KAUST's Vice President for Research Jean M.J. Fréchet has been cited over 100,000 times. Fréchet's research contributions have had a significant impact in his field. Why it matters: This milestone highlights KAUST's growing influence in scientific research and the impact of its faculty.
Jean M. J. Fréchet, retired KAUST senior vice president, has been awarded the King Faisal Prize in Chemistry for his pioneering work in dendrimers, photoresists, and organic photovoltaics. His work has contributed to advancements in biotherapeutics, organic electronics, materials, and microfluidics. Fréchet is the 10th most cited chemist globally, with over 900 publications and 200 patents. Why it matters: The recognition highlights KAUST's impact on global scientific advancement and underscores the importance of investing in basic research with broad applications.
Professor Jean M.J. Fréchet, former VP at KAUST, received the 2019 King Faisal Prize in Science for his contributions to chemical science. His work includes the convergent synthesis of dendrimers, chemically amplified photoresists, and organic photovoltaics. Fréchet expressed his confidence that KAUST will contribute to scientific excellence and economic development in the Kingdom. Why it matters: The award highlights KAUST's role in fostering scientific innovation and recognizes contributions with global impact from researchers based in the Kingdom.
Four KAUST researchers were named in the "Thomson Reuters Highly Cited Researchers 2014." The researchers are Jean M.J. Frechet (Chemistry), Victor M. Calo (Computer Science), Mohamed Eddaoudi (Chemistry), and Heribert Hirt (Plant & Animal Science). The list recognizes researchers who rank in the top 1% most cited for their subject field and year of publication. Why it matters: This recognition highlights KAUST's contributions to impactful scientific research and its standing within the global research community.
MBZUAI's Institute of Foundation Models has released K2, a 70-billion-parameter, reasoning-centric foundation model. K2 is designed to be fully inspectable, with open weights, training code, data composition, mid-training checkpoints, and evaluation harnesses. K2 outperforms Qwen2.5-72B and approaches the performance of Qwen3-235B. Why it matters: This release promotes transparency and reproducibility in AI development, providing researchers with the resources needed to study, adapt, and build upon a strong foundation model.