced RP-SAM2, a method to improve surgical instrument segmentation by refining point prompts for more stable results. RP-SAM2 uses a novel shift block and compound loss function to reduce sensitivity to point prompt placement, improving segmentation accuracy in data-constrained settings. Experiments on the Cataract1k and CaDIS datasets show that RP-SAM2 enhances segmentation accuracy and reduces variance compared to SAM2, with code available on GitHub.
Researchers introduce SALT, a parameter-efficient fine-tuning method for medical image segmentation that combines singular value adaptation with low-rank transformation. SALT selectively adapts influential singular values and complements this with a low-rank update for the remaining subspace. Experiments on five medical datasets show SALT outperforms state-of-the-art PEFT methods by 2-5% in Dice score with only 3.9% trainable parameters.
Researchers at MBZUAI introduce FissionFusion, a hierarchical model merging approach to improve medical image analysis performance. The method uses local and global aggregation of models based on hyperparameter configurations, along with a cyclical learning rate scheduler for efficient model generation. Experiments show FissionFusion outperforms standard model souping by approximately 6% on HAM10000 and CheXpert datasets and improves OOD performance.
Researchers at MBZUAI have introduced MedMerge, a transfer learning technique that merges weights from independently initialized models to improve performance on medical imaging tasks. MedMerge learns kernel-level weights to combine features from different models into a single model. Experiments across various medical imaging tasks demonstrated performance gains of up to 7% in F1 score.
This paper introduces a new Single Domain Generalization (SDG) method called ConDiSR for medical image classification, using channel-wise contrastive disentanglement and reconstruction-based style regularization. The method is evaluated on multicenter histopathology image classification, achieving a 1% improvement in average accuracy compared to state-of-the-art SDG baselines. Code is available at https://github.com/BioMedIA-MBZUAI/ConDiSR.
Researchers from MBZUAI have developed XReal, a diffusion model for generating realistic chest X-ray images with precise control over anatomy and pathology location. The model utilizes an Anatomy Controller and a Pathology Controller to introduce spatial control in a pre-trained Text-to-Image Diffusion Model without fine-tuning. XReal outperforms existing X-ray diffusion models in realism, as evaluated by quantitative metrics and radiologists' ratings, and the code/weights are available.
Video-ChatGPT is a new multimodal model that combines a video-adapted visual encoder with a large language model (LLM) to enable detailed video understanding and conversation. The authors introduce a new dataset of 100,000 video-instruction pairs for training the model. They also develop a quantitative evaluation framework for video-based dialogue models.
Researchers from MBZUAI have developed EchoCoTr, a novel spatiotemporal deep learning method for estimating left ventricular ejection fraction (LVEF) from echocardiograms. EchoCoTr combines CNNs and vision transformers to overcome the limitations of each when applied to medical video data. The method achieves state-of-the-art results on the EchoNet-Dynamic dataset, demonstrating improved accuracy compared to existing approaches, with code available on GitHub.
This paper introduces a self-supervised contrastive learning method for segmenting the left ventricle in echocardiography images when limited labeled data is available. The approach uses contrastive pretraining to improve the performance of UNet and DeepLabV3 segmentation networks. Experiments on the EchoNet-Dynamic dataset show the method achieves a Dice score of 0.9252, outperforming existing approaches, with code available on Github.
MBZUAI faculty and students presented 31 papers at the 2022 Conference on Computer Vision and Pattern Recognition (CVPR), including 6 oral presentations. Professors Fahad Khan and Shijian Lu had 9 and 8 papers accepted respectively. Researchers collaborated with 57 institutions across 16 countries. Why it matters: MBZUAI's strong showing at a top-tier CV conference demonstrates the rapid growth and international collaboration of AI research in the UAE.
MBZUAI researchers are refining AI techniques to improve cancer diagnosis for colorectal and breast cancer, both common in the Middle East. They are using "few-shot tissue image generation," in which AI generates data for training AI models to recognize lesions, addressing the challenge of limited training data. The developed framework improves the efficiency of radiologists in breast cancer diagnosis, leading to better detection of breast lesions and timely treatment interventions. Why it matters: These advancements in AI-aided diagnostics can lead to earlier and more accurate cancer detection, ultimately improving patient outcomes in the region and beyond.
MBZUAI researchers, in collaboration with TUM, developed Open-YOLO 3D, a new method for open-vocabulary 3D instance segmentation. Open-YOLO 3D enables robots to detect and differentiate individual objects in a 3D scene without being limited to predefined object categories, using both camera images and lidar-generated 3D point clouds. The new system was shown to be more accurate and significantly faster than previous approaches. Why it matters: This advancement enhances robots' ability to understand and interact with dynamic, real-world environments, bringing robots closer to being useful in everyday life.
MBZUAI researchers co-led a study published in Nature demonstrating that GluFormer, an AI foundation model trained on continuous glucose monitoring (CGM) data, more accurately predicts long-term diabetes and cardiovascular risk than current clinical standards. GluFormer, built on a transformer architecture and trained using NVIDIA AI infrastructure on over 10 million CGM measurements, forecasts individual health risks using short-term glucose dynamics. In a 12-year follow-up, the model captured 66% of new-onset diabetes cases and 69% of cardiovascular-death events in its highest-risk group, outperforming established CGM-derived metrics across 19 external cohorts. Why it matters: The development of GluFormer represents a significant advancement in personalized healthcare, enabling proactive and individualized health strategies through the analysis of dynamic glucose data.
Researchers from MBZUAI developed "uncertainty quantification heads" (UQ heads) to detect hallucinations in language models by probing internal states and estimating the credibility of generated text. UQ heads leverage attention maps and logits to identify potential hallucinations without altering the model's generation process or relying on external knowledge. The team found that UQ heads achieved state-of-the-art performance in claim-level hallucination detection across different domains and languages. Why it matters: This approach offers a more efficient and accurate method for identifying hallucinations, improving the reliability and trustworthiness of language models in various applications.
MBZUAI's Institute of Foundation Models has released K2, a 70-billion-parameter, reasoning-centric foundation model. K2 is designed to be fully inspectable, with open weights, training code, data composition, mid-training checkpoints, and evaluation harnesses. K2 outperforms Qwen2.5-72B and approaches the performance of Qwen3-235B. Why it matters: This release promotes transparency and reproducibility in AI development, providing researchers with the resources needed to study, adapt, and build upon a strong foundation model.
MBZUAI researchers introduced CausalVerse, a new benchmark for causal representation learning (CRL) presented at NeurIPS 2025. CausalVerse combines high-fidelity visual complexity with access to underlying causal variables and graphs, featuring 200,000 images and 300 million video frames across 24 sub-scenes in four domains. It aims to provide a realistic and precise testbed to evaluate whether CRL methods can truly learn the right causes. Why it matters: By bridging the gap between toy datasets and real-world data, CausalVerse can drive advances in AI systems capable of understanding causality in complex scenarios.
The Tahnoon bin Zayed Scholarship in AI Excellence has been established to support undergraduate students at MBZUAI, starting in the 2025-2026 academic year. The merit-based scholarship aims to support approximately 350 students over six years, focusing on mathematical fluency, leadership, and entrepreneurial attitude. Scholarship recipients will have opportunities to represent the UAE at youth events and pursue entrepreneurial ventures. Why it matters: This scholarship program is a significant investment in developing local AI talent and positioning the UAE as a leader in global AI innovation.
MBZUAI researchers developed a new AI-generated image detection method called 'consistency verification' (ConV). Instead of training on labeled real and fake images, ConV identifies structural patterns unique to real photos using a data manifold concept. The system modifies images and uses DINOv2 to measure the difference between original and transformed representations, classifying images based on their proximity to the manifold. Why it matters: This approach offers a more robust way to detect AI-generated images without needing training data from every image generator, addressing a key limitation in the rapidly evolving landscape of AI image synthesis.
MBZUAI researchers at the Institute of Foundation Models (IFM) investigated the role of reinforcement learning (RL) in improving reasoning abilities of language models. Their study found that RL acts as an 'elicitor' for reasoning in domains frequently encountered during pre-training (e.g., math, coding), while genuinely teaching new reasoning skills in underrepresented domains (e.g., logic, simulations). To support their analysis, they created a new dataset called GURU containing 92,000 examples across six domains. Why it matters: This research clarifies the impact of reinforcement learning on language model reasoning, paving the way for developing models with more generalizable reasoning abilities across diverse domains, an important direction for more capable AI systems.
MBZUAI researchers presented a NeurIPS 2024 Spotlight paper that quantifies AI vulnerability by measuring bits leaked per query. Their formula predicts the minimum queries needed for attacks based on mutual information between model output and attacker's target. Experiments across seven models and three attack types (system-prompt extraction, jailbreaks, relearning) validate the relationship. Why it matters: This work offers a framework to translate UI choices (like exposing log-probs or chain-of-thought) into concrete attack surfaces, informing more secure AI design and deployment in the region.
MBZUAI Adjunct Professor Iryna Gurevych has won the 2025 Royal Society Milner Award for her contributions to NLP and AI. The Milner Award recognizes outstanding European computer scientists and includes a bronze medal and a £5,000 honorarium. Gurevych's work focuses on processing big data with NLP tools, argument mining, and detecting misleading content. Why it matters: The award highlights MBZUAI's growing prominence in the international AI research landscape and Gurevych's work in making language models safer.
MBZUAI researchers developed ClinGRAD, a multimodal graph neural network that analyzes genomic data, MRI scans, and clinical information to classify dementia types (Alzheimer's, vascular, etc.). The system addresses the challenge of high misdiagnosis rates (up to 30%) in dementia, where incorrect diagnoses can significantly impact patient life expectancy. ClinGRAD aims to be an interpretable AI system, providing explainability to clinicians. Why it matters: Accurate and early diagnosis of dementia subtypes is crucial for slowing disease progression and improving patient care in the region, where the prevalence of dementia is expected to rise significantly.
MBZUAI researchers presented DEFUSE-MS at MICCAI 2025, a novel AI system for analyzing changes in MRI scans of multiple sclerosis (MS) patients. DEFUSE-MS uses a deformation field-guided spatiotemporal graph-based framework to identify new lesions by reasoning about how the brain has changed. The model constructs graphs of small regions within baseline and follow-up MRIs, linking them across time with edges enriched with learned embeddings of the deformation field. Why it matters: DEFUSE-MS reframes the task from simple "spot the difference" to understanding structural changes, potentially improving the speed and accuracy of MS diagnosis and treatment monitoring.
MBZUAI researchers developed Data Wise, an AI platform that provides instant data analytics for businesses. The platform uses AI agents and LLMs to analyze raw customer data and generate actionable recommendations. Data Wise aims to address the shortage of data scientists, particularly in the UAE and GCC. Why it matters: This platform democratizes access to advanced analytics, empowering businesses in the region to make data-driven decisions without relying on scarce technical expertise.
MBZUAI researchers are introducing MedNNS, a system to be presented at MICCAI 2025, that recommends the best AI architecture and initialization for medical imaging tasks. MedNNS addresses the challenge of inefficient trial-and-error in building medical imaging AI by reframing model selection as a retrieval problem. The system employs a Once-For-All ResNet-like model and a learned meta-space of 720k model-dataset pairs, using dataset embeddings to predict optimal model performance. Why it matters: By automating model selection, MedNNS promises to significantly reduce the time and resources required to develop effective AI solutions for healthcare, particularly in medical imaging.
MBZUAI researchers developed a new approach called Multimodal Optimal Transport via Grounded Retrieval (MOTOR) to improve the accuracy of vision-language models for medical image analysis. MOTOR combines retrieval-augmented generation (RAG) with an optimal transport algorithm to retrieve and rank relevant image and textual data. Testing on two medical datasets showed that MOTOR improved average performance by 6.45%. Why it matters: This technique addresses the challenges of limited specialized medical datasets and computational costs associated with training AI models for medical image interpretation, offering a more efficient and accurate solution.
MBZUAI researchers have developed a new kernel-based method to identify dependence patterns in data, especially in small regions exhibiting 'rare dependence' where relationships between variables differ. The method uses sample importance reweighting, assigning more importance to regions with rare dependence. Tested on synthetic and real-world data, the algorithm successfully identified relations between variables even with rare dependence, outperforming traditional methods like HSIC. Why it matters: This advancement can improve data analysis in fields like public health, economics, genomics, and AI, enabling more accurate insights from complex observational data.
MBZUAI Ph.D. student Raza Imam and colleagues presented a new benchmark called MediMeta-C to test the robustness of medical vision-language models (MVLMs) under real-world image corruptions. They found that top-performing MVLMs on clean data often fail under mild corruption, with fundoscopy models particularly vulnerable. To address this, they developed RobustMedCLIP (RMC), a lightweight defense using few-shot LoRA tuning to improve model robustness. Why it matters: This research highlights the critical need for robustness testing in medical AI to ensure reliability in clinical settings, particularly in resource-constrained environments where image quality may be compromised.
Researchers from Carnegie Mellon University and MBZUAI have developed a new method called ConceptAligner for precise image editing using AI. The system decomposes text embeddings into independent building blocks called atomic concepts, allowing users to make targeted tweaks without generating entirely new images. Their approach ensures that each latent factor maps to a specific user-controllable dial, enabling accurate concept-level modifications. Why it matters: This research addresses a major limitation in AI image generation, enhancing its usefulness in industries where precise control is crucial, such as advertising and medicine, and improving the reliability of AI-driven creative tools.
The Human Phenotype Project (HPP), led by researchers from Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), published findings in Nature Medicine detailing an understanding of the health-disease continuum. The HPP involves deep and longitudinal profiling of approximately 28,000 participants, collecting diverse data including medical history, lifestyle, blood tests, and molecular profiling. The project aims to create AI-based predictive models for disease onset and progression, and digital twins to simulate interventions. Why it matters: This research can transform precision medicine and preventative care in the UAE by creating personalized digital twins that can simulate interventions and predict health trajectories.
MBZUAI researchers have developed GeoPixel, a new multimodal model for pixel grounding in remote sensing images. GeoPixel associates individual pixels with object categories, enabling detailed image analysis by linking language to objects at the pixel level. The model was trained on a new dataset and benchmark, outperforming existing systems in precision. Why it matters: This advancement enhances the utility of remote sensing data for critical applications like environmental management and disaster response by providing more granular and accurate image interpretation.
MBZUAI researchers developed FarSight, a plugin to reduce hallucinations in Multimodal Large Language Models (MLLMs). FarSight addresses the issue where MLLMs generate inaccurate text by losing focus on relevant image details, leading to snowball hallucinations. Testing on models like LLaVA-1.5-7B showed FarSight's effectiveness in reducing initial mistakes, thereby minimizing overall hallucinations. Why it matters: Improving the reliability of MLLMs is crucial for applications requiring high accuracy, enhancing their utility in various real-world scenarios.
Researchers at MBZUAI, IBM Research, and other institutions have developed EarthDial, a new vision-language model (VLM) specifically designed to process geospatial data from remote sensing technologies. EarthDial handles data in multiple modalities and resolutions, processing images captured at different times to observe environmental changes. The model outperformed others on over 40 tasks including image classification, object detection, and change detection. Why it matters: This unified model bridges the gap between generic VLMs and domain-specific models, enabling complex geospatial data analysis for applications like disaster assessment and climate monitoring in the region.
MBZUAI is previewing PAN, a next-generation world model designed to simulate diverse realities and advance machine reasoning. PAN allows researchers to test AI agents in simulated environments before real-world deployment, enabling them to learn from mistakes without real-world consequences. It facilitates complex reasoning about actions, outcomes, and interactions, crucial for reliable AI performance in dynamic environments. Why it matters: PAN represents a significant advancement in AI by enabling comprehensive simulation and testing of AI agents, which can revolutionize fields like disaster management and healthcare where real-world experimentation is risky.
MBZUAI and Corniche Hospital researchers have developed FetalCLIP, a foundation model for analyzing fetal ultrasound images to detect congenital conditions. FetalCLIP outperformed other foundation models on ultrasound analysis tasks. The AI model aims to improve the early diagnosis of ailments like congenital heart defects. Why it matters: This innovation has the potential to dramatically improve health outcomes for millions of children annually by providing physicians with better insights into fetal health.
MBZUAI researchers created a new benchmark dataset called TextGames to evaluate the reasoning abilities of LLMs. The dataset uses simple, text-based games requiring skills like pattern recognition and logical thinking. LLMs struggled with the hardest questions, suggesting limitations in their reasoning capabilities despite advancements in language understanding. Why it matters: This research highlights the need for specialized reasoning models and benchmarks that go beyond memorization to truly test AI's problem-solving abilities.
MBZUAI's VP of Research, Professor Sami Haddadin, and his team at TUM have developed the 'Tree of Robots,' a new framework for categorizing robots based on capabilities and morphology rather than appearance or purpose. This framework uses a Process Database and Metrics Definitions to assess a robot's fitness for specific tasks, resulting in a fitness score and classification within the tree. The research appears in the March 2025 issue of Nature Machine Intelligence. Why it matters: This systematic approach could fundamentally change how we understand, compare, and develop robotic systems, enabling a deeper understanding of intelligent machines and their potential.
MBZUAI has launched its first Bachelor of Science in Artificial Intelligence program, which blends core AI expertise with leadership and entrepreneurial skills. The program will cover machine learning, NLP, CV, and robotics, alongside cross-disciplinary training in business and finance. It will also integrate AI into the educational journey using a co-pilot education model, enhancing critical thinking and problem-solving skills. Why it matters: This program aims to cultivate visionary thinkers and problem-solvers who can drive AI innovation and development in the UAE and beyond.
Researchers from MBZUAI and King's College London have developed a new prompting strategy called self-guided exploration to improve LLM performance on combinatorial problems. The method was tested on complex challenges like the traveling salesman problem. The findings will be presented at the 38th Annual Conference on Neural Information Processing Systems (NeurIPS) in Vancouver. Why it matters: This research could lead to practical applications of LLMs in industries like logistics, planning, and scheduling by offering new approaches to computationally complex problems.
MBZUAI researchers developed a method to adapt Meta's Segment Anything Model (SAM) for medical image segmentation, addressing its performance gap with natural images. Their approach improves SAM's accuracy without requiring extensive retraining or large medical image datasets. The research, led by Chao Qin, was nominated for the Best Paper Award at the MICCAI conference in Marrakesh. Why it matters: This offers a more efficient and effective way to leverage foundation models in specialized medical imaging applications, potentially improving diagnostic accuracy and reducing the need for large-scale, domain-specific training data.
MBZUAI has launched a full robotics program focusing on robot learning, humanoid robotics, human-computer interaction, and sensory awareness. The program includes research and teaching labs for experimentation and aims to address challenges in agriculture, logistics, and healthcare. The robotics department, founded in 2023, welcomed 18 students in August and is expected to grow to more than 15 faculty members over the next two years. Why it matters: This program signifies a major investment in AI for physical systems in the UAE, potentially leading to innovations across sectors and strengthening MBZUAI's position as a leading AI research institution.
MBZUAI researchers found that ImageNet performance isn't always indicative of real-world task performance for computer vision models. The study analyzed four popular model configurations, revealing variations in behavior on specific image types despite similar overall ImageNet accuracy. It indicates that certain model configurations are better suited for particular tasks, even with lower ImageNet scores. Why it matters: This challenges the reliance on ImageNet as a sole benchmark and highlights the need for task-specific evaluations in computer vision.
Researchers from MBZUAI presented a new algorithm at ICLR 2024 that identifies causal relationships involving both observed and latent variables. The algorithm addresses limitations of existing methods that struggle with latent variables or assume observed variables don't directly influence latent variables. The proposed algorithm can accommodate both scenarios, offering a more generalizable approach to causal discovery. Why it matters: This research advances the development of AI systems that can analyze complex data and identify causal relationships, with potential applications in fields like medicine where understanding causality is crucial for developing treatments and preventative measures.
MBZUAI Professor Agathe Guilloux developed the SigLasso model to forecast hospitalizations using real-time data from Google and Météo France during the COVID-19 pandemic. The model integrates mobility data and weather patterns to predict hospitalization rates 10-14 days in advance. SigLasso outperformed industry standards like GRU and Neural CDE in reducing reconstruction error. Why it matters: This research demonstrates the potential of AI to improve healthcare resource allocation and crisis management by accurately predicting patient flow using readily available data.
MBZUAI and Sheikh Shakbout Medical City researchers developed PECon, a deep learning method for pulmonary embolism detection using CT scans and electronic health records. PECon uses neural networks and contrastive learning to encode and align image and text data. The method aims to improve diagnosis accuracy and speed, potentially saving lives. Why it matters: This research demonstrates AI's potential to enhance medical diagnostics in the UAE, addressing a critical healthcare challenge.
MBZUAI had 22 papers accepted at ICLR 2023, with faculty Kun Zhang co-authoring seven of them. Yuanzhi Li, an affiliated assistant professor at MBZUAI, received an honorable mention for his paper on knowledge distillation. Additionally, a paper co-authored by MBZUAI President Eric Xing was recognized as a top 5% paper at the conference. Why it matters: MBZUAI's strong presence at a top-tier machine learning conference like ICLR demonstrates the university's growing influence and research capabilities in the global AI landscape.
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.