演講主題： Hiding a Swarm's Leader from RL Agent and Human AI Safety in the Information and Physical Spaces 講者:  Prof. Michael Lewis 美國匹茲堡大學（University of Pittsburgh）資訊學院教授 School of Computing and Information, Department of Informatics and Networked Systems, University of Pittsburgh Prof. Katia Sycara 美國卡內基美隆大學（CMU）機器人研究所資深研究教授，現任機器人研究所副主任，並擔任可信任人機協作研究中心主任 Edward Fredkin Research Professor of Robotics, School of Computer Science, Carnegie Mellon University 時間: 2026年5月12日 (二)  15:00-17:00 地點: 管理大樓11樓-AI講堂 直播連結: https://reurl.cc/xW6OmZ或 掃描海報 QR code 講者簡介: Michael Lewis 教授為美國匹茲堡大學（University of Pittsburgh）資訊學院教授，專長於人機互動、人機協作與群體機器人（swarm robotics）研究，特別關注人類如何與多機器人系統及AI協同運作。其研究涵蓋人因工程、強化學習、人機信任與決策支援等領域，長期獲美國國防高等研究計畫署（DARPA）與多個政府機構支持，為人機協作與智慧系統領域的重要學者。 Michael Lewis is a Professor at the School of Computing and Information at the University of Pittsburgh. Trained in engineering psychology, his research focuses on human-computer interaction, human-agent teaming, and swarm robotics. He investigates how humans interact with complex autonomous systems, with particular emphasis on trust, decision-making, and coordination in multi-agent environments. His work integrates artificial intelligence, visualization, and human factors to improve the effectiveness of human–AI collaboration. Professor Lewis has led and contributed to numerous research projects supported by agencies such as DARPA, NSF, and other U.S. government organizations. He has published extensively in leading journals and conferences in human-machine systems, robotics, and AI. Katia Sycara 教授為美國卡內基美隆大學（CMU）機器人研究所資深研究教授，現任機器人研究所副主任，並擔任可信任人機協作研究中心主任。她為人工智慧、多代理系統與人機協作領域的國際權威學者，為 AAAI 與 IEEE Fellow，並曾獲多項國際學術終身成就獎。其學術影響力深遠，發表超過700篇論文，引用數逾5萬次，致力於發展可信任、自主且可解釋的AI系統。 Katia Sycara is the Edward Fredkin Research Professor in the School of Computer Science at Carnegie Mellon University and a Research Professor at the Robotics Institute. She is a leading expert in artificial intelligence, multi-agent systems, and human-agent collaboration. Her research spans AI autonomy, distributed intelligent systems, and trust in human-AI interaction, with applications in robotics, defense, and large-scale information systems. She has been a pioneer in agent-based systems and has made significant contributions to semantic web technologies and collaborative AI. Professor Sycara has received numerous honors, including recognition as a Fellow of the Association for the Advancement of Artificial Intelligence and the Institute of Electrical and Electronics Engineers. She has also served in key advisory roles for government and international research initiatives, and her work has had a lasting impact on the development of intelligent, cooperative systems. 演講大綱: Hiding a Swarm's Leader from RL Agent and Human 本演講探討在機器人群體系統中保護領導者的策略。雖然以領導者為核心的控制方式可提升群體協作效率，但同時也增加系統遭受攻擊的風險。本研究透過圖神經網路（GNN）訓練群體追隨領導者，並利用對抗式模型辨識領導者位置。研究結果顯示，在一般情境下，AI模型在領導者辨識上優於人類；然而當群體採用隱匿策略時，人類的辨識能力反而優於AI。即使在對抗模型持續學習及高視覺干擾環境下，人類仍展現出穩定且較佳的判斷能力。此結果突顯人類與人工智慧在複雜多代理系統中的關鍵差異。 This talk explores methods for protecting leadership within robotic swarms, where leader-based control improves coordination but introduces vulnerability. Using graph neural networks (GNNs), swarms can be trained to follow a leader, while adversarial models attempt to identify it. Results show that although AI models outperform humans in identifying leaders under normal conditions, humans become more effective when swarms adopt deception strategies to hide leaders. Even with adaptive adversaries and increased visual complexity, human observers demonstrate robust performance. These findings highlight key differences between human perception and AI in complex multi-agent environments. AI Safety in the Information and Physical Spaces 本演講探討基礎多模態模型（Foundational Multi-Modal Models）的潛在安全漏洞，重點分析透過「意圖欺騙（intention deception）」進行模型越獄（jailbreaking）的風險，以及在何種條件下多模態模型更容易受到對抗性攻擊並洩露敏感或危險資訊。此外，將提出相應的安全防護策略，並進一步介紹「情境安全（contextual safety）」在真實世界應用中所面臨的挑戰。 In this talk, we will present our work on vulnerabilities of Foundational Multi-Modal Models. In particular, we will present jailbreaking of Frontier Models via intention deception and conditions that make multi modal models more vulnerable to adversarial attacks in disclosing dangerous information. We will also propose safety mitigations. Additionally, we will introduce contextual safety and its challenges in the physical world. ※ No registration needed.

演講主題：數位轉型：大數據與人工智慧引領的臨床試驗變革 The Digital Shift: Transforming Clinical Trials with Big Data and AI 講者:  林士睿博士 美國Stanford 大學生物醫學資訊博士 PhD degree in Biomedical Informatics from Stanford University 時間: 2026年5月5日 (二)  14:00-16:00 地點: 管理大樓11樓-AI講堂 直播連結: https://reurl.cc/WbvX79或 掃描海報 QR code 講者簡介: 林士睿博士畢業於台大資訊工程學系/研究所取得學士和碩士學位，並在美國Stanford 大學完成統計碩士及生物醫學資訊博士學位。林博士對將人工智慧和資訊科技運用在生物醫學特別有熱忱。在Stanford時，他致力於透過電腦模型虛擬病人，運用模擬臨床試驗 ，以評估乳癌及肺癌篩檢的成效。之後林博士在羅氏藥廠 (Roche / Genentech) 從事癌症治療新藥的研發工作已有十五年，同時致力參與並領導跨領域的臨床醫學的研究合作。 Dr. Ray Lin got his BS and MS degrees in Computer Science and Information Engineering from National Taiwan University, specializing in AI in his Master’s Thesis. He later obtained a MS degree in Statistics and a PhD degree in Biomedical Informatics from Stanford University. His PhD research focused on synthetic clinical trials using computer-simulated virtual patients to evaluate cancer screening for lung cancer and breast cancer. Dr. Lin has accumulated 15 years of experience in drug development with Genentech (Roche) supporting clinical trials for developing new cancer treatments, including therapies approved in multiple cancer types globally. He has also been actively engaging in cross-industry collaborations and professional organizations, including multiple leadership positions in an American Statistical Association (ASA) scientific working stream, a US FDA initiated cross-pharma initiative in oncology, and the ASA San Francisco Bay Area local chapter. 演講大綱: 隨機對照試驗（RCTs）長期以來一直是臨床研究的最具可信度的研究方法；然而，這類試驗也面臨顯著挑戰，包括高昂成本、漫長的時程，以及倫理或可行性上的限制。大數據與人工智慧（AI）時代的到來，為解決這些難題提供了契機。透過整合試驗外的真實世界數據（Real-world data）——如電子病歷、保險資料庫及疾病登錄系統——可補足隨機對照試驗的不足，有效降低研究成本、提高受試者招募的可行性並縮短時程，對於急需醫療研究的罕見疾病尤為重要。同時，人工智慧驅動的模型能預測病患預後，並在傳統隨機對照試驗不可行性或違背倫理的情況下，透過模擬「虛擬病患」進行合成臨床試驗（Synthetic clinical trials）以評估干預措施。 本演講將分享兩個案例研究：(1) 提出一種創新的「動態借用」（Dynamic borrowing）方法，整合外部數據以提升研究效率，同時極小化選擇偏差並維持試驗的可信度；(2) 運用虛擬病患與合成臨床試驗評估癌症篩檢計畫的成效。相關研究結果已納入美國國家癌症研究所（NCI）的癌症干預及監測模型網路（CISNET），並實質影響了美國與台灣的乳癌篩檢建議指引。 Randomized controlled trials (RCTs) remain the gold standard for clinical research; however, they face significant hurdles, including high costs, lengthy timelines, and ethical or feasibility constraints. The era of Big Data and AI presents transformative opportunities to address these challenges. Real-world data (RWD)—such as electronic medical records, insurance databases, and disease registries—can supplement RCTs to reduce costs, enhance enrollment, and accelerate timelines, particularly for rare diseases with high unmet medical needs. Simultaneously, AI-driven models can predict patient outcomes, enabling the simulation of "virtual patients" in synthetic clinical trials when traditional randomization is impractical or unethical. This presentation features two case studies: (1) A novel dynamic borrowing method that integrates RCTs with external data to increase study efficiency while minimizing selection bias and maintaining trial integrity; (2) The use of virtual patients and synthetic trials to evaluate cancer screening programs. These findings were incorporated into the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network consortium (CISNET) consortium and have directly influenced breast cancer screening recommendations in the U.S. and Taiwan. ※ No registration needed.

Topic: Transforming Assistive Oral Communication Technologies through Artificial Intelligence Speaker: Prof. Yu Tsao, Research Fellow (Professor) and the Deputy Director at the Research Center for Information Technology Innovation, Academia Sinica Time : April 21, 2026 (Tuesday), 2:00 – 4:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/grQlk4 or scan QR code on poster  About the Speaker:    Yu Tsao (Senior Member, IEEE) received the B.S. and M.S. degrees in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in 1999 and 2001, respectively, and the Ph.D. degree in Electrical and Computer Engineering from the Georgia Institute of Technology, At-lanta, GA, USA, in 2008. From 2009 to 2011, he was a Researcher at the National Institute of Information and Communications Technology (NICT), Tokyo, Japan, where he conducted re-search and product development in multilingual speech-to-speech translation systems, focusing on automatic speech recognition. He is currently a Research Fellow (Professor) and the Deputy Director at the Research Center for Information Technology Innovation, Academia Sinica, Tai-pei, Taiwan. He also holds a joint appointment as a Professor in the Department of Electrical Engineering at Chung Yuan Christian University, Taoyuan, Taiwan. His research interests in-clude assistive oral communication technologies, audio coding, and bio-signal processing. He serves as an Associate Editor for IEEE Transactions on Consumer Electronics and IEEE Signal Processing Letters. He received the Outstanding Research Award from Taiwan’s National Sci-ence and Technology Council (NSTC), the 2025 IEEE Chester W. Sall Memorial Award, and served as the corresponding author of a paper that won the 2021 IEEE Signal Processing Society Young Author Best Paper Award. Abstract: This presentation provides an overview of AI-driven assistive oral communication technologies, encompassing both assistive speaking and assistive hearing domains. The first part focuses on assistive speaking technologies, highlighting intelligent diagnostic and enhancement frame-works for speech disorders. It introduces machine learning approaches for pathological speech classification, severity assessment, and targeted enhancement for conditions such as dysarthria, post-surgical speech impairment, and electrolaryngeal speech. The second part addresses assis-tive hearing, presenting recent advances in AI-based diagnostic and signal processing tech-niques for hearing disorders. Representative applications include automated detection of otitis media with effusion, as well as AI-driven speech generation and objective quality assessment methods for hearing aids and cochlear implants. By integrating speech enhancement, assess-ment, and generation within a unified AI framework, this presentation demonstrates the poten-tial of neural-based technologies to enhance communication effectiveness and accessibility, while underscoring the importance of interdisciplinary research in advancing next-generation, human-centered assistive systems. Organizers: College of Intelligent Computing & Artificial Intelligence Research Center ※ No registration needed.

【Recruitment】Chang Gung University College of Intelligent Computing is recruiting one full-time International Academic Officer 1. Job Description: 1)      Receive and host international visitors, short-term visiting scholars, and recruit international faculty for the College. 2)      Promote College-wide international exchange programs and establishment of partners agreements. 3)      Coordinate international student affairs and short-term overseas study and exchange programs. 4)      Coordinate projects between the College and enterprises, both domestic and international. 5)      Create bilingual communication and promotional materials, including posters, websites, and social media, etc. 6)      Admissions, student affairs, and general administration for the Institute of Health Data Science. 7)      Other ad hoc tasks assigned by supervisors. 2. Salary and Benefits: 1)      Per university regulations, the monthly salary for Bachelor degree holder is NT$43,440, and NT$44,970 for Master’s degree holder. 2)      Eligible candidates with relevant work experience at public or private universities may apply for salary promotion. 3)      Those with B2 language proficiency (TOEIC 750 or equivalent) can apply for a language allowance of NT$1,500 or more per month (depending on the level); those with C1 language proficiency (TOEIC 880 or equivalent) can apply for a language allowance of NT$4,000 or more per month (depending on the level). 4)      First-year contract reserve employees receive labor/health insurance, holiday, and year-end bonuses. Upon passing the first-year evaluation, employees transition to full-time status with additional public insurance benefits. 3. Education Requirements: University degree or higher, no specific field required. 4. Application Requirements: 1)      Ability to handle administrative tasks, draft official documents, communicate effectively, with a positive and enthusiastic work attitude, detail-oriented, responsible, and the ability to work independently. 2)      Fluent in English (listening, speaking, reading, writing). Applicants should provide proof of English proficiency equivalent to TOEIC 750 or higher within the past three years (other types of English proficiency tests are also acceptable). 3)      Preferred qualifications:              i.                Experience in international affairs is highly desirable.            ii.                Experience in university administration.          iii.                Experience studying abroad.          iv.                Proficiency in languages other than English. 4)      Computer skills: Proficient in Office software and internet operations. 5. Application Method: 1)      Interested applicants should prepare the following documents:              i.                Resume and bio            ii.                Graduation certificate          iii.                Transcript of highest degree          iv.                Proof of English proficiency            v.                Consent letter from current supervisor* (Attachment 1)          vi.                Work report and achievements* (Attachment 2)        vii.                Personal information collection and consent form (Attachment 3)      viii.                Other certification or proof of relevant experience, etc.          ix.                Applicants are required to deliver a 10-15 minute presentation in English during the interview. *Attachments 1 and 2 are required for CGU staff or contract employees with at least one year of service. 6.Application Process: Please apply via email; other methods will not be reviewed or responded to. Email Submission: Combine all application documents in the order listed above into a single PDF file and email it to tanya.lin@cgu.edu.tw. Please use the subject line: 【Application for School of Intelligent Computing [International Academic Specialist] - (Your Name)】. Deadline: Applications are accepted starting immediately and will be reviewed on a rolling basis. Recruitment will close once a suitable candidate is found. Review Process: Candidates who pass the initial review will be notified of the written test and interview time. Those not selected will not be notified, and application materials will not be returned. Any falsification of personal data will result in disqualification or dismissal. Employment Terms: For accepted applicants who are not current full-time staff applying for a transfer, the initial hiring will be as a contract reserve staff member. After passing a one-year evaluation and training period, the status will be converted to official full-time staff. If the evaluation is not passed, the contract will be terminated. 7. Contact Information: College of Intelligent Computing Secretary, Ms. Lin, Tel: 03-211-8800 ext. 2502; Personnel Office Ms. Mai, Tel: 03-211-8800 ext. 3471.

Topic: Genomics, Big Data, and AI: New Approaches for Understanding Complex Diseases Speaker: Prof. AiRu Hsieh — Associate Professor, Department of Statistics, National Taipei University Time: March 25, 2026 (Tuesday), 12:10 – 14:00 Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/lpdb2q or scan QR code on poster Registration: https://forms.office.com/r/iUTLaer75x or scan QR code on poster  About the Speaker:   Professor Ai-Ru Hsieh is a researcher in statistical genetics and biomedical data science. Her work focuses on integrating genome-wide association studies (GWAS), polygenic risk scores (PRS), Mendelian randomization (MR), and machine learning approaches to investigate the genetic architecture and causal mechanisms of complex diseases. By leveraging large-scale biobank resources and high-dimensional biomedical data, her research aims to develop interpretable models for disease risk prediction and biological discovery. Her work contributes to advancing precision medicine and improving our understanding of disease mechanisms at both clinical and population health levels. Abstract: Recent discoveries in human genetics are driving precision medicine forward. Large-scale genome-wide association studies (GWAS) have mapped thousands of variants related to complex diseases, offering insight into their biology. Polygenic risk scores (PRS) pull together these small genetic signals to flag people who might warrant earlier screening or closer monitoring. Mendelian Randomization (MR) provides a statistical framework for evaluating potential causal relationships between exposures and disease outcomes, offering support for targeted interventions. In addition, machine learning approaches provide powerful tools for feature selection and predictive modeling in high-dimensional biomedical datasets. Techniques such as LASSO, random forests, and gradient boosting models can identify informative genetic variants, biomarkers, and clinical variables from large-scale datasets. These approaches improve disease risk prediction, refine polygenic risk models, and help uncover hidden patterns in MR analysis. By integrating statistical genetics methods, machine learning algorithms, and large-scale biomedical data resources, we can build interpretable models for disease prediction and biological discovery. The findings from these approaches make us more understanding of disease mechanisms and guide more effective prevention and treatment efforts in both clinical and population health contexts. Organizers: Institute of Health Data Science ※ Registration needed.