KAUST Ph.D. student Zhijie Chen won the Faraday Division Poster Prize at the Royal Society of Chemistry’s "New Directions in Porous Crystalline Materials" Faraday Discussion for his poster entitled "Applying the Power of Reticular Chemistry to Finding the Missing alb-MOF Platform Based on the (6, 12)-Coordinated Edge-Transitive Net." Chen's research focuses on the reticular synthesis of metal-organic frameworks and their applications in gas storage and separation. He is a member of Professor Mohamed Eddaoudi's FMD3 research group. Why it matters: This award recognizes the high-quality research being conducted at KAUST and highlights the university's contributions to the field of advanced materials.
Dr. Zeke Xie from HKUST(GZ) presented research on noise initialization and sampling strategies for diffusion models. The talk covered golden noise for text-to-image models, zigzag diffusion sampling, smooth initializations for video diffusion, and leveraging image diffusion for video synthesis. Xie leads the xLeaF Lab, focusing on optimization, inference, and generative AI, with previous experience at Baidu Research. Why it matters: The work addresses core challenges in improving the quality and diversity of generated content from diffusion models, a key area of advancement for AI applications in the region.
Zesheng Dong, a KAUST alumnus with a master's degree in chemical science (2011), is working as a chemical scientist at SABIC since 2011. At SABIC, he provides analytical support to improve the accuracy and efficiency of the company's research. Dong advises current KAUST students to study and do research wholeheartedly. Why it matters: The success of KAUST alumni in key Saudi industries like SABIC highlights the university's role in developing talent for the Kingdom's economic diversification goals.
Ang Chen from the University of Michigan presented a talk at MBZUAI on reducing cloud manageability burdens. The talk covered detecting semantic errors before cloud deployment and curating datasets for automated generation of cloud management programs. He introduced the concept of "cloudless computing" to free tenants from cloud management tasks. Why it matters: This research direction could simplify cloud infrastructure management for businesses in the UAE and beyond, allowing them to focus on core activities.
Xi Chen from NYU Stern gave a talk at MBZUAI on digital privacy in personalized pricing using differential privacy. The talk also covered research in Web3 and decentralized finance, including delta hedging liquidity positions on Uniswap V3. Chen highlighted open problems in decentralized finance during the presentation. Why it matters: The talk suggests MBZUAI's interest in exploring the intersection of AI, privacy, and blockchain technologies, reflecting growing trends in data protection and decentralized systems.
Wanfang Chen and Yuxiao Li, a married couple, came to KAUST in August 2016 to pursue Ph.D. studies in statistics under the supervision of Distinguished Professor Marc Genton and Professor Ying Sun respectively. Prior to KAUST, they obtained degrees from the Beijing Institute of Technology, with Chen also attending Xiamen University and Li attending the University of California, Irvine. Both students have completed their first academic papers and have submitted the papers to journals. Why it matters: This highlights KAUST's ability to attract international talent in STEM fields, contributing to its research output and global reputation.
Dr. Pengtao Xie joins MBZUAI as an assistant professor focusing on healthcare and machine learning, inspired by human learning. He is developing automated machine learning methods for healthcare, such as neural architectures for pneumonia detection from chest X-rays. His method achieves state-of-the-art performance with 95% accuracy and is under review by Nature Scientific Report. Why it matters: This appointment strengthens MBZUAI's research capabilities in healthcare AI and signals the university's commitment to attracting top global talent to Abu Dhabi.
Xiuying Chen from KAUST presented her work on improving the trustworthiness of AI-generated text, focusing on accuracy and robustness. Her research analyzes causes of hallucination in language models related to semantic understanding and neglect of input knowledge, and proposes solutions. She also demonstrated vulnerabilities of language models to noise and enhances robustness using augmentation techniques. Why it matters: Improving the reliability of AI-generated text is crucial for its deployment in sensitive domains like healthcare and scientific discovery, where accuracy is paramount.