General AI research publications from MBZUAI, KAUST, TII, QCRI, and other GCC institutions, including conference papers, journal articles, and preprints.
MBZUAI's Ke Wu is developing bio-inspired robots, including a transmedia drone modeled after the diving beetle that can move in both air and water. The design will be presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025) in Hangzhou, China. The research explores how principles from nature and evolution can inform robot design, control, and learning, integrating AI with embodied intelligence. Why it matters: This work contributes to the rapidly evolving field of soft robotics and offers new insights for developing more adaptive, biologically grounded intelligent systems in the region and globally.
MBZUAI has launched the K2 Think Hackathon, an international challenge focused on building applications powered by the K2 Think open-source system for advanced reasoning. The hackathon has two stages: a global open call for proposals and an in-person build challenge in Abu Dhabi for the top 10 teams. The winning team's idea will be integrated into the K2 Think app. Why it matters: This hackathon fosters innovation in AI reasoning and provides a platform for developers to create impactful solutions using a system developed in partnership with G42.
The Abu Dhabi Centre for Emergencies, Crises and Disasters Management (ADCMC) and Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) have signed an MoU at GITEX Global 2024. The agreement focuses on AI research, smart transformation, and institutional preparedness. It includes specialized AI studies, training programs, and the development of AI systems for monitoring cyber threats. Why it matters: The partnership aims to leverage AI to enhance Abu Dhabi's emergency management and crisis response capabilities, aligning with the emirate's digital transformation vision.
MBZUAI is hosting the K2 Think Hackathon, challenging participants to develop applications using the K2 Think reasoning model developed with G42. The hackathon involves a global idea call followed by a 48-hour build challenge in Abu Dhabi for the top 10 teams. The winning feature will be integrated into the K2 Think application. Why it matters: This hackathon provides a valuable opportunity to test and shape a cutting-edge AI model, potentially leading to innovative applications in various sectors like finance and education within the UAE and beyond.
Nobel laureate Michael Spence lectured at MBZUAI on AI's impact on global economies, focusing on productivity and growth. He highlighted information asymmetries around LLMs and the risk of AI-driven bias in job screening. Spence suggests finding signals that cannot be easily imitated and increase the cost of delivering misleading information. Why it matters: The lecture brings attention to AI's transformative potential and the challenges it poses for economic models and policy in the UAE and globally.
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.
Malaria No More (MNM), Reaching the Last Mile (RLM), and MBZUAI have signed an agreement to expand the Forecasting Healthy Futures (FHF) initiative with a $5 million award from RLM. The initiative aims to address the impact of climate change on malaria and other climate-sensitive infectious diseases. MBZUAI will provide expertise to support the eradication of malaria. Why it matters: This partnership highlights the UAE's commitment to global health and leverages AI to combat climate-sensitive diseases, demonstrating a proactive approach to addressing complex global challenges.
MBZUAI President Eric Xing addressed questions about the university's global collaborations, ethical standards, and relationship to Chinese interests. He emphasized the UAE's focus on technological advancement and economic diversification through AI, highlighting MBZUAI's role in advancing this vision. Xing affirmed the university's autonomy, its commitment to Western academic values, and his lack of engagement with the Chinese government on university matters. Why it matters: The interview provides insights into the UAE's approach to AI development, emphasizing responsible global engagement and dispelling concerns about undue influence from specific countries.
MBZUAI graduate Maryam Mohamed Buty Alghfeli's master's research focused on using the metaverse to enhance intelligent transportation systems and vehicular networks. Her work proposed a framework for sensing, communication, and task offloading in the vehicular metaverse, addressing challenges related to computing and communication resource constraints. The research also considered self-sustainability and proactive learning approaches to improve network operation and serve autonomous vehicles. Why it matters: This research contributes to the development of more efficient and resilient autonomous vehicle networks, which are crucial for the future of smart cities and transportation in the UAE and beyond.
MBZUAI celebrated its Board Chairman, Dr. Sultan Al Jaber, being appointed as UAE COP28 President-Designate. MBZUAI has recently signed agreements with IBM and Malaria No More/Reaching Last Mile to better understand and mitigate the effects of climate change. The UAE will host the annual climate summit at Expo City, Dubai, from November 30 to December 12, 2023. Why it matters: This appointment highlights the UAE's leadership in climate action and the role AI can play in addressing climate change challenges.
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.
UAE President Sheikh Mohamed bin Zayed Al Nahyan visited MBZUAI and was briefed on academic programs, research areas, and partnerships. Researchers presented AI projects spanning energy, climate, medicine, genetics, media, and robotics, and two start-ups from the university’s incubator showcased their work. MBZUAI President Professor Eric Xing noted the university's ranking as 10th globally in AI fields. Why it matters: The visit underscores the UAE's commitment to developing national AI talent and building a knowledge-based economy with AI at its core.
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.
MBZUAI Associate Professor Mohammad Yaqub is focused on translating AI research into real-world healthcare solutions. His previous work includes the development of SanNav, an AI-based fetal anomaly detection system that became an FDA-approved product used by GE Healthcare and used on his own wife during pregnancy. Yaqub joined MBZUAI to help build a new model of AI research and education with a focus on interdisciplinary collaboration and industry partnerships. Why it matters: This highlights the UAE's growing focus on AI in healthcare and MBZUAI's role in bridging the gap between research and practical applications in the medical field.
MBZUAI master's graduate Rohit Bharadwaj is pursuing a Ph.D. at the University of Edinburgh, following in the footsteps of Geoffrey Hinton. His research focuses on developing generative models, specifically diffusion models, to anonymize datasets while preserving utility, addressing GDPR compliance. He aims to balance privacy protection with the need for useful data in AI systems. Why it matters: This highlights the growing importance of MBZUAI as a feeder institution for top global AI research programs and the increasing focus on privacy-preserving AI technologies.
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 graduate Daniel Gebre from Eritrea has developed iShrink, a compression pipeline that reduces the size of LLMs to run offline on mobile phones and edge devices. Gebre's master's thesis focused on enabling access to cutting-edge AI without requiring internet access, motivated by his experience in under-resourced regions. His journey to MBZUAI began with a Merit Scholarship awarded by the UAE Ministry of Education, leading him to pursue a Master of Science in Machine Learning. Why it matters: This research has the potential to democratize access to AI in areas with limited internet connectivity, particularly in regions like Gebre's homeland, Eritrea.
MBZUAI researchers presented a study at ACL 2024 on improving Arabic ASR by pre-training on dialectal Arabic. They trained three versions of the ArTST model: one on MSA, one on MSA and dialectal data, and one on MSA, dialectal, and multilingual data. Results showed that pre-training on dialectal Arabic improves ASR performance across MSA and various dialects. Why it matters: This research addresses a key challenge in Arabic NLP, given the diversity and lack of standardization in dialects, which could lead to more accurate speech recognition systems.
MBZUAI has joined the Aspen Institute’s Technology Leaders Initiative as a founding partner, alongside the Institute for Ethics in AI at the University of Oxford. This two-year program within the Aspen Global Leadership Network will convene 20 senior leaders from the global technology ecosystem. The initiative aims to explore ethical innovation, responsible leadership, and the societal impact of technology. Why it matters: The partnership highlights MBZUAI's commitment to fostering responsible AI development and ethical leadership in the technology sector.
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 researchers identified 'self-referencing causal cycles' in LLM training data that can mitigate the 'reversal curse,' where LLMs struggle with information presented in reverse order. The study, to be presented at ACL, explains that the transformer architecture's unidirectional token generation causes this issue. By leveraging the repetitive nature of information in training texts, the team developed an efficient solution to improve LLM performance. Why it matters: Overcoming the reversal curse can significantly enhance LLM accuracy and reliability, especially in tasks requiring bidirectional reasoning and understanding of context.
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 NLP master's graduate Hasan Iqbal developed OpenFactCheck, a framework for fact-checking and evaluating the factual accuracy of large language models. The framework consists of three modules: ResponseEvaluator, LLMEvaluator, and CheckerEvaluator. OpenFactCheck was published at EMNLP 2024 and accepted at NAACL 2025 and COLING 2025, with Iqbal playing an active role at COLING in Abu Dhabi. Why it matters: The development of automated fact-checking frameworks is crucial for ensuring the reliability and trustworthiness of information generated by increasingly prevalent LLMs, especially in the Arabic-speaking world.
MBZUAI doctoral student Hawau Toyin is applying AI to the identification, correction, and evaluation of stuttering, particularly in developing countries where it often goes undiagnosed. She is collaborating with the SpeechCare Center UAE and her advisor Dr. Hanan Aldarmaki to develop AI tools for faster and more accessible diagnosis and treatment. The research focuses on data collection from around the world to build an effective AI system that can analyze the various forms of stuttering. Why it matters: This research addresses a critical healthcare gap by leveraging AI to improve diagnosis and treatment of speech disorders in underserved regions.
Muhammad Arslan Manzoor became MBZUAI's first NLP Ph.D. graduate, focusing his research on media bias under Professor Preslav Nakov. His thesis, 'MGM,' explored using audience overlap graphs to predict the factuality and bias of news media, an approach that differs from traditional textual analysis. Manzoor's work aims to improve the efficiency of media profiling in real-time by leveraging relationships captured in media graphs. Why it matters: This research offers innovative methods for identifying bias in news, which is crucial for promoting informed social discourse and combating disinformation in the region.
MBZUAI valedictorian Salma Hassan developed a multimodal graph learning approach for early dementia diagnosis and prognosis. Her master's thesis focused on differential diagnosis to identify specific dementia subtypes and predicting cognitive decline in preclinical patients. The AI model leverages brain imaging, genetics, and clinical records to improve diagnostic accuracy. Why it matters: This research can improve early detection and resource allocation for dementia management, especially in developing countries.
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.
A panel discussion at Manarat Al Saadiyat, featuring MBZUAI's Elizabeth Churchill, explored the evolving relationship between the physical and digital worlds. The panel, titled 'Body as medium: InterFACES: Skin/Screen,' addressed how hyper-connectivity and digital amplification alter our understanding of the human body and its limits. Churchill highlighted the profound shift occurring as we navigate the era of AI and its implications for human beings. Why it matters: The discussion underscores the increasing importance of understanding the ethical, social, and existential questions arising from the intersection of AI and human identity in the digital age.
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 hosted a Single Cell Summer Workshop, gathering over 25 participants and speakers to explore single-cell omics data. The two-day event featured three workshops, nine talks, and networking opportunities, aiming to develop bioinformatics skills and strengthen the regional ecosystem. Workshops were led by Eduardo Beltrame (MBZUAI), Mariano Gabitto (Allen Institute), and Luke Zappia (Data Intuitive), covering topics like scvi-tools, multimodal machine learning, and benchmarking. Why it matters: This workshop signifies MBZUAI's commitment to advancing bioinformatics research in the UAE and fostering collaboration within the regional scientific community.
NYU Abu Dhabi and MBZUAI researchers have developed ARWI, a free web application to help Arabic language learners improve their writing skills in Modern Standard Arabic. ARWI provides essay prompts aligned with CEFR skill levels, features an Arabic text editor, and gives personalized feedback. The tool won the Diversity Award at the Workshop on Intelligent and Interactive Writing Assistants (In2Writing). Why it matters: This tool can help preserve the quality and personal voice of Arabic writing amid the rise of LLMs.
James Landay from Stanford University spoke at MBZUAI about human-centered AI design, arguing current approaches focus too narrowly on the user level. He proposed considering user, community, and societal impacts at all stages of AI design, not as an afterthought. Landay emphasized the need for collaboration and cooperation to implement this approach effectively. Why it matters: This highlights the growing emphasis on ethical AI development and the importance of considering broader societal impacts in the design of AI systems within the region.
MBZUAI Professor Preslav Nakov discusses Meta's shift to crowdsourced fact-checking via Community Notes, replacing third-party fact-checkers. Community Notes, originating from Twitter's Birdwatch, allows users to add context to potentially misleading posts, visible after community consensus. Research indicates this approach can reduce misinformation and lead to post retractions. Why it matters: The adoption of crowdsourcing for content moderation by major platforms like Meta could significantly impact online information quality for billions of users.
Researchers at MBZUAI presented a new Arabic dataset at NAACL to measure LLM safety, building on a Chinese dataset called 'Do Not Answer'. The dataset includes nearly 5,800 questions with challenges and harmless requests containing sensitive terms to test for over-sensitivity. The team localized cultural concepts and added 3,000 questions specific to Arabic language and culture. Why it matters: This comprehensive benchmark, accounting for the diversity of Arabic dialects and cultures, advances the development of safer and more culturally aligned LLMs for Arabic speakers.
MBZUAI researchers presented two studies at NAACL 2025 concerning how LLMs understand cultural differences, with one study winning the SAC award. One study, titled "Reading between the lines: Can LLMs identify cross-cultural communication gaps," assesses GPT-4o's ability to identify cultural references in Goodreads book reviews. The researchers created a benchmark dataset using annotations from 50 evaluators across different cultures to measure the LLM's ability to identify culture-specific items (CSIs). Why it matters: Improving LLMs' cross-cultural understanding is crucial for ensuring these models can be used effectively and equitably across diverse global contexts.
MBZUAI's Computational Biology department hosted the first Data Carpentry workshop in the UAE, a global initiative focused on foundational data science skills for researchers. The workshop included 35 participants from academia and industry, covering topics like data tidiness, cloud computing, command lines, and data wrangling. 70% of the participants were female, and several were Emirati nationals. Why it matters: This initiative addresses the growing need for computational skills in genomics research, bridging the gap between wet lab experiments and data analysis for scientists in the UAE.
MBZUAI researchers presented a new machine learning method at ICLR for uncovering hidden variables from observed data. The method, called "complementary gains," combines two weak assumptions to provide identifiability guarantees. This approach aims to recover true latent variables reflecting real-world processes, while solving problems efficiently. Why it matters: The research advances disentangled representation learning by finding minimal assumptions necessary for identifiability, improving the applicability of AI models to real-world data.
MBZUAI researchers found that only 5.7% of music in existing datasets used to train generative music systems comes from non-Western genres. They discovered that 94% of the music represented Western music, while Africa, the Middle East, and South Asia accounted for only 0.3%, 0.4%, and 0.9% respectively. The team also tested whether parameter-efficient fine-tuning with adapters could improve generative music systems on underrepresented styles, presenting their findings at NAACL. Why it matters: This research highlights the critical need for more diverse datasets in AI music generation to better serve global musical traditions and audiences.
MBZUAI researchers developed AnomalyGen, a framework using foundation models to help household robots anticipate and react to dangerous scenarios. The system uses collaborative agents to brainstorm hazards, recreates scenarios in a 3D simulation, and develops mitigation methods. AnomalyGen will be presented at the upcoming NAACL conference. Why it matters: This research advances the development of trustworthy AI for real-world applications, specifically enabling robots to proactively ensure safety in home environments.
Dr. Farida Al Hosani, a UAE public health leader and MBZUAI Executive Program graduate, has been at the forefront of the nation’s public health strategy, specializing in infectious diseases. She emphasizes the growing importance of AI in healthcare, particularly in areas like early detection, drug discovery, and public health strategies. Al Hosani has worked on several AI initiatives, including building algorithms and predictive modeling systems to bridge gaps in human health, especially in prevention. Why it matters: This highlights the increasing role of AI in transforming healthcare in the UAE, with public health leaders leveraging AI tools to improve disease prevention and treatment.
MBZUAI researchers developed a new conditional independence test (DCT) that determines the dependence of two variables when both are discrete, continuous, or when one is discrete and the other is continuous. The new test addresses cases where variables are inherently continuous but represented in discretized form due to data collection limits. The findings will be presented at the 13th International Conference on Learning Representations (ICLR) in Singapore. Why it matters: This research addresses a fundamental problem in machine learning and statistics, improving causal relationship discovery in mixed datasets common across finance, public health, and other fields.
MBZUAI graduate Svetlana Maslenkova worked with Assistant Professor Mohammad Yaqub on a project focused on the earlier detection of kidney failure using tabular data. Maslenkova's master's thesis involved predicting Acute Kidney Injury (AKI) using Electronic Health Records (EHR), specifically the MIMIC-IV v2.0 database. She found that patient weight distribution was a factor in the severity of kidney failure. Why it matters: This research highlights the potential of AI and machine learning to improve healthcare outcomes through the analysis of often-overlooked tabular data in electronic health records.
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 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.
The Augmented Humans International Conference 2025 (AHs) was held at MBZUAI in Abu Dhabi, focusing on technology's role in advancing human capabilities. The conference, in cooperation with ACM, attracted over 180 researchers to discuss innovations from AI-enhanced storytelling to AI-enhanced prosthetics. The program included talks, papers, posters, demonstrations, and workshops on topics like AR/VR interaction, bionic systems, and cognitive augmentation with AI. Why it matters: Hosting AHs at MBZUAI highlights the UAE's growing role as a hub for AI research and its applications in enhancing human potential.
Faris Almalik, an MBZUAI alumnus, discusses his journey from mechanical engineering to becoming a senior data scientist at the Department of Government Enablement – TAMM in Abu Dhabi. Almalik emphasizes his passion for continuous learning and knowledge sharing, which has driven his career across defense, education, finance, and government sectors. He published his first paper on adversarial attacks on medical imaging at MICCAI during his time at MBZUAI. Why it matters: This highlights the success of MBZUAI in producing impactful AI professionals who contribute to both research and practical applications in key sectors within the UAE.
MBZUAI researchers have developed "Culturally Yours," a reading assistant that highlights and explains culturally-specific items on webpages to help users understand unfamiliar terms. The tool addresses the "cold-start problem" by asking users for demographic information to personalize the identification of potentially unfamiliar cultural references. It was presented at the 31st International Conference on Computational Linguistics in Abu Dhabi. Why it matters: This tool can help bridge linguistic and cultural gaps, particularly for underrepresented languages and cultures, and aid businesses in reaching diverse audiences.