General AI research publications from MBZUAI, KAUST, TII, QCRI, and other GCC institutions, including conference papers, journal articles, and preprints.
Daisuke Kihara from Purdue University presented a seminar at MBZUAI on using deep learning for biomolecular structure modeling. His lab is developing 3D structure modeling methods, especially for cryo-electron microscopy (cryo-EM) data. They are also working on RNA structure prediction and peptide docking using deep neural networks inspired by AlphaFold2. Why it matters: Applying advanced deep learning techniques to biomolecular structure prediction can accelerate drug discovery and our understanding of molecular functions.
This article previews a presentation by Kevin Murphy (Google Brain) at MBZUAI on a unified perspective of machine learning, based on his book "Probabilistic Machine Learning: Advanced Topics". The presentation will cover the "4 pillars of ML": predictions, decisions, discovery and generation. Murphy will summarize recent methods and his own contributions in each of these tasks. Why it matters: Hosting prominent international AI researchers strengthens MBZUAI's position as a global hub for AI research and education.
MBZUAI alumnus Ahmed Sharshar is developing smaller AI models to make the technology more accessible, especially in resource-constrained environments like Egypt. His master's thesis involved creating an app that assesses lung health using mobile phone video analysis, eliminating the need for traditional medical devices. Sharshar is pursuing his Ph.D. at MBZUAI, focusing on lightweight and energy-efficient models for various applications. Why it matters: Democratizing AI through smaller, efficient models can enable broader applications and innovation across diverse sectors in the Middle East and beyond.
The article provides a basic overview of large language models (LLMs), explaining their functionality and applications. LLMs are AI systems that process and generate human-like text using transformer architecture, trained on vast datasets to predict the next word in a sequence. The piece differentiates between general-purpose, task-specific, and multimodal models, as well as closed-source and open-source LLMs. Why it matters: LLMs are foundational for advancements in Arabic NLP, as evidenced by models like MBZUAI's Jais, and understanding their mechanics is crucial for regional AI development.
Tailin Wu from Stanford presented research on using machine learning to accelerate scientific discovery and simulation at MBZUAI. The work covers learning theories from dynamical systems with improved accuracy and interpretability. It also introduces LAMP, a deep learning model optimizing spatial resolutions in simulations. Why it matters: Efficient AI-driven scientific simulation has broad implications for research in physics, biomedicine, materials science and engineering across the region.
Prof. Chun Jason Xue from the City University of Hong Kong presented research on optimizing mobile memory and storage by analyzing mobile application characteristics, noting their differences from server applications. The research explores system software designs inherited from the Linux kernel and identifies optimization opportunities in mobile memory and storage management. Xue's work aims to enhance user experience on mobile devices through mobile application characterization, focusing on non-volatile and flash memories. Why it matters: Optimizing mobile systems based on the unique characteristics of mobile applications can significantly improve device performance and user experience in the region.
A new Bayesian matrix factorization approach is explored for performance prediction in multilingual NLP, aiming to reduce the experimental burden of evaluating various language combinations. The approach outperforms state-of-the-art methods in NLP benchmarks like machine translation and cross-lingual entity linking. It also avoids hyperparameter tuning and provides uncertainty estimates over predictions. Why it matters: Accurate performance prediction methods accelerate multilingual NLP research by reducing computational costs and improving experimental efficiency, especially valuable for Arabic NLP tasks.
A panel discussion was hosted at MBZUAI in collaboration with the Manara Center for Coexistence and Dialogue. The discussion centered on the potential of multimodal machine intelligence for human-centered applications, particularly in health and wellbeing. USC Professor Shrikanth Narayanan spoke on creating trustworthy and inclusive AI that considers protected variables. Why it matters: This signals MBZUAI's interest in exploring ethical AI development and its applications for societal good, potentially driving research and policy initiatives in the region.
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.
Xiaolin Huang from Shanghai Jiao Tong University presented a talk at MBZUAI on training deep neural networks in tiny subspaces. The talk covered the low-dimension hypothesis in neural networks and methods to find subspaces for efficient training. It suggests that training in smaller subspaces can improve training efficiency, generalization, and robustness. Why it matters: Investigating efficient training methods is crucial for resource-constrained environments and can enable broader access to advanced AI.
Ivan Laptev from INRIA Paris presented a talk at MBZUAI on embodied multi-modal visual understanding, covering advancements in video understanding tasks like question answering and captioning. The talk highlighted recent work on vision-language navigation and manipulation. He argued that detailed understanding of the physical world through vision is still in early stages, discussing open research directions related to robotics and video generation. Why it matters: The discussion of robotics applications and future research directions in embodied AI could influence the direction of AI research and development in the UAE, particularly at MBZUAI.
Axel Sauer from the University of Tübingen presented research on scaling Generative Adversarial Networks (GANs) using pretrained representations. The work explores shaping GANs into causal structures, training them up to 40 times faster, and achieving state-of-the-art image synthesis. The presentation mentions "Counterfactual Generative Networks", "Projected GANs", "StyleGAN-XL”, and “StyleGAN-T". Why it matters: Scaling GANs and improving their training efficiency is crucial for advancing image and video synthesis, with implications for various applications in computer vision, graphics, and robotics.
KAUST Professor Wolfgang Heidrich is researching computational imaging systems that jointly design optics and image reconstruction algorithms. He focuses on hardware-software co-design for imaging systems with applications in HDR, compact cameras, and hyperspectral imaging. Heidrich's work on HDR displays was the basis for Brightside Technologies, acquired by Dolby in 2007. Why it matters: This research aims to advance imaging technology through AI-driven design, potentially impacting various fields from consumer electronics to scientific research within the region and globally.
MBZUAI's Incubation and Entrepreneurship Center (MIEC), launched in November 2023, is fostering AI-driven startups, including LibrAI, Audiomatic, and Limb. LibrAI is an AI safety monitoring platform founded by MBZUAI postdoctoral researcher Xudong Han. Audiomatic, created by MBZUAI students Muhammad Taimoor Haseeb and Ahmad Hammoudeh, is an AI-powered audio integration platform. Why it matters: These startups demonstrate MBZUAI's role in translating AI research into practical solutions, contributing to the UAE's innovation ecosystem and addressing real-world challenges.
MBZUAI students won an award at the Asian Conference on Computer Vision (ACCV) for their ObjectCompose method. ObjectCompose generates object-to-background variations of images to validate neural network performance. It helps developers test AI systems by adding variability to validation datasets without distorting the main object. Why it matters: This research offers a new approach to improve the robustness and reliability of computer vision models, which is crucial for real-world applications in the region.
MBZUAI researchers presented a new second-order method for optimizing neural networks at NeurIPS 2024. The method addresses optimization problems related to variational inequalities common in machine learning. They demonstrated that for monotone inequalities with inexact second-order derivatives, no faster second- or first-order methods can theoretically exist, supporting this with experiments. Why it matters: This research has the potential to reduce the computational cost of training large and complex neural networks, which could accelerate AI development in the region.
Thamar Solorio of MBZUAI served as general chair of EMNLP 2024, which hosted over 4,000 attendees. MBZUAI researchers presented nearly 50 studies, including one co-authored by Solorio and Monojit Choudhury that received an Outstanding Paper Award. Key themes included cultural awareness, machine-generated content detection, and LLM empathy and cultural representation. Why it matters: MBZUAI's strong presence at EMNLP highlights its growing influence in the international NLP research community and its focus on culturally aware AI.
Justice Connect, an Australian charity, collaborated with MBZUAI's Prof. Timothy Baldwin to improve their legal intake tool using NLP. The tool helps route legal requests, but users struggled to identify the relevant area of law, leading to delays and frustration. By applying NLP, the collaboration aims to help users more easily navigate the tool and access appropriate legal resources. Why it matters: This project demonstrates how NLP can be applied to improve access to justice and address unmet legal needs, particularly for those unfamiliar with legal terminology.
The IEEE International Conference on Data Mining (ICDM) opened in Abu Dhabi, marking the emirate as a growing hub for global AI conferences. Three faculty members from Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) hold key positions in the ICDM organizing committee. Abu Dhabi has recently hosted IROS in October and will host COLING in January 2025, with MBZUAI faculty heavily involved in these events. Why it matters: Hosting top-tier AI conferences in Abu Dhabi, with strong MBZUAI participation, enhances the region's reputation and visibility in the global AI community.
MBZUAI alumna Akbobek Abilkaiyrkyzy, who graduated with a master's in machine learning in 2022, has been involved in various domains, including industry, entrepreneurship, and sustainability. Her master's thesis focused on developing a chatbot for mental health problem detection, leading to the creation of WellRound, an app that uses data from various sources to improve mental and physical wellbeing. She further developed the app with support from the MBZUAI Incubation and Entrepreneurship Center (MIEC). Why it matters: This highlights MBZUAI's role in fostering AI innovation and entrepreneurship in the healthcare sector, as well as empowering its graduates to create solutions addressing critical societal needs.
MBZUAI researchers presented a new causal discovery method at NeurIPS that identifies relationships between deterministic and non-deterministic variables. The method builds directed graphs visualizing relationships between variables, incorporating both probabilistic and deterministic principles. The lead author, Longkang Li, aims to apply causal discovery to healthcare and biology for better understanding of diseases. Why it matters: This research advances the field of causal inference, potentially improving applications in areas like healthcare where understanding complex relationships is critical.
MBZUAI researchers presented new resources at EMNLP for improving the factuality of LLMs, including a web application for fact-checking LLM-generated text and benchmarks for evaluating automated fact-checkers. They found that current automated fact-checkers miss nearly 40% of false claims generated by LLMs. The study breaks down the fact-checking process into eight tasks, including decomposition and decontextualization, to identify where systems fail. Why it matters: This work addresses a critical challenge in the deployment of LLMs by providing tools and methods for improving their reliability and trustworthiness, which is essential for widespread adoption in sensitive applications.
Researchers from MBZUAI, University of Washington, and other institutions presented studies at EMNLP 2024 exploring how LLMs represent cultures. A survey analyzed dozens of recent studies on LLMs and culture and proposes a new framework for future research. The survey found that there is no widely accepted definition of 'culture' in NLP, making it challenging to interpret how models represent culture through language. Why it matters: This highlights a key gap in the field and emphasizes the need for a more rigorous and consistent understanding of culture in AI, especially as LLMs become more globally integrated.
Researchers from MBZUAI and Monash University presented a study at EMNLP 2024 examining LLMs' ability to interpret empathy, emotion, and morality in written stories. The study builds on a framework for modeling empathic similarity between narratives, using the EmpathicStories dataset. They are exploring ways to improve LLMs' capabilities with complex concepts like empathy, especially for applications in fields like healthcare. Why it matters: Enhancing LLMs with empathic understanding could lead to more effective and human-centered AI applications, particularly in sensitive domains requiring nuanced communication.
MBZUAI visiting professor of computer vision, Xiaojun Chang, has been named to Clarivate’s 2024 Highly Cited Researchers list, placing him in the top 1% of researchers worldwide. Chang has accumulated over 18,000 citations from around 200 papers, with his research focusing on multimodal foundation models and their applications to embodied AI and healthcare. Chang also holds positions at the University of Technology Sydney and RMIT University in Australia. Why it matters: This recognition highlights MBZUAI's growing impact and its role in fostering interdisciplinary collaboration and innovation in AI research, attracting further opportunities and collaborations.
The MBZUAI Incubation and Entrepreneurship Center (MIEC) is supporting AI startups in Abu Dhabi, leveraging its position as the region’s first AI-native incubator. In its first year, MIEC has supported nine startups that have attracted external funding exceeding internal grants by eight times. MIEC is also launching an AI-focused Mentors Network connecting international AI experts and investors with startup founders. Why it matters: MIEC's activities help grow the AI ecosystem in the UAE, which is seeing rapid growth in the number of AI companies.
Nutrigenics, an AI startup founded by MBZUAI PhD students, is developing a platform to improve dietetics. The platform features 200,000 tagged recipes and a vision language model (VLM) that analyzes meal photos with 70% accuracy. Nutrigenics aims to bridge the gap between clinical advice and daily eating habits, enabling dieticians to monitor patient progress more effectively. Why it matters: This technology can personalize nutrition at scale in the region and improve adherence to dietary guidelines, addressing a critical need in preventative healthcare.
MBZUAI Professor Timothy Baldwin reflects on his decision to move from the University of Melbourne to help build MBZUAI. He emphasizes the importance of inclusive AI education to ensure the benefits of AI are shared globally. Baldwin notes the concentration of AI innovation in a few countries, leading to disparities in language model performance for non-dominant languages. Why it matters: The article highlights MBZUAI's role in addressing the global imbalance in AI development and promoting inclusivity in AI education and research, particularly for Arabic and other underrepresented languages.
MBZUAI researchers developed Human-in-the-Loop for Prognosis (HuLP), a new AI system designed to help physicians assess cancer progression by providing information about its predictions and allowing user intervention. The system aims to foster collaboration between physicians and AI, rather than replacing doctors. It was presented at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: This research highlights the potential of AI to augment physician expertise in critical areas like cancer prognosis, improving patient care and treatment decisions.
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's Eduardo da Veiga Beltrame is developing machine learning tools for analyzing single-cell RNA sequencing data, which measures RNA in thousands of individual cells. Sequencing costs have decreased faster than Moore's Law, enabling large-scale data collection in biology. RNA sequencing provides insights into gene expression and cellular activity, crucial for personalized medicine. Why it matters: Advancements in single-cell RNA sequencing and ML analysis will accelerate personalized medicine by providing detailed insights into cellular mechanisms and disease pathways.
The 31st International Conference on Computational Linguistics (COLING 2025) will be held in Abu Dhabi in January 2025, hosted by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). COLING is a major biennial NLP and AI conference that brings together leaders from research centers, academia, and industry. The conference will feature keynote talks, presentations, workshops, and tutorials, with 1,500 expected participants. Why it matters: Hosting COLING underscores the UAE's growing role in AI and NLP research and provides a platform to address regional linguistic challenges and advance AI technologies.
MBZUAI will present its latest AI research applications at GITEX 2024, focusing on computer vision, machine learning, NLP, and robotics. The university aims to demonstrate AI's potential in transforming sectors like healthcare, industry, and media, aligning with the UAE’s vision for a knowledge-based economy. MBZUAI will also showcase AI tools designed to combat misinformation and enhance digital media reliability. Why it matters: MBZUAI's presence at GITEX highlights the UAE's commitment to AI innovation and the university's role in developing AI solutions for real-world challenges.
The AI4Bio Workshop at MBZUAI explored the intersection of AI and biology, focusing on AI-driven virtual organisms and foundation models. Eric Xing presented his vision of using AI to simulate biological activities, offering a safer alternative to physical experiments. Researchers like Le Song and Jen Philippe Vert are developing foundation models for biological systems, enhancing drug discovery and bioengineering. Why it matters: This signals the growing importance of AI in advancing biological research and healthcare innovation within the UAE and globally.
MBZUAI's computer science department, led by Xiaosong Ma, focuses on improving AI efficiency and sustainability by reducing wasted resources. Xiaosong's background in high-performance computing informs her approach to optimizing AI workloads. She aims to collaborate with experts across different AI domains at MBZUAI to address these challenges. Why it matters: Optimizing AI efficiency is crucial for reducing the environmental impact and computational costs associated with increasingly complex AI models in the GCC region and globally.
Elizabeth Churchill, chair and professor of human-computer interaction at MBZUAI, emphasizes the importance of understanding the relationship between people, technologies, and technical infrastructures, especially in the age of AI. Drawing inspiration from psychology, sociology, anthropology, and human factors, she believes HCI can help understand people's needs and how to design usable, useful, and delightful technologies. She focuses on augmenting human potential and experience through technology, while also addressing the motivations of those who misuse technology. Why it matters: This highlights MBZUAI's focus on human-centered AI design and ethical considerations, aligning technology with human needs and societal well-being.
MBZUAI will present robotics research and solutions at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) in Abu Dhabi from October 14-18. Professors Yoshihiko Nakamura and Sami Haddadin, renowned robotics experts, lead MBZUAI's robotics initiatives. MBZUAI will be located at Booth 6 at ADNEC. Why it matters: Showcasing MBZUAI's commitment to becoming a global leader in AI and robotics research, attracting top talent and fostering interdisciplinary collaboration in the UAE.
MBZUAI M.Sc. graduate Shamma Al Saedi won the Ministry of Education Entrepreneurship Challenge prize for her startup, Limb, an AI-powered physiotherapy system. The Limb app uses a personalized 3D model and pose estimation to correct patient posture and provide feedback, while the Limb API allows physiotherapists to monitor patient progress. Al Saedi credits MBZUAI's Incubation and Entrepreneurship Center (IEC) for providing knowledge, access to talks, and networking opportunities that helped her develop the startup. Why it matters: This highlights the potential of AI and entrepreneurship programs within UAE universities to foster innovative healthcare solutions and support local talent.
MBZUAI alumnus Adnan Khan is pursuing a Ph.D. at Carleton University, focusing his research on using computer vision to improve accessibility in healthcare, particularly for the visually impaired. His work builds upon his master's thesis at MBZUAI, which focused on domain generalization, enabling models to adapt across different data domains. Khan credits his experiences at MBZUAI for shaping his community spirit and career path. Why it matters: This highlights the role of AI education in fostering socially impactful research and driving innovation in healthcare accessibility in the region and beyond.
MBZUAI Professor Ivan Laptev is working to bridge the gap between data-driven AI systems and embodied agents (robots). He notes challenges in robotics including data scarcity, the need to generate new data through actions, and the requirement for real-time operation. Laptev aims to transfer innovations from computer vision to robotics, addressing these challenges to improve robots' ability to interpret and respond to the complexities of the real world. Why it matters: Overcoming these hurdles is crucial for advancing robotics and enabling robots to effectively interact with and navigate dynamic real-world environments.
MBZUAI Professor Yoshihiko Nakamura discusses his career in robotics, starting from its early days as a field. He notes the initial skepticism towards robotics as an academic discipline in the 1970s and its gradual formalization. Nakamura's research is driven by the mathematics of movement, optimization, and non-linearity, drawing inspiration from neuroscience, psychology, and linguistics. Why it matters: Nakamura's insights provide a historical perspective on the evolution of robotics research and highlight the interdisciplinary nature of the field, with implications for the future of AI development in the region.
MBZUAI researchers are studying how AI can be used to combat disinformation and improve news verification during elections, as AI amplifies the volume and speed of fake news. Dilshod Azizov is using machine learning to spot patterns in news that will improve verification, while Preslav Nakov's FRAPPE system identifies persuasive techniques and framing in news articles. FRAPPE uses machine learning and NLP to analyze news presentation and reporting, aiming to help users understand the underlying context of news. Why it matters: This research highlights the potential of AI to both negatively and positively impact democratic processes, emphasizing the need for tools to analyze and verify information in the face of increasing AI-generated disinformation.
Researchers from MBZUAI and other institutions presented a study at ACL 2024 on combatting misinformation by identifying misrepresented scientific research. They compiled a dataset called MISSCI, comprised of real-world examples of misinformation gathered from the HealthFeedback fact-checking website. The annotators classified the different types of errors in reasoning into nine different classes. Why it matters: This work addresses a critical need to combat the spread of scientific falsehoods online, especially given the challenges of manual fact-checking.
MBZUAI researchers presented a study at NAACL 2024 analyzing errors made by open-source LLMs when solving math word problems. The study, led by Ekaterina Kochmar and KV Aditya Srivatsa, investigates characteristics that make math word problems difficult for machines. Llama2-70B was used to test the ability of LLMs to solve these problems, revealing that LLMs can perform math operations correctly but still give the wrong answer. Why it matters: The research aims to improve AI's ability to understand and solve math word problems, potentially leading to better educational applications and teaching methods.
MBZUAI welcomed its largest cohort of 209 graduate students for Fall 2024, selected from over 4800 applications. The new cohort includes the first students in computer science and robotics, representing 36 countries with 44 Emiratis. MBZUAI is also creating new departments dedicated to human-computer interaction, statistics and data science, and computational biology. Why it matters: This expansion strengthens Abu Dhabi's AI ecosystem and MBZUAI's position as a leading global AI institution, providing a diverse talent pool for the UAE's innovation goals.
MBZUAI researchers presented a new strategy for handling complex optimization problems in machine learning at ICLR 2024. The study, a collaboration with ISAM, combines zeroth-order methods with hard-thresholding to address specific settings in machine learning. This approach aims to improve convergence, ensuring algorithms reach quality solutions efficiently. Why it matters: Improving optimization techniques is crucial for advancing machine learning models used in various applications, potentially accelerating development and enhancing performance.
MBZUAI highlighted five Emirati women making contributions in AI, entrepreneurship, and leadership, coinciding with Emirati Women’s Day. Fatima AlKhoori is pursuing a Ph.D. at MBZUAI, researching transformer models for autonomous vehicle traffic sign recognition using UAE and German datasets. Dr. Hanan Aldarmaki, an MBZUAI assistant professor, specializes in Arabic speech and language processing. Why it matters: Showcasing Emirati women in AI underscores the UAE's commitment to gender diversity and national talent development in advanced technology fields.
MBZUAI researchers have expanded LLM safety research to Chinese, presenting their work at the 62nd Annual Meeting of the Association for Computational Linguistics in Bangkok. They developed an open-source Chinese dataset of 3,000 prompts translated and localized from the English "Do-Not-Answer" dataset. The dataset includes a "region-specific sensitivity" category to address unique safety risks for Chinese speakers, evaluating if models are over-sensitive in identifying innocuous questions as harmful. Why it matters: This research addresses a critical gap in LLM safety evaluation, ensuring that language models are both safe and effective for diverse linguistic and cultural contexts, particularly in regions with unique sensitivities.
MBZUAI researchers presented a new uncertainty quantification method at ACL to identify hallucinations in LLMs, called claim conditioned probability (CCP). CCP leverages the internal token probabilities generated by the LLM itself to highlight claims with low confidence. Unlike external fact-checking methods, CCP is computationally efficient as it uses probabilities already computed by the model. Why it matters: This research offers a practical approach to mitigate the impact of LLM hallucinations by highlighting potentially unreliable information, improving the trustworthiness of these models, especially for Arabic LLMs.
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) emphasizes campus life to support its growing international student population. The Masdar City campus provides living quarters, communal spaces, and services for student wellbeing. MBZUAI offers advanced AI labs with nearly 1,000 Nvidia GPU servers and high-performance computing resources, along with technology-enhanced classrooms and a well-stocked library. Why it matters: This focus on comprehensive campus resources signals MBZUAI's commitment to attracting and supporting top AI talent in the UAE.