Researchers have developed a scalable pre-screening framework that integrates climate and remote sensing data to identify cost-efficient sites for sustainable dryland restoration, using Saudi Arabia as a case study. The framework employs machine learning models to derive a Climate Suitability Score (CSS), which captures climatic dependencies on vegetation persistence. National-scale prediction maps were generated using multi-year ERA5-Land data for Saudi Arabia, leading to the identification of thirteen priority locations with an estimated potential for a 2.5-fold increase in vegetation coverage. Why it matters: This approach significantly reduces the search space and costs associated with restoration efforts, supporting more resilient and sustainable ecosystem recovery planning in water-limited regions of the Middle East.
KAUST researchers developed a machine learning algorithm to control a deformable mirror within the Subaru Telescope's exoplanet imaging camera, compensating for atmospheric turbulence. The algorithm, which computes a partial singular value decomposition (SVD), outperforms a standard SVD by a factor of four. The KAUST team received a best paper award at the PASC Conference for this work, which has already been deployed at the Subaru Telescope. Why it matters: This advancement enables sharper images of exoplanets, facilitating their identification and study, and showcases the impact of optimizing core linear algebra algorithms.
MBZUAI has published 674 papers in 2023 and holds a global ranking of 18 in AI, CV, ML, NLP, and robotics according to CSRankings. The university presented 30 papers at ICCV, will present 53 papers at NeurIPS, and has 44 papers at EMNLP 2023. MBZUAI was also awarded its first patent by the US Patent Office for a system and method for handwriting generation. Why it matters: This demonstrates the rapid growth and increasing prominence of MBZUAI as a leading AI research institution in the region and globally.
MBZUAI researchers developed a machine-learning method to predict antimicrobial resistance (AMR) by analyzing electronic health records. The system predicts if a patient will experience AMR when prescribed an antibiotic or if infected with a bacterium. Published in Scientific Reports, the innovation helps physicians identify patients at risk for AMR by using patient demographics, lab results, and physician notes. Why it matters: This approach can help combat the rise of drug-resistant bacteria by providing timely predictions and supporting more informed prescription decisions.
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.
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.
DERC is partnering with EPFL in Switzerland on a four-year project using EMTR and ML to study electromagnetic disturbance localization in PCBs. Professor Farhad Rachidi (EPFL) and Dr. Nicolas Mora (DERC) will mentor a PhD student. The collaboration builds on prior relationships between DERC researchers and Prof. Rachidi's lab. Why it matters: The partnership strengthens DERC's methodological expertise and international recognition in electromagnetic studies, potentially leading to further collaborations.
KAUST, in collaboration with the Ministry of Communications and Information Technology (MCIT), will host the second edition of the MENA Machine Learning Winter School (MenaML) from January 24-29, 2026. The program will cover the latest developments in intelligent model engineering, AI for science, and high-efficiency computing technologies with representatives from 16 international institutions. 300 researchers will be selected from over 2,300 applicants to participate in the intensive academic program. Why it matters: The MenaML winter school strengthens KAUST's role as a regional hub for AI research and contributes to human capital development in AI across the MENA region.
Researchers at Khalifa University have developed an AI system capable of predicting cardiovascular disease (CVD) risks up to 12 years in advance. The AI model uses data from the Framingham Heart Study to assess long-term CVD risk factors. It outperforms existing methods in predicting CVD incidence over extended periods. Why it matters: This advancement could significantly improve preventative healthcare strategies in the UAE and globally by enabling earlier interventions for individuals at high risk of heart disease.
KAUST has launched the Center of Excellence for Smart Health (KCSH), chaired by Professor Imed Gallouzi and co-chaired by Professor Xin Gao. The center aims to develop smart-health technologies, integrating AI, machine learning, and other disciplines to address health challenges. KCSH will collaborate with partners across Saudi Arabia to focus on personalized diagnosis, treatment, and prevention of diseases. Why it matters: This initiative addresses the evolving healthcare needs of Saudi Arabia's aging population and high prevalence of genetic diseases, positioning the Kingdom as a leader in smart health solutions.
KAUST is expanding its Lifelong Learning Initiative, now called KAUST Academy, to meet growing demand for AI and machine learning training in Saudi Arabia. The Academy offers short courses and certificates in STEM fields, targeting both recent graduates and professionals. KAUST faculty and industry partners contribute to the program, which is free for Saudi nationals and residents. Why it matters: The KAUST Academy aims to upskill the Saudi workforce and support the Kingdom's Vision 2030 and 2050 goals by providing accessible, high-quality training in AI and other key areas.
This paper proposes a smart dome system for mosques that uses machine learning to automatically control dome ventilation based on weather conditions and outside temperatures. The system was tested on the Prophet Mosque in Saudi Arabia using K-Nearest Neighbors and Decision Tree algorithms. The Decision Tree algorithm achieved a higher accuracy of 98% compared to 95% for the k-NN algorithm.
KAUST and the Saudi Electricity Company (SEC) collaborated to reduce non-technical losses in the Saudi power sector using machine learning. KAUST Visualization Core Lab (KVL) developed models using five years of SEC billing data from the Riyadh area to predict electricity usage and detect anomalous billing transactions. SEC estimates it could recover at least 73,000,000 SAR in lost revenue by correcting anomalies identified by KAUST models. Why it matters: This partnership demonstrates the potential of AI to address inefficiencies and fraud in critical infrastructure sectors in Saudi Arabia.
MBZUAI and UC Berkeley held a joint workshop on machine learning, featuring discussions on online learning, fair allocation in dynamic mechanism design, and causal inference. Michael I. Jordan, Laureate Professor and Honorary Program Director at MBZUAI, highlighted the institute's rapid growth during his visit. Researchers explored methods for enhancing the properties of large, complex models, such as calibration, fairness, and robustness. Why it matters: Such collaborations advance AI research and foster knowledge exchange between leading global experts and regional institutions like MBZUAI.
A talk discusses the challenges of single-cell data analysis, such as feature sparsity and the effects of rare cells. AI/ML strategies are uniquely positioned to model this data. ImYoo, a startup founded in 2021, is applying single-cell model architectures for unsupervised discovery of patient groupings and predicting sample-level phenotypical data in autoimmune disease. Why it matters: This highlights the growing application of AI/ML in analyzing single-cell data for population-scale human health studies, an area ripe for innovation and improvement in the Middle East's growing biotech sector.
A recent talk at MBZUAI discussed "Green Learning" and Operational Neural Networks (ONNs) as efficient alternatives to CNNs. ONNs use "nodal" and "pool" operators and "generative neurons" to expand neuron learning capacity. Moncef Gabbouj from Tampere University presented Self-Organized ONNs (Self-ONNs) and their signal processing applications. Why it matters: Exploring more efficient AI models is crucial for sustainable development of AI in the region, as it addresses computational resource constraints and promotes broader accessibility.
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.
Ahmed Elhag, a PhD student at the University of Oxford, presented a new training procedure that approximates equivariance in unconstrained machine learning models via a multitask objective. The approach adds an equivariance loss to unconstrained models, allowing them to learn approximate symmetries without the computational cost of fully equivariant methods. Formulating equivariance as a flexible learning objective allows control over the extent of symmetry enforced, matching the performance of strictly equivariant baselines at a lower cost. Why it matters: This research from a speaker at MBZUAI balances rigorous theory and practical scalability in geometric deep learning, potentially accelerating drug discovery and design.
NYU Abu Dhabi hosted a talk by Prof. Debdeep Mukhopadhyay on the intersection of machine learning and hardware security. The talk covered using ML/DL for side-channel attacks, leakage assessment in crypto-devices, and threats to hardware security primitives. Prof. Mukhopadhyay is a visiting professor at NYU Abu Dhabi and Institute Chair Professor at IIT Kharagpur. Why it matters: The talk highlights the growing importance of hardware security in modern systems and the role of machine learning in both attacking and defending hardware vulnerabilities.
Abdulrahman Mahmoud, a postdoctoral fellow at Harvard University, discusses software-directed tools and techniques for processor design and reliability enhancement in ML systems. He emphasizes the need for a nuanced approach to numerical data formats supported by robust hardware. He advocates for integrating reliability as a foundational element in the design process. Why it matters: This research addresses the critical challenge of hardware reliability in AI processors, particularly relevant as the field moves towards hardware-software co-design for sustained growth.
MBZUAI is hosting a short course on developing open-source machine learning packages. The course will be led by Chih-Jen Lin, an affiliated professor at MBZUAI and distinguished professor at National Taiwan University, who has developed widely used ML packages like LIBSVM and LibMultiLabel. The course will cover topics such as starting a project, choosing functionalities, and identifying research problems from user feedback. Why it matters: This course can help improve the quality and usability of open-source machine learning tools coming from the region's research institutions.
Researchers at MBZUAI have developed a new machine learning method called survival rank-n-contrast (SurvRNC) to improve survival models for cancer prognoses. The method is designed to predict survival times for head and neck cancer patients using multimodal data while accounting for censored data (missing values). Numan Saeed presented the team’s work at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: Accurate prognoses can significantly improve patient outcomes, and this research contributes to advancements in machine learning techniques for handling complex and incomplete medical data.
MBZUAI Professor Kun Zhang is developing machine learning techniques to identify hidden causal variables, which are underlying concepts driving cause-and-effect relationships. Zhang and colleagues from Carnegie Mellon University are presenting a new approach for this at ICML 2024. Their method, causal representation learning, assumes that measured variables are generated by unobserved latent variables. Why it matters: Uncovering hidden causal relationships can significantly advance understanding in various fields by revealing the underlying mechanisms driving observed phenomena.
MBZUAI Ph.D. graduate Hilal Mohammad Hilal AlQuabeh researched methods to improve the efficiency of machine learning algorithms, specifically focusing on pairwise learning and multi-instance learning. Pairwise learning teaches AI to make decisions by comparing options in pairs, useful for ranking and anomaly detection. Multi-instance learning involves learning from sets of data points, applicable in areas like drug discovery. Why it matters: Optimizing AI for low-resource environments expands its accessibility and applicability in critical sectors like healthcare and remote area operations.
The first International Olympiad in AI for high school students will be held in Bulgaria from August 9-15, 2024. Organized by the LERAI Foundation, the competition will test students on machine learning, computer vision, and natural language processing. Sponsors include Google and MBZUAI, with organizers hoping participants pursue further AI education and careers. Why it matters: This event aims to cultivate global AI talent and could increase interest in AI education and careers in the GCC region, particularly at MBZUAI.
MBZUAI researchers are developing AI applications for malaria prevention in Indonesia using sensory data fusion and digital twins. Another MBZUAI team is using machine learning and computer vision to detect cardiovascular disease from CT scans in collaboration with the University of Oxford. AI-powered remote patient monitoring is also being explored for proactive interventions and chronic disease management. Why it matters: These projects demonstrate the potential of AI to address healthcare challenges in underserved communities and improve disease prevention and management in the region.
Researchers at MBZUAI developed a method to measure vital signs using webcams by analyzing color intensity changes in facial blood flow. They built a digital twin system that uses machine learning to combine heart rate, respiratory rate, and blood oxygen level measures. The system displays real-time vital sign information, enabling remote patient triage. Why it matters: This research contributes to the advancement of telemedicine, potentially improving healthcare access in underserved regions and aligning with UN Sustainable Development Goal #3.
MBZUAI held its Class of 2023 commencement ceremony, graduating 59 students with master's degrees in CV, ML, and NLP. The ceremony was attended by UAE officials including Sheikh Hamed bin Zayed Al Nahyan and Dr. Sultan bin Ahmed Al Jaber. Dr. Al Jaber emphasized AI's role in addressing global challenges and supporting the UAE's economic diversification and sustainability goals. Why it matters: The graduation highlights the UAE's ongoing investment in AI talent development and its focus on leveraging AI to address critical challenges in climate, healthcare, and education.
MBZUAI researchers are using federated learning to optimize energy production and use in microgrids, balancing individual and grid-level needs with a focus on sustainability. They presented a multi-agent framework called MAHTM at the ICLR 2023 workshop, aiming to minimize the carbon footprint of electrical grids. The system uses three layers of decision-making agents to minimize cost, decrease carbon impact, and balance production. Why it matters: This research offers a novel approach to integrating renewable energy sources into existing grids, potentially accelerating the transition to more sustainable energy systems in the region and globally.
MBZUAI valedictorian Shahd AlShamsi is using AI and ML to develop personalized cognitive healthcare, shifting treatment from reaction to prevention. Her master's research involves a digital twin framework that integrates representations of a person’s cognitive experience using deep learning models and EEG data. She hopes to develop a mobile application to extend her work to personalized mental health. Why it matters: This research highlights the potential of AI to improve personalized healthcare in the UAE and beyond, and demonstrates the contributions of Emirati researchers.
MBZUAI President Eric Xing has been named an ACM Fellow for his contributions to machine learning algorithms, architectures, and applications. His research focuses on machine learning, statistical methodology, and large-scale computational systems. As MBZUAI’s first president, Xing has facilitated the university's growth in AI research. Why it matters: The recognition of MBZUAI's president highlights the university's growing prominence and commitment to AI research and development in the region.
Two student teams from MBZUAI won top prizes at the inaugural Agritech Hackathon (“Agrithon”) organized by ADAFSA. The “Masdar Boys” team developed a dashboard integrating ML models for plant disease diagnosis, optimal animal clinic placement, and disease outbreak zone classification. The “Green AI” team built a machine learning framework for plant disease classification, winning second prize. Why it matters: This highlights the growing role of AI in addressing food security challenges in the UAE and the region, with potential for real-world applications through ADAFSA's interest in further developing the students' work.
MBZUAI Assistant Professor Qirong Ho is researching AI operating systems to standardize algorithms and enable non-experts to create AI applications reliably. He emphasizes that countries mastering mass production of AI systems will benefit most from the Fourth Industrial Revolution. Ho is co-founder and CTO at Petuum Inc., an AI startup creating standardized building blocks for affordable and scalable AI production. Why it matters: This research aims to democratize AI development and promote widespread adoption across industries in the UAE and beyond.
MBZUAI has opened graduate admissions for the 2023/2024 academic year, offering master’s and doctoral programs in computer vision, machine learning, and natural language processing. The university ranks in the top 25 globally in AI, computer vision, machine learning, and NLP, according to Computer Science Rankings. In Fall 2022, MBZUAI welcomed 125 new students, including 22 doctoral and 105 master’s students, bringing the total student population to over 250. Why it matters: This signals continued growth for MBZUAI as a leading AI research institution in the region, attracting international talent and contributing to the development of AI expertise in the UAE.
MBZUAI faculty and researchers had 27 papers accepted at the 2022 NeurIPS conference. 12 MBZUAI faculty members have at least one paper accepted, with Professor Kun Zhang leading with 10 papers. Other faculty with accepted publications include Eric Xing, Le Song, and Fahad Khan. Why it matters: This achievement highlights MBZUAI's growing prominence in the global machine learning research community.
This article discusses adversarial training (AT) as a method to improve the robustness of machine learning models against adversarial attacks. AT aims to correctly classify data and ensure no data fall near decision boundaries, simulating adversarial attacks during training. Dr. Jingfeng Zhang from RIKEN-AIP will present on improvements to AT and its application in evaluating and enhancing the reliability of ML methods. Why it matters: As ML models become more prevalent in real-world applications in the GCC region, ensuring their robustness against adversarial attacks is crucial for maintaining their reliability and security.
MBZUAI faculty members led or co-led several top global AI conferences in 2022, including ACL, ICML, CLeaR, and UAI. Faculty members Timothy Baldwin, Le Song, Kun Zhang, and Preslav Nakov held leadership positions at these conferences. According to CSRankings, MBZUAI ranks in the top 30 globally in AI, computer vision, machine learning, and NLP. Why it matters: This highlights MBZUAI's growing influence and reputation in the global AI research community, solidifying its position as a leading institution in the field.
Najwa Aaraj, Chief Researcher at the Cryptography Research Centre at TII, has joined MBZUAI as the first female faculty member in the Machine Learning Department. Aaraj leads R&D of cryptographic technologies, including post-quantum cryptography and lightweight cryptographic libraries. Her research will focus on the intersection of cryptography, cybersecurity, and machine learning, including using ML for cryptanalysis and protecting ML models with cryptography. Why it matters: This appointment strengthens MBZUAI's expertise in a critical area of AI security and cryptography, fostering cross-disciplinary research and innovation in the UAE.
MBZUAI Associate Professor Martin Takáč is working on high-performance computing and machine learning with applications in logistics, supply chain management, and other areas. His research focuses on using AI to improve precision and efficiency in tasks like predicting demand and optimizing delivery routes. Takáč's interests include imitative learning, predictive modeling, and reinforcement learning to enable AI to mimic human behavior and predict future outcomes. Why it matters: This research contributes to the development of more efficient and reliable AI systems that can be applied to a wide range of industries in the UAE and beyond.
MBZUAI President Eric Xing has been elected a fellow of the American Statistical Association (ASA) for 2022. The ASA recognized Xing for pioneering machine learning research in areas like structured inference and probabilistic graphical models, as well as his entrepreneurship and leadership in AI education. He was previously a professor at CMU and the founder of Petuum Inc. Why it matters: This recognition highlights the growing prominence of MBZUAI and its leadership in the international AI research community.
Professor Le Song has joined MBZUAI as Deputy Department Chair of the Machine Learning Department. He brings decades of experience from institutions like Georgia Tech, Google Research, and Carnegie Mellon University. Le Song's research focuses on machine learning methods and algorithms for complex and dynamic data, with over 160 papers published in top ML conferences. Why it matters: This appointment bolsters MBZUAI's machine learning department and signals the university's commitment to attracting world-class AI talent to the UAE.
This article discusses distribution shifts in machine learning and the use of importance weighting methods to address them. Masashi Sugiyama from the University of Tokyo and RIKEN AIP presented recent advances in importance-based distribution shift adaptation methods. The talk covered joint importance-predictor estimation, dynamic importance weighting, and multistep class prior shift adaptation. Why it matters: Understanding and mitigating distribution shifts is crucial for deploying robust and reliable AI models in real-world scenarios within the GCC region and beyond.