KAUST and AlFaisal University have signed an MOU to establish a joint M.D.-Ph.D. program, the first of its kind in Saudi Arabia. AlFaisal medical students selected for the program will enroll in a KAUST Ph.D. program focused on basic research, smart-health tools, and precision medicine. Graduates will become clinician-scientists implementing smart-health methods in the Saudi healthcare system. Why it matters: This program will cultivate a new generation of leaders in smart health and precision medicine, fostering evidence-based practices in Saudi healthcare.
KAUST Ph.D. graduate Dr. Noha Al-Harthi and doctoral student Rabab Alomairy won the German Gauss Center for Supercomputing (GCS) Award for optimizing solvers for high-performance computing applications. Their work focused on acoustic boundary integral equations, common in engineering and fluid dynamics. The award provides them access to the supercomputer "Isambard" in the UK and other opportunities. Why it matters: This recognition highlights KAUST's leading role in high-performance computing research in the Middle East and the growing expertise in supercomputing among Saudi researchers.
MBZUAI has launched the Ruwwad AI Scholars (RAIS) program, a fully funded two-year postdoctoral fellowship for Emirati Ph.D. graduates to conduct research at leading global institutions. The fellowship covers a competitive stipend, research funds, insurance, relocation support, and conference travel, with no cost to host institutions. Completion aims to strengthen eligibility for tenure-track faculty positions at MBZUAI. Why it matters: This program represents a strategic investment in developing Emirati AI talent and building a globally competitive, homegrown AI faculty to drive the UAE's research ambitions.
MBZUAI has launched master’s and Ph.D. programs in computational biology, expanding its research into life sciences. This includes projects like AIDO (AI-Driven Digital Organism) and analysis of the Emirati Genome Program. The programs are part of MBZUAI’s School of Digital Public Health and aim to integrate computational biology with precision medicine. Why it matters: This initiative supports the UAE's vision for a knowledge-based economy and its ambition to become a global center for scientific and technological progress in biotechnology and healthcare.
A new paper at ICCV 2025, co-authored by MBZUAI Ph.D. student Dmitry Demidov, introduces Dense-WebVid-CoVR, a 1.6-million sample benchmark for composed video retrieval (CoVR). The benchmark features longer, context-rich descriptions and modification texts, generated using Gemini Pro and GPT-4o, with manual verification. The paper also presents a unified fusion approach that jointly reasons across video and text inputs, improving performance on fine-grained edit details. Why it matters: This work advances video search capabilities by enabling more human-like queries, which is crucial for creative and analytic workflows that require nuanced video retrieval.
MBZUAI Ph.D. candidate Muhammad Maaz has been awarded the 2025 Google Ph.D. Fellowship in Machine Perception. Maaz is the first student from MBZUAI and the first from the Gulf region to receive this recognition, which includes funding, mentorship, and $50,000. He has published extensively in top-tier CV/NLP venues and has over 4,500 citations. Why it matters: This award highlights the growing prominence of MBZUAI and the increasing quality of AI research in the Gulf region on the global stage.
MBZUAI Ph.D. students Muhammad Maaz and Hanoona Rasheed interned at Meta, developing a vision encoder for images and videos. They created PerceptionLM, a multimodal language model, to generate synthetic video-caption data to train the Perception Encoder. The team addressed the challenge of limited labeled video data by building a multimodal language model called PerceptionLM to understand video's spatial and temporal aspects. Why it matters: This highlights MBZUAI's strength in computer vision and provides students opportunities to contribute to cutting-edge research at global tech firms.
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.