MBZUAI Professor Chih-Jen Lin gave a keynote at the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval in Taipei. Lin's address, titled ‘On the “Rough Use” of Machine Learning Techniques’, focused on instances where machine learning techniques are employed inappropriately, using examples from graph representation learning and deep neural networks. He advocated for the development of high-quality, user-friendly software to improve the practical application of machine learning and mitigate misuse. Why it matters: Showcases MBZUAI's faculty expertise and contributions to the discussion on responsible AI research and deployment on a global stage.
MBZUAI is hosting a short course on developing open-source machine learning packages. The course will be led by Chih-Jen Lin, an affiliated professor at MBZUAI and distinguished professor at National Taiwan University, who has developed widely used ML packages like LIBSVM and LibMultiLabel. The course will cover topics such as starting a project, choosing functionalities, and identifying research problems from user feedback. Why it matters: This course can help improve the quality and usability of open-source machine learning tools coming from the region's research institutions.
KAUST Discovery Ph.D. student Chun-Ho Lin received the best paper award at the 2nd International Symposium on Devices and Application of Two-dimensional Materials in June 2016. The award recognizes Lin's contributions to the field of two-dimensional materials. Why it matters: Recognition of KAUST student research highlights the university's contributions to advanced materials science.
Former KAUST President Professor Choon Fong Shih was presented with the Graduate School of Arts and Sciences (GSAS) Centennial Medal by Harvard University in May. Shih received his Ph.D. in applied mathematics from Harvard in 1973 and was recognized for his contributions to knowledge and society. He served as the founding president of KAUST from 2008 and previously held positions at the National University of Singapore and GE Corporate Research Lab. Why it matters: The award recognizes the impact of a key figure in KAUST's early development and highlights the university's connection to globally recognized researchers and institutions.
Qirong Ho, co-founder and CTO of Petuum Inc., will be contributing to the "ML Systems for Many" initiative. Petuum is recognized for creating standardized building blocks for AI assembly. Ho also holds a Ph.D. from Carnegie Mellon University and is part of the CASL open-source consortium. Why it matters: Showcases the ongoing efforts to democratize AI development and deployment, making it more accessible and sustainable, although the specific initiative is not further detailed.
KAUST Ph.D. student Jian Cao received a best paper award from the American Statistical Association (ASA) for his paper on computing high-dimensional normal and Student-t probabilities. The paper uses Tile-Low-Rank Quasi-Monte Carlo and Block Reordering. Cao, a member of Professor Marc Genton's group, will be recognized at the ASA's Joint Statistical Meetings. Why it matters: This award highlights KAUST's strength in high-performance computing and statistical research, contributing to advancements in handling complex, high-dimensional datasets.
Daisuke Kihara from Purdue University presented a seminar at MBZUAI on using deep learning for biomolecular structure modeling. His lab is developing 3D structure modeling methods, especially for cryo-electron microscopy (cryo-EM) data. They are also working on RNA structure prediction and peptide docking using deep neural networks inspired by AlphaFold2. Why it matters: Applying advanced deep learning techniques to biomolecular structure prediction can accelerate drug discovery and our understanding of molecular functions.
Song Chaoyang from the Southern University of Science and Technology (SUSTech) presented research on Vision-Based Tactile Sensing (VBTS) for robot learning, combining soft robotic design with learning algorithms to achieve state-of-the-art performance in tactile perception. Their VBTS solution demonstrates robustness up to 1 million test cycles and enables multi-modal outputs from a single, vision-based input, facilitating applications such as amphibious tactile grasping and industrial welding. The talk also highlighted the DeepClaw system for capturing human demonstration actions, aiming for a universal interaction interface. Why it matters: This research advances embodied intelligence by improving robot dexterity and adaptability through enhanced tactile sensing, which is crucial for complex manipulation tasks in various sectors such as manufacturing and healthcare within the region.