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.
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.
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.
Fudan University's Zhongyu Wei presented research on social simulation driven by LLMs, covering individual and large-scale social movement simulation. Wei directs the Data Intelligence and Social Computing Lab (Fudan DISC) and has published extensively on multimodal large models and social computing. His work includes the Volcano multimodal model, DISC-MedLLM, and ElectionSim. Why it matters: Using LLMs for social simulation could provide new tools for understanding and potentially predicting social dynamics in the Arab world.
This seminar explores vision systems through self-supervised representation learning, addressing challenges and solutions in mainstream vision self-supervised learning methods. It discusses developing versatile representations across modalities, tasks, and architectures to propel the evolution of the vision foundation model. Tong Zhang from EPFL, with a background from Beihang University, New York University, and Australian National University, will lead the talk. Why it matters: Advancing vision foundation models is crucial for expanding AI applications, especially in the Middle East where computer vision can address challenges in areas like urban planning, agriculture, and environmental monitoring.
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.
Dr. Hao Dong from Peking University presented research on addressing the challenge of limited large-scale training data in embodied AI, particularly for manipulation, task planning, and navigation. The presentation covered simulation learning and large models. Dr. Dong is a chief scientist of China's National Key Research and Development Program and an area chair/associate editor for NeurIPS, CVPR, AAAI, and ICRA. Why it matters: Overcoming data scarcity is crucial for advancing embodied AI research and enabling more sophisticated robotic applications in the region.