1.      Chang Gung University plans to establish the Institute of Health Data Science (HDS) next August, with the first admissions starting this October! 2.      Introduction of HDS: As AI technology grows by leaps and bounds, researchers in the medical field need to process large amounts of data using machine learning and artificial intelligence to innovatively solve clinical and health-related problems. Currently, existing data science programs mainly focus on engineering science and lack of professional training in the field of health data science. Therefore, the” Institute of Health Data Science “is going to be established in 2025 to cultivate graduate-level researchers who can deploy AI to process health data, conduct predictive analyses and contribute to the development of the global eHealth industry. ⭐We offer NT$192,000 scholarship ⭐   3.      Admissions briefing details: l   Date: 2024/10/04 (Fri) 12:10-13:00 l   Venue: The Management Building, 11F, AI Lecture Hall l   Registration URL: https://forms.gle/fWBZw8txZXZ7pnBU7  (Online registration only, deadline: 12:00 PM, October 3rd) l   Join us Online: https://gqr.sh/mGaY ※Lunch provided for online registrants only. Organizers: College of Intelligent Computing & Institute of Health Science

The CGU "Medical AI: Opportunities and Challenges" forum brings together leading researchers at the forefront of integrating artificial intelligence into medical and health applications. As AI transforms the medical field, from diagnostic algorithms and treatment predictions to drug discovery and robotic surgeries, it also introduces complex challenges such as data privacy, ethical concerns, and the need for clinical validation.  This forum serves as a platform for deep discussions on cutting-edge advancements, and experience sharing on obstacles that must be overcome to ensure AI's safe, effective, and trustworthy implementation in real-world medical domains. *For more details, please visit our website: https://sites.google.com/view/2024-medicalai-cgu/home Forum Information: Date: 2024/12/09 (Mon) Time: 9:00am-17:00pm Venue: Shengen Lecture Hall  (Management Building 2F) Online registration only:  https://sites.google.com/view/2024-medicalai-cgu/registration Organizer : Chang Gung University, CGU Artificial Intelligence Research Center,  College of Intelligent Computing Advisory Organization: CGU Higher Education Sprout Project Office

Topic: Data-driven next generation medical research and healthcare system Speaker: Chin Lin, Associate Professor, School of Medicine, National Defense Medical Center Time: 2024/10/01(Tue) 14:10-16:00 Venue: CGU Artificial Intelligence Research Center (Management Building 11F) Join Online: https://gqr.sh/VD5p   About the Speaker: Dr. Lin's primary research focuses on the development of artificial intelligence models and their integration into clinical practice to validate improvements in healthcare quality. Dr. Lin's most renowned work is the AI-enabled ECG interpretation platform, capable of detecting over 50 diseases from a single ECG. This platform has initiated more than 10 clinical trials and has recently demonstrated the benefits of AI-ECG in reducing short-term mortality.   Abstract: Medical research is the foundation of health care systems. In recent decades, researchers have developed a framework that has significantly improved the quality and efficiency of healthcare. With advancements in deep learning, AI is poised to play a crucial role, but this also poses challenges to the traditional medical research paradigm. In response, the National Defense Medical College (NDMC) team is focusing on utilizing AI to re-analyze existing medical data, enabling opportunistic patient screening. This presentation will share the NDMC team’s experience in smart healthcare, highlighting how retrospective studies and clinical trials point to the future of a healthcare system shaped by human-machine collaboration.。   Organizers: College of Intelligent Computing & Artificial Intelligence Research Center   ※ No registration needed.

Topic: Trustworthy AI in a Smarter World: Addressing Awareness, Authenticity, and Security Challenges Speaker: Ming-Ching Chang, Associate Professor, Dept. of Computer Science, College of Engineering and Applied Sciences University at Albany, State University of New York Time: 2024/11/05 (Tue) 14:10-16:00 Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://gqr.sh/LrGY   About the Speaker: Ming-Ching Chang is an Associate Professor with tenure (since Fall 2022) in the Department of Computer Science at the University at Albany, SUNY. He previously held positions in the Department of Electrical and Computer Engineering (2016-2018) and as an Adjunct Professor in Computer Science (2012-2016). From 2008 to 2016, he worked as a Computer Scientist at GE Global Research Center and was an Assistant Researcher at the Mechanical Industry Research Labs, ITRI in Taiwan from 1996 to 1998.   Dr. Chang earned his Ph.D. in Engineering Man/Machine Systems from Brown University in 2008, along with an M.S. in Computer Science and Information Engineering (1998) and a B.S. in Civil Engineering (1996) from National Taiwan University. His research focuses on video analytics, computer vision, image processing, and artificial intelligence, with over 70 published papers. His projects have received funding from DARPA, IARPA, NIJ, VA, GE Global Research, Kitware Inc., and the University at Albany. Dr. Chang is a senior member of IEEE.   Abstract: Trustworthy AI research aims to create AI models that are efficient, robust, secure, fair, privacy-preserving, and accountable. As the adoption of Foundation Models and Generative AI grows, enabling the composition of articles and the generation of hyper-realistic images, the boundary between authenticity and deception is increasingly blurred in our rapidly evolving digital landscape. The demand for sophisticated tools and techniques to authenticate media content and discern the real from the fake has never been more urgent.   In this talk, I will explore recent breakthroughs in Trustworthy AI, Digital Media Forensics, and secure computation. First, I will introduce a novel approach to learning multi-manifold embeddings for Out-of-Distribution (OOD) detection, along with a method for uncovering hidden hallucination factors in large vision-language models through causal analysis. Additionally, I will cover a noisy-label learning technique designed to tackle long-tailed data distributions.   In the field of Digital Media Forensics, I will showcase novel advancements in Image Manipulation Detection (IMD) using implicit neural representations under limited supervision. This includes the development of IMD datasets featuring object-awareness and semantically significant annotations, leveraging stable diffusion to emulate real-world scenarios more effectively.   Finally, I will discuss key innovations in secure encrypted computation, particularly in accelerating Fully Homomorphic Encryption (FHE) for deep neural network inference using GPUs, as well as enhancing functional bootstrapping through quantization and network fine-tuning strategies.   Organizers: College of Intelligent Computing & Artificial Intelligence Research Center   ※ No registration needed.

Topic: From Word Embeddings to Large Language Models: Evolution and Prospects Speaker: Ying-Jia Lin Ph.D. in Computer Science and Information Engineering form National Cheng Kung University Time: 2024/10/15 (Tue) 14:10-16:00 Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://gqr.sh/NU8B About the Speaker:   Dr. Ying-Jia Lin is a postdoctoral researcher at National Tsing Hua University. He received his PhD from the Department of Computer Science and Information Engineering at National Cheng Kung University in 2024. Prior to that, he obtained his MS from the Institute of Biomedical Informatics at National Yang-Ming University in 2019 and his BS in Biomedical Sciences from Chang Gung University in 2017. His current research focuses on text summarization, model compression, and BioNLP. Ying-Jia Lin has published in top AI/NLP conferences, such as AAAI, EMNLP, and AACL. He is an honorary member of the Phi Tau Phi Society, and he won two Best Paper Awards at TAAI in 2022 and 2019.   Abstract: This presentation explores the evolution of Natural Language Processing (NLP) from the foundational concept of word embeddings to the emergence of large-scale language models like GPT. In the first part, we will journey through the history of NLP, highlighting key developments that have led to the current state of the field. The second part critically examines whether GPT has really solved the challenges of Natural Language Generation, using text summarization as a case study. We will discuss architectural issues inherent in GPT models, such as those related to the Key-Value (KV) cache, and examine knowledge limitations, particularly in the application of GPT to medical text reports. The role of Retrieval-Augmented Generation (RAG) in addressing these challenges will also be explored. This talk aims to provide insights into the advancements and remaining hurdles in NLP, offering perspectives on future directions and prospects. Organizers: College of Intelligent Computing & Artificial Intelligence Research Center   ※ No registration needed.

Key Dates: Application Date: 2024/10/9~2024/10/24 Interview Date: 2024/11/9 (Sat)  2024/10/9 9:00a.m.-2024/10/24 3:00p.m. Apply the payment account number for the registration fee 2024/10/9 9:00a.m.-2024/10/24 6:00p.m. Fill out the online registration form. Upload a JPG photo and academic documents in PDF format, then click “Submit Registration”. After submission, download and save the registration form. 2024/10/9 9:00a.m.-2024/10/25 3:30p.m. Pay the registration fee via ATM transfer, online banking, or over-the-counter remittance 2024/10/9 9:00a.m.-2024/10/27 6: 00p.m. Upload required documents For application details and required documents, please see CGU admission website:  https://www.cgu.edu.tw/recruit/Subject/Detail/55235?nodeId=238 Admission guideline:

Topic: Data-driven next generation medical research and healthcare system Speaker: Chin Lin, Associate Professor, School of Medicine, National Defense Medical Center Time: 2024/10/01(Tue) 14:10-16:00 Venue: CGU Artificial Intelligence Research Center (Management Building 11F) Join Online: https://gqr.sh/VD5p   About the Speaker: Dr. Lin's primary research focuses on the development of artificial intelligence models and their integration into clinical practice to validate improvements in healthcare quality. Dr. Lin's most renowned work is the AI-enabled ECG interpretation platform, capable of detecting over 50 diseases from a single ECG. This platform has initiated more than 10 clinical trials and has recently demonstrated the benefits of AI-ECG in reducing short-term mortality.   Abstract: Medical research is the foundation of health care systems. In recent decades, researchers have developed a framework that has significantly improved the quality and efficiency of healthcare. With advancements in deep learning, AI is poised to play a crucial role, but this also poses challenges to the traditional medical research paradigm. In response, the National Defense Medical College (NDMC) team is focusing on utilizing AI to re-analyze existing medical data, enabling opportunistic patient screening. This presentation will share the NDMC team’s experience in smart healthcare, highlighting how retrospective studies and clinical trials point to the future of a healthcare system shaped by human-machine collaboration.。   Organizers: College of Intelligent Computing & Artificial Intelligence Research Center   ※ No registration needed.