Topic: Data at the Negotiation Table: Harmonizing Hard Skills and Soft Skills Speaker: Dr. Robert Lo, Head of Access, Sustainability & External Engagement for Taiwan & Hong Kong at UCB Time: October 15, 2025 (Wednesday), 12:10 – 14:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall Registration: https://forms.office.com/r/afGDewBp3n or scan QR code on poster  About the Speaker:   Po-Ching Lo (Robert Lo) is the Head of Access, Sustainability & External Engagement for Taiwan & Hong Kong at UCB. With over fifteen years of experience, he is a seasoned professional in health technology assessment, pricing and reimbursement strategies, and health economics and outcomes research (HEOR). Robert's career spans pivotal roles in leading pharmaceutical companies, including Novartis and AbbVie, where he led market access strategies for new products in oncology, immunology, and rare diseases. Before joining the industry, he served as a Senior Researcher at the HTA division in Center for Drug Evaluation (CDE), Taiwan, where he conducted economic evaluations for more than 60 reimbursement dossiers. He holds a Ph.D. in Health and Welfare Policy from National Yang-Ming University. This unique combination of regulatory and industry experience equips him with a holistic perspective to develop innovative access solutions and foster win-win collaborations with stakeholders. Abstract: In Taiwan reimbursement journey of new drug, data is not only the foundation of decision-making but also the key language at the negotiation table. However, precise data alone is not enough to drive national reimbursement decisions. Cross-functional coordination and soft skills in communicating with government and stakeholders are essential to ensure that evidence is understood and accepted. This talk will share practical experiences from Taiwan’s pharmaceutical market access and product lifecycle, guiding the audience to explore how data analytics (hard skills) and negotiation communication (soft skills) perform in harmony in the AI era, accelerating the inclusion of innovative medicines in health insurance coverage and benefiting more patients. Organizers: Graduate Institute of Health Data Science ※ Registration needed.  

Topic: Artificial Intelligence: Deep Learning versus Green Learning Speaker: Dr. Jay Kuo, Distinguished Professor of Electrical and Computer Engineering and Computer Science, University of Southern California Time: October 21, 2025 (Tuesday), 2:10 – 3:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/EQlmra or scan QR code on poster  About the Speaker:   Dr. C.-C. Jay Kuo received his Ph.D. from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as the Ming Hsieh Chair Professor, a Distinguished Professor of Electrical and Computer Engineering and Computer Science, and the Director of the Media Communications Laboratory. His research interests are in visual computing and communication. He is an Academician of Academia Sinica and a Fellow of AAAS, ACM, IEEE, NAI, and SPIE. Dr. Kuo has received a few awards for his research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award. Dr. Kuo was the Editor-in-Chief of the IEEE Transactions on Information Forensics and Security (2012-2014) and the Journal of Visual Communication and Image Representation (1997-2011). He is currently the Editor-in-Chief for the APSIPA Trans. on Signal and Information Processing (2022-2023). He has guided 181 students to their Ph.D. degrees and supervised 31 postdoctoral research fellows. Abstract: The term “Artificial Intelligence (AI)” was coined in 1956. The field evolved slowly in the first 55 years. Yet, we have witnessed rapid advances in AI in the last decade. A recent successful example is the emergence of large language models. In this talk, I will shed light on two issues. First, I will explain the reasons for the advancement of AI in the last decade. Simply speaking, modern AI relies on numerous training samples that contain input/output pairs. An AI system provides a data-fitting solution to capture input and output mapping. Second, I will present two data–fitting methodologies: deep learning (DL) and green learning (GL). Although DL is dominant today, it is neither interpretable nor sustainable. Developing an alternative, interpretable, and sustainable AI methodology is challenging but essential. I have researched this problem since 2015. GL models offer energy-efficient AI solutions in cloud centers and mobile/edge devices. They have been successfully applied to various applications. I will use several medical imaging examples to highlight the differences between DL and GL solutions. Organizers: College of Intelligent Computing & Artificial Intelligence Research Center ※ No registration needed.  

The College of Intelligent Computing at Chang Gung University established the Graduate Institute of Health Data Science in August 2025 and is now launching its second round of admission by recommendation! About the Institute: The Graduate Institute of Health Data Science is dedicated to training master’s-level professionals who can flexibly apply artificial intelligence in combination with statistical methods to process diverse health data, conduct cross-disciplinary analyses, and drive innovative applications. This briefing session will introduce the Institute’s curriculum highlights, integrated resources, and advantages of collaborating with the Chang Gung Medical System, as well as provide information on admission by recommendation. Briefing Session Details Date & Time: October 3, 2025 (Friday), 12:10 – 13:00 Venue: AI Lecture Hall, 11th Floor, Management Building / Online Live Stream Language: The session will be conducted in Mandarin Chinese. How to Participate: • For on-site participation, please register at https://forms.office.com/r/Fd7LC6bQKy • For the live stream, join via https://reurl.cc/7Vv869 Application Period for Admission by Recommendation: October 8 – October 31, 2025 Institute Website: https://www.cgu.edu.tw/hds Organizers: College of Intelligent Computing & Graduate Institute of Health Data Science

Topic: A Unified Framework to Calculate Every Deep Learning Architecture by Hand Speaker: Dr. Tom Yeh, Assistant Professor, Department of Computer Science, University of Colorado Boulder Time: October 14, 2025 (Tuesday), 2:00 – 4:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/K9n8dM or scan QR code on poster  About the Speaker:   Prof. Tom Yeh leads the Imagine AI Lab at the University of Colorado Boulder. His research and teaching spans AI, HCI, Education, Ethics, and Neuroscience. He published more than 150 papers. He is the author of the popular AI by Hand ✍️ series, with by more than 160K followers across LinkedIn, X, and Substack. At his university, Prof. Tom Yeh received several awards, including the Student Affair Faculty of the Year Award. Abstract: I will present AI by Hand, a new framework I developed to demonstrate the core calculations of a wide range of deep learning architectures by hand, including basic building blocks such as artificial neuros and multi-layer perceptron, well-known architectures such as the transformer,, and state-of-art architectures such as the Diffusion Transformer and the Switch Transformer. Organizers: College of Intelligent Computing & Artificial Intelligence Research Center ※ No registration needed.

Topic：From Data to Action: Leveraging AI, Big Data, and Modeling for Precision Health and Epidemic Prevention Speaker:  Hsiao-Hui Sophie Tsou from Institute of Population Health Sciences 、 National Health Research Institutes Time: 2025/10/01 (Wed)  12:10-14:00 Venue: The Management Building, 11F, AI Lecture Hall Registration link: https://forms.office.com/pages/responsepage.aspx?id=qjVW2kse_ESSFFlrQmXkU3LqnmbpYQBPqN7Y8UREohFURFRSQUFJWUQwQTVOSjUyRERCWUdHSkRQUi4u&route=shorturl or you can scan the link from the poster Abstract: In healthcare research and development, the integration of modeling, big data, and artificial intelligence (AI) with advanced scientific tools has become a driving force for innovation. These approaches not only enhance our understanding of complex diseases but also improve the evaluation of medical and public health interventions. This presentation will highlight the expanding role of big data in infectious disease epidemiology, with a particular focus on applications in disease surveillance, predictive modeling, and the assessment of intervention effectiveness. We will also share a comparative analysis of COVID-19 containment strategies across 50 regions, providing insights into policy impact and response effectiveness. In addition, the potential of AI for advancing precision health will be demonstrated, showcasing how personalized approaches can transform healthcare delivery. Finally, we will discuss future challenges and opportunities for the continued development and implementation on infectious disease.