長庚大學

演講主題：台北智慧城市建設案例分析 講者:  李達生博士 國立臺北科技大學 能源與冷凍空調工程系 終身特聘教授 時間: 2025/11/11 (二)  14:00-16:00 地點: 管理大樓11樓-AI講堂 直播連結: https://reurl.cc/K9VY6n 或 掃描海報 QR code 講者簡介: 李達生博士為臺北科技大學終身特聘教授，兼任多個研究與產業顧問職務，致力整合產學研推動節能減碳技術。實務上，帶領團隊協助台灣19個產業、百餘工廠改善空壓機、冰水主機、冷卻水塔與鍋爐，並導入AI深度節能。研究方面，2015~2024年專注AI應用於節能減碳，在SCI發表28篇相關論文，總發表47篇，廣受引用，並獲2024年Stanford「全球前2%頂尖科學家」肯定。 演講大綱: 本演講將探討自2016年起，臺北市TPMO推動315件PoC智慧城市專案，助力城市智慧建設與永續發展。2024年，臺北在Smart City Index與Cities in Motion Index中分別位列16與27名，碳排放自12,409,700降至10,931,500噸，減碳11.9%。研究發現，大企業與中小企業協力形成economy co-evolution loop，是提升智慧程度與永續發展的關鍵。基於five-stage整理法，提出six-stage AI Driven Smart City Framework，結合AI research loop與PoC duplicate loop，可量化導入AI，建構智慧永續城市。 主辦單位：智慧運算學院、人工智慧研究中心  ※本活動無需報名

演講主題：Agentic AI in Action: Integrating Autonomous Systems at NCHU   講者:  范耀中教授 國立中興大學資訊工程學系   時間: 2025/10/28 (二)  14:00-16:00   地點: 管理大樓11樓-AI講堂   直播連結: https://reurl.cc/daEED6 或 掃描海報 QR code   講者簡介:   范耀中是國立中興大學（NCHU）資訊工程學系教授兼圖書館副館長。他的研究專注於自主型人工智慧（Agentic AI）、大型語言模型（LLM）以及特定領域知識系統。他主持多項跨領域計畫，將人工智慧技術整合進中興大學的教學與圖書館平台，並應用於智慧農業及法律資訊檢索等領域。       演講大綱:   本次演講將介紹國立中興大學在自主型人工智慧（Agentic AI）方面的最新進展，重點在於將自主 AI 代理整合至大學的教學與圖書館系統。我們也將分享在智慧農業領域的應用，包括結合生成模型與知識檢索的「神農 TAIDE 系統」。最後，我們將探討開源模型如何達到與專有模型相當的效能，展示在學術界實現開放 AI 生態系統的可行性。   主辦單位：智慧運算學院、人工智慧研究中心    ※本活動無需報名

演講主題：從資料到實證：OHDSI全球網絡如何改變健康數據研究的格局   講者: 徐之昇教授 現任臺北醫學大學管理學院國際生技醫療管理博士學位學程教授   時間: 2025/10/29 (三)  12:10-14:00   地點: 管理大樓11樓-AI講堂   報名連結: https://forms.office.com/r/EVADcz6zp8或掃描海報QRcode   (教職員可獲得2小時活動時數認證，請上活動報名系統報名參加)   講者簡介:   徐之昇教授專長於數位健康、真實世界數據(RWD)與真實世界證據(RWE)之研究與應用，現任臺北醫學大學管理學院國際生技醫療管理博士學位學程教授。同時兼任多項與健康數據研究相關的職務，包括臺灣觀察性健康資料研究學會(OHDSI Taiwan Society)理事長、臺北醫學大學臨床數據中心主任、管理學院健康產業數據加值研究中心主任，以及北醫附設醫院臨床大數據研究中心主任。徐教授曾於美國哈佛醫學院接受進修與研究訓練，長期推動跨領域與跨國合作，主持多項大型RWD/RWE研究計畫。他的研究使命是透過全球資料合作與證據生成，推動數位醫療、精準治療與生技醫療管理的發展，致力於打造可重現、可延伸的智慧健康研究生態系。演講大綱:   新藥在台灣健保給付的旅程中，數據不僅是決策的依據，更是談判桌上的關鍵語言。然而，僅有精準的數據仍不足以推動健保給付決策，跨部門協調及如何和政府、利害關係人溝通的軟實力，才是讓證據被理解與採納的關鍵。本演講將分享台灣新藥市場准入及產品生命週期的實務經驗，帶領聽眾探索數據分析（hard skills）與溝通談判（soft skills）如何在AI時代協奏，共同加速創新藥物進入健保給付，造福更多病人。   主辦單位：健康數據科學研究所    ※本活動需報名

演講主題：Artificial Intelligence: Deep Learning versus Green Learning 講者: Dr. Jay Kuo美國南加州大學電機與計算機工程及計算機科學傑出教授  時間: 2025/10/21 (二)  14:10-15:00 地點: 管理大樓11樓-AI講堂 直播連結: https://reurl.cc/EQlmra 或 掃描海報 QR code 講者簡介: 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. 演講大綱: 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. 主辦單位：智慧運算學院、人工智慧研究中心  ※本活動無需報名

演講主題：A Unified Framework to Calculate Every Deep Learning Architecture by Hand 講者: Dr. Tom Yeh美國科羅拉多大學波德分校電腦科學系助理教授  時間: 2025/10/14 (二)  14:00-16:00 地點: 管理大樓11樓-AI講堂 直播連結: https://reurl.cc/K9n8dM 或 掃描海報 QR code 講者簡介: 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. 演講大綱: 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. 主辦單位：智慧運算學院、人工智慧研究中心  ※本活動無需報名