MBZUAI's Executive Program held a module on AI ethics, safety, and societal impacts, led by Professors Tom Mitchell and Justine Cassell. The session covered machine learning bias, privacy, AI's impact on jobs and education, and the ethical use of AI. Forty-two participants from ministerial leadership and top industry executives are part of the first cohort. Why it matters: This highlights MBZUAI and the UAE's commitment to ethical AI development as part of building a knowledge-based economy.
American artist Rachel Sussman spoke at KAUST's 2019 Winter Enrichment Program about her project documenting the world's oldest living organisms. Sussman photographed 30 species alive for over 2,000 years, including trees, coral, and bacteria. She collaborated with 30 scientists to identify and document these organisms. Why it matters: The lecture highlights KAUST's interdisciplinary approach to knowledge, connecting art, science, and philosophy to explore concepts of time and longevity.
KAUST alumna Justine Braguy co-founded Thya Technology, an AI startup that automates image and video analysis. The company's platform allows users to upload and label images to generate AI detection models without coding. Thya Technology was born out of a tool developed at KAUST to count plant seeds and won the TAQADAM showcase in 2022. Why it matters: This highlights KAUST's role in fostering AI entrepreneurship and translating research into practical applications, particularly in automating scientific processes.
Janet Kelso from the Max Planck Institute and Sudhir Kumar from Temple University discussed evolutionary biology in a KAUST Facebook Live interview. Kelso's research focuses on interactions between modern humans and Neanderthals, finding similarities in DNA and benefits for environmental adaptation. Kumar's work, highly cited, involves big data analyses in evolutionary biology. Why it matters: The interview highlights KAUST's engagement with international experts in bioinformatics and evolutionary biology, promoting interdisciplinary research and knowledge dissemination.
KAUST Associate Professor Jürgen Kosel has been named a distinguished lecturer of the Institute of Electrical and Electronics Engineers (IEEE) Sensors Council for 2020-2022. Kosel's research focuses on sensors and transducers with applications in animal monitoring, precision farming, Formula One racing, and biomedical instruments. His group is also developing magnetic devices for high-density data storage and cancer treatment. Why it matters: This recognition highlights KAUST's contributions to sensor technology and its potential impact on diverse fields, including healthcare in developing regions.
MBZUAI hosted a talk on causal AI, featuring Professor Jin Tian from Iowa State University. The talk covered enriching AI systems with causal reasoning capabilities, moving AI beyond prediction to understanding. Professor Tian shared research on causal inference and estimating causal effects from data, using a novel estimator with double/debiased machine learning (DML) properties. Why it matters: Causal AI can improve the explainability, robustness, and adaptability of AI systems, addressing limitations of purely statistical models.
The Communications and Computing Systems Lab (CCSL) at KAUST received two awards in the International Telecommunication Union AI for Good Machine Learning Challenge and tinyML Hackathon Challenge 2023: Pedestrian Detection. The KAUST team's solution achieved high accuracy in pedestrian identification using event-based cameras, while consuming less power and achieving lower latency. They also received an award for innovative use of "Edge Impulse" for building datasets and training models. Why it matters: This recognition highlights KAUST's growing influence in AI research, particularly in edge computing and computer vision applications for public safety.
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.