Topic: Trustworthy AI in a Smarter World: Addressing Awareness, Authenticity, andSecurity Challenges
Speaker: Ming-ChingChang, Associate Professor, Dept. of Computer Science,
College of Engineering and Applied SciencesUniversity at Albany, State University of New York
Time: 2024/11/05(Tue) 14:10-16:00
Venue: TheManagement Building, 11F, AI Lecture Hall
Join Online: https://gqr.sh/LrGY
About the Speaker:
Ming-Ching Chang is an Associate Professorwith tenure (since Fall 2022) in the Department of Computer Science at theUniversity at Albany, SUNY. He previously held positions in the Department ofElectrical and Computer Engineering (2016-2018) and as an Adjunct Professor inComputer Science (2012-2016). From 2008 to 2016, he worked as a ComputerScientist at GE Global Research Center and was an Assistant Researcher at theMechanical Industry Research Labs, ITRI in Taiwan from 1996 to 1998.
Dr. Chang earned his Ph.D. in EngineeringMan/Machine Systems from Brown University in 2008, along with an M.S. inComputer Science and Information Engineering (1998) and a B.S. in CivilEngineering (1996) from National Taiwan University. His research focuses onvideo 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 modelsthat are efficient, robust, secure, fair, privacy-preserving, and accountable.As the adoption of Foundation Models and Generative AI grows, enabling thecomposition of articles and the generation of hyper-realistic images, theboundary between authenticity and deception is increasingly blurred in ourrapidly evolving digital landscape. The demand for sophisticated tools andtechniques to authenticate media content and discern the real from the fake hasnever been more urgent.
In this talk, I will explore recentbreakthroughs in Trustworthy AI, Digital Media Forensics, and securecomputation. First, I will introduce a novel approach to learningmulti-manifold embeddings for Out-of-Distribution (OOD) detection, along with amethod for uncovering hidden hallucination factors in large vision-languagemodels through causal analysis. Additionally, I will cover a noisy-labellearning technique designed to tackle long-tailed data distributions.
In the field of Digital Media Forensics, Iwill showcase novel advancements in Image Manipulation Detection (IMD) usingimplicit neural representations under limited supervision. This includes thedevelopment of IMD datasets featuring object-awareness and semanticallysignificant annotations, leveraging stable diffusion to emulate real-worldscenarios more effectively.
Finally, I will discuss key innovations insecure encrypted computation, particularly in accelerating Fully HomomorphicEncryption (FHE) for deep neural network inference using GPUs, as well asenhancing functional bootstrapping through quantization and network fine-tuningstrategies.
Organizers: College of Intelligent Computing& Artificial Intelligence Research Center
Topic: FromWord Embeddings to Large Language Models: Evolution and Prospects
Speaker: Ying-JiaLin Ph.D. in Computer Science and Information Engineering form National ChengKung University
Time: 2024/10/15 (Tue) 14:10-16:00
Venue: TheManagement Building, 11F, AI Lecture Hall
Join Online: https://gqr.sh/NU8B
About the Speaker:
Dr. Ying-Jia Lin is a postdoctoralresearcher at National Tsing Hua University. He received his PhD from theDepartment of Computer Science and Information Engineering at National ChengKung University in 2024. Prior to that, he obtained his MS from the Instituteof Biomedical Informatics at National Yang-Ming University in 2019 and his BSin Biomedical Sciences from Chang Gung University in 2017. His current researchfocuses on text summarization, model compression, and BioNLP. Ying-Jia Lin haspublished in top AI/NLP conferences, such as AAAI, EMNLP, and AACL. He is anhonorary member of the Phi Tau Phi Society, and he won two Best Paper Awards atTAAI in 2022 and 2019.
Abstract:
This presentation explores the evolution ofNatural Language Processing (NLP) from the foundational concept of wordembeddings to the emergence of large-scale language models like GPT. In thefirst part, we will journey through the history of NLP, highlighting keydevelopments that have led to the current state of the field. The second partcritically examines whether GPT has really solved the challenges of NaturalLanguage Generation, using text summarization as a case study. We will discussarchitectural issues inherent in GPT models, such as those related to theKey-Value (KV) cache, and examine knowledge limitations, particularly in theapplication of GPT to medical text reports. The role of Retrieval-AugmentedGeneration (RAG) in addressing these challenges will also be explored. Thistalk aims to provide insights into the advancements and remaining hurdles inNLP, offering perspectives on future directions and prospects.
Organizers: Collegeof Intelligent Computing & Artificial Intelligence Research Center
※ No registration needed.
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: Decoding Biological Language in Membrane Proteins: Combining Protein Language Model Embeddings with Multi-Window Scanning Deep Learning for Functional Identification
Speaker: Yu-Yen Ou Prof. of Dept. of Computer Science and Engineering,Yzu University
Time: 2024/09/24(Tue) 14:00-16:00
Venue: The Management Building, 11F,
Chang Gung University, AI Lecture Hall
Join Online: https://gqr.sh/awHN
About the Speaker:
Yu-Yen Ou earned his Ph.D. in Computer Science from National Taiwan University in 2005 and is currently a professor in the Department of Computer Science and Engineering at Yuan Ze University. His research interests include machine learning and bioinformatics, particularly focusing on protein sequence function prediction and identification. Since 2016, he has pioneered the integration of deep learning and natural language processing in membrane protein sequence analysis.
Professor Ou has received the 21st Outstanding Professor Award from the Far Eastern Y. Z. Hsu Science and Technology Memorial Foundation and several research grants from Taiwan's Ministry of Science and Technology.
Abstract:
This presentation will explore the integration of protein language pre-training models with multi-window scanning deep learning techniques to decode and analyze the biological language embedded within membrane protein sequences. We will first utilize protein language models such as ProtTrans or ESM-2 to transform protein sequences into high-dimensional vector embeddings, capturing the intricate biological language within these sequences. Following this, we will employ multi-window convolutional neural networks (MCNN) to extract features across various scales, enabling the identification of membrane protein functions based on these language features. This innovative approach, combining language models with multi-scale analysis, not only enhances our understanding of membrane and transporter proteins but also offers new perspectives and potential applications in the field of bioinformatics.
Organizers: College of Intelligent Computing & Artificial Intelligence Research Center
※ No registration needed.
Topic: INDUSTRIAL AI : From Theory to Practices
Speaker: Charles Tsao – AVP of Wistron NeWeb Corp / Prof. of Dept. of Computer Science, NYCU
Time: 2024.09.03(二) 14:00-16:00
Venue: CGU Artificial Intelligence Research Center (Management Building 11F)
Join Online: https://reurl.cc/MjRQKk
About the Speaker:
Charles Tsao earned his PhD degree in engineering science from National Cheng Kung University, Taiwan in 1999. His research interests include energy-aware computing, embedded software and system, and mobile communication and wireless network. He was a visiting scholar at Bell Labs, Lucent technologies, USA, in the summer of 1998, a visiting professor at Dept. of Electrical and Computer Engineering, University of Waterloo, Canada, in the summer of 2007, and Dept. of Computer Science, ETH Zurich, Switzerland, in the summer of 2010 and 2011, and 2012-2013. From 1999 to 2003, Dr. Tsao joined Computers and Communications Research Labs (CCL) of Industrial Technology Research Institute (ITRI) as a researcher and a section manager. Dr. Tsao is currently a professor of Dept. of Computer Science of National Chiao Tung University. Prof. Tsao has published more than 100 international journal and conference papers, and has held or applied 21 US patents. Prof. Tsao received the Research Achievement Awards of ITRI in 2000 and 2004, Highly Cited Patent Award of ITRI in 2007, Outstanding Project Award of Ministry of Economic Affairs (MOEA) in 2003, and Advanced Technologies Award of MOEA in 2003. He also received the Young Engineer Award from the Chinese Institute of Electrical Engineering in 2007, Outstanding Teaching Award of National Chiao Tung University, K. T. Li Outstanding Young Scholar Award from ACM Taipei/Taiwan chapter in 2008, and 2013 Award for Excellent Contributions in Technology Transfer from National Science Council. He is a member of IEEE.
Talk Abstract:
l Why industrial AI
l Overview of industrial AI Applications and Their Theory
l Challenges From Zero to One
l What’s Next: from industrial AI to Industrial GAI
Organizers: College of Intelligent Computing & Artificial Intelligence Research Center
※ No registration needed.