Artificial Intelligence Research Center

*Full-Time Research Fellow Invitation* Assistant/ Associate/ Full Research Fellows in Artificial Intelligence Research Center, Chang Gung University Artificial Intelligence Research Center (AIC) at Chang Gung University (CGU) currently invites applications for full-time Assistant, Associate, and Full Research Fellows. There are 2 positions to be filled. Research Fellow Positions: We are looking for candidates with research areas in machine learning, computer vision, natural language processing, smart medicine, smart manufacturing, robotics, metaverse and human-computer interaction. Candidates should have a Ph.D. degree in Artificial Intelligence, Computer Science, Data Science, Engineering, Biomedical or related areas. Senior candidates must demonstrate significant research achievement, and have an international reputation such as strong publication records, patents, industrial experience, or other academic achievements. CGU AIC is a research-oriented institute and teaching duties are not required. Application Deadline: (1)   There is no application deadline. However, applications received by Jun 15, 2026 will receive the fullest consideration. (2)   Application materials will be reviewed on a first-come, first-served basis. (3)   Interview date/time will be set up promptly if an application has received a favorable opinion by the committee. Online interviews can be arranged. Starting Date:  August 1, 2026 or February 1, 2027  CGU Artificial Intelligence Research Center offers an internationally competitive compensation package commensurate with the candidate’s professional experience. Successful candidates will be appointed at the level of Assistant, Associate or Full Research Fellow according to qualifications. Application Requirements: (1)   Cover letter (one-page). (2)   CV with higher academic degree details (undergraduate to Ph.D.), professional experiences, research statement, publication list, and a list of three professional references. (3)   Chang Gung University is an equal-opportunity employer following the international standard for diversity and inclusion. Applicants from all regions and nationalities are welcome. (4)   Please submit your application in PDF files or in a cloud storage link to Ms. Sue Liu (sueliu@cgu.edu.tw). About AIC @ CGU Artificial Intelligence Research Center (AIC), established with the support of the Formosa Plastics Group (one of the largest private enterprises in Taiwan), is a research center in the Chang Gung University (CGU). In addition to the Formosa Plastics Group companies, AIC works closely with the Chang Gung Medical Foundation (the largest healthcare system in Taiwan) on advanced medical research projects, as well as other research centers in Chang Gung University. AIC also has strong connections with many world-leading high-tech companies in Taiwan. AIC researchers work closely with the faculty in the College of Intelligent Computing (CoIC), many with joint appointments from both units. CoIC is the first academic college in Taiwan devoted to the research and education of the AI technology and application. CoIC has many highly-reputed research faculty with a strong international and/or industrial working experience. AIC researchers and CoIC professors conduct joint research projects using AIC’s server hardware, IoT equipment and other research facilities. The center/college joint research eco-system further provides the support for CGU university-wide and international AI research activities. Chang Gung University (CGU), a student centric and research-directed university with entrepreneurial spirits, was established by the founding Wang family of the Formosa Plastics Group. CGU is highly ranked globally in the QS World Universities Rankings 2023, and in the top 5 among the 151 universities in Taiwan. CGU has 4 colleges: College of Medicine, College of Engineering, College of Management, and College of Intelligent Computing. CGU offers a student-faculty ratio of 12:1, with ~7,000 students, ~600 full-time faculty and ~665 adjunct faculty members. The University strives for excellence in research, teaching, and innovative technology commercialization. Under strong support from the Formosa Plastics Group and the Chang Gung Medical Foundation (the largest private-owned healthcare system in Taiwan), CGU provides advanced yet practical training on industrial technology and medical sciences. CGU is located in the suburban area of the Taipei city, about 30 minutes to the city center by Metro (Mass Rapid Transit). For details about Chang Gung University, please visit  https://www.cgu.edu.tw/en Learn more about CGU: https://www.cgu.edu.tw/en AIC: https://www.cgu.edu.tw/aic-en

Topic: Hiding a Swarm's Leader from RL Agent and Human AI Safety in the Information and Physical Spaces Speaker:  Prof. Michael Lewis, School of Computing and Information, Department of Informatics and Networked Systems, University of Pittsburgh Prof. Katia Sycara, Edward Fredkin Research Professor of Robotics, School of Computer Science, Carnegie Mellon University Time : May12, 2026 (Tuesday), 3:00 – 5:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/xW6OmZ or scan QR code on poster  About the Speaker: 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 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. Abstract: Hiding a Swarm's Leader from RL Agent and Human 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 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. Organizers: College of Intelligent Computing & Artificial Intelligence Research Center ※ No registration needed.

Topic: The Digital Shift: Transforming Clinical Trials with Big Data and AI Speaker: PhD degree in Biomedical Informatics from Stanford University Time : May 05, 2026 (Tuesday), 2:00 – 4:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/WbvX79 or scan QR code on poster  About the Speaker: 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. Abstract: 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. Organizers: College of Intelligent Computing & Artificial Intelligence Research Center ※ No registration needed.

Topic: AI-based Image Analysis for Medical Problems: Challenges and New Approaches   Speaker: Prof. Danny Z. Chen Department of Computer Science and Engineering University of Notre Dame Time : April 22, 2026 (Wednesday), 12:10-14:00 Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/epD4WW or scan QR code on poster Registration link: https://forms.gle/XiK5ecK3LCjuzydJ6 or scan QR code on poster   About the Speaker:  Dr. Danny Z. Chen received the B.S. degrees in Computer Science and in Mathematics from the University of San Francisco, California in 1985, and the M.S. and Ph.D. degrees in Computer Science from Purdue University, West Lafayette in 1988 and 1992, respectively. He is a Professor in the Department of Computer Science and Engineering at the University of Notre Dame. Dr. Chen’s main research interests include computational biomedicine, biomedical imaging, machine learning, data mining, computational geometry, algorithms, and VLSI.  He has worked extensively with biomedical researchers and practitioners, published many papers in these areas, and holds 8 US patents for technology development in biomedical applications. He received the US NSF CAREER Award in 1996 and the 2017 PNAS Cozzarelli Prize of the US National Academy of Sciences. He is a Fellow of IEEE and AAAS,  and is a Distinguished Scientist of ACM.   Abstract:   New technologies for acquiring massive amounts of medical image data give rise to an ever-increasing demand for effective approaches for medical image analysis tasks. In recent years, deep learning (DL) approaches have yielded remarkably high-quality solutions for numerous medical imaging applications, largely outperforming traditional image analysis methods. Comparing to natural scene images, medical image analysis faces several different challenges. Commonly, DL methods rely on a large amount of labeled data for model training. While natural scene images are usually 2D, medical images can be 2D, 3D, and even higher dimensional. In particular, 3D medical images are widely used in basic research and clinical practice. However, 3D medical image analysis presents big challenges to DL methods. First, 3D medical images can be of very large sizes (e.g., billions of voxels), and thus incur high computational costs. But, current GPUs are of limited memory for implementing 3D DL models. Furthermore, few efficient automatic techniques for labeling 3D images are available. Since in general, only trained medical experts can label medical images effectively, medical image annotation is a highly costly and labor-intensive process (even for 2D images).  Therefore, how to attain sufficient good quality labeled image data for DL model training while significantly reducing annotation efforts of medical experts is a big bottleneck to the successful development and deployment of DL methods for medical imaging applications.   We present new DL-based approaches for medical image analysis tasks (segmentation, classification, denoising, etc). We show that it is often not enough to simply apply DL methods alone to tackle medical image analysis problems. Thus, our approaches are based on combinations of DL methods and algorithmic techniques (e.g., topological data analysis). For example, our sparse annotation schemes judiciously select the most representative or valuable samples to label. Actually, the problem of finding an optimal subset of samples (as sparse labeled data) to cover or represent an entire image dataset is an NP-hard problem, which can be solved approximately with guaranteed good quality. Our approaches achieve high performances with efficient costs. We present experimental results on various datasets to demonstrate the applicability of our approaches on medical image analysis problems.     Organizers: Institute of Health Data Science  ※ 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.