Speaker 1: Dr. Chu Lun Alex Leung, Associate Professor in Advanced Manufacturing, University College London . Title: My journey to data-driven reliable efficient additive manufacturing (My DREAM) & Speaker 2: Dr. Chun Ann Huang, Senior Lecturer in Energy Storage Materials Title: Electrode microstructure for lithium batteries
The symposium session, organized by Centre of AI and Robotics (CAIR), Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, and Institute of Automation, Chinese Academy of Sciences (CASIA), will be a platform for researchers and practitioners to share their latest findings, breakthroughs, and applications in Medical Large Models. The symposium session will feature distinguished speakers who have made significant contributions to the field of medical AI, leveraging large and foundation models to drive innovation in healthcare. Each talk speaker will present their research and insights on how large models have enhanced medical image analysis, natural language processing in healthcare, drug discovery, and other medical applications.
为了更好地促进学术交流，尤其是交叉学科和前沿工作的同行交流，中国科学院自动化研究所与机器之心联合举办「人工智能 x 基础科学系列论坛」，尝试在更轻松和开放的交流氛围下，邀请研究者分享近期工作，讨论领域热点问题。 6月29日15:00-17:00，中国科学院自动化研究所、中国科学院香港创新院人工智能与机器人创新中心、机器之心联合举办系列论坛第六期，以「当医学插上人工智能的翅膀」为主题，特邀中科院香港创新院副教授张忠凯博士主持，多位领域专家学者做技术分享，聚焦人工智能和医学的深度融合和应用，核心受众为人工智能和医学科学领域的学生、学者。
Abstract: With the advancement of AI, sensor technologies, and data collection tools, the field of systems health monitoring and management (SHMM) have been evolved over the past several decades under different names and application domains, such as statistical process control (SPC), process monitoring, health surveillance, prognostics and health management (PHM), engineering asset management (EAM), personalized medicine, etc. There are tremendous opportunities in interdisciplinary research of system monitoring through integration of SPC, system informatics, data analytics, PHM, and personalized health management. In this talk we will share our views and experiences in SHMM, its challenges and opportunities, as well as its applications in machine health management and human health management.
The Epilepsy Navigation (EpiNav) software platform combines advanced deep learning image processing algorithms and computer assisted planning to aid neurosurgeons in stereotactic surgeries. EpiNav has been demonstrated to provide stereotactic plans with similar accuracy as expert human planners. Since 2017 EpiNav has been used prospectively to plan patient surgeries at the National Hospital of Neurology and Neurosurgery (United Kingdom) with no observed adverse effects. In this talk Dr. Sparks will overview the underpinning technologies of EpiNav, how it is being used in clinical practice, and her current research in deep learning and data-driven approaches to better understand and represent neuroimaging.
Interactive simulations applied in virtual surgery and robotics are usually required to predict the complex behavior of deformable objects and to provide relevant information for users in real-time. Therefore, real-time Finite Element (FE) simulations have to meet the antagonistic requirements of accuracy and fast computation time at the same time. One of the essential issues in interactive simulations is the presence of contact and friction. To solve the contact problem in coupled systems, constraint-based techniques have been shown to be accurate and robust, but come at a high computational cost. In this talk, we will discuss a novel method that efficiently addresses the problem using a mathematical reformulation and a GPU-based implementation. The method leverages reduced and parallelized computation, enabling the computing of the compliance matrix to no longer be a bottleneck in contact problems.