Institute of Health Data Science

Topic: Merging Sensors, AI, and VR for Athlete Training Speaker: Prof. Min-Chun Hu — Associate Chair, Department of Computer Science at National Tsing Hua University. Time: March 10, 2026 (Tuesday), 2:00 – 4:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall Join Online: https://reurl.cc/EbkDzv or scan QR code on poster  About the Speaker:   Dr. Min-Chun Hu received her Ph.D. from the Graduate Institute of Networking and Multimedia, National Taiwan University in 2011. After graduation, she worked as a postdoctoral researcher at the Research Center for Information Technology Innovation in Academia Sinica, and later served as an assistant/associate professor in the Department of Computer Science and Information Engineering at National Cheng Kung University. She is currently a professor in the Department of Computer Science at National Tsing Hua University. Dr. Hu has been recognized with numerous awards, including Exploration Research Award of Pan Wen Yuan Foundation (2015), Outstanding Young Researcher Award from the Computer Society of the Republic of China (2017), IEEE Tainan Section Best Young Professional Member Award (2018), Google Research exploreCSR Award (2021-2024), CES Innovation Awards (2023), and NSTC Ta-You Wu Memorial Award (2023). Her research interests encompass digital signal processing, multimedia content analysis, computer vision, computer graphics, virtual reality, and augmented reality. As a passionate basketball enthusiast, she has long been dedicated to developing sports technology that assists athletes in training and performance analysis. Dr. Hu previously served as Deputy Executive Director of the Taiwan Institute of Sports Science and is also the co-founder of NeuinX, a startup specializing in AI technology for sports analysis. Abstract: Tactical and skill training play a crucial role in athletic development. With the support of artificial intelligence (AI) technology, it is now possible to track the ball and players to detect fine-grained events, helping coaches collect detailed statistics and infer each team’s tactics. Additionally, virtual reality (VR) technology can be leveraged to enhance both the effectiveness and experience of tactical and skill-based training. This talk will introduce modern systems that utilize AI and VR to help athletes conveniently gather valuable sports data and improve a wide range of skills. Organizers: College of Intelligent Computing & Artificial Intelligence Research Center ※ No registration needed.

Topic: Integrating genetics, multimodal medical imaging, and AI for high-precision risk prediction of type 2 diabetes Speaker: Hsin-Chou Yang, Ph.D. Research Fellow and Director of the Institute of Statistical Science, Academia Sinica Time: December 24, 2025 (Wednesday), 12:10 – 14:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall or Livestreaming https://reurl.cc/R9Ynp6 Registration: https://forms.office.com/r/YGHweb7HNT or scan QR code on poster  About the Speaker:   Dr. Hsin-Chou Yang is a statistician, bioinformatician, and data scientist at Academia Sinica, where he is a Full Research Fellow and Director. His work integrates methodological innovation with biomedical applications, including major studies on the Han Chinese cohort (Nature), AI-based diabetes prediction (Nature Communications), and COVID-19 viral subtypes (PNAS). He is an elected ISI and GYA member, recipient of multiple national awards, active journal editor/reviewer, conference chair, and mentor to young scientists. Abstract: In this talk, the speaker will share how the team combined genetics, medical imaging, and rich health data from over 131,000 Taiwan Biobank participants to better understand and predict Type 2 Diabetes (T2D). Using AI—from deep learning on raw images to polygenic and imaging risk scores—the study uncovered genetic links, causal relationships, and new insights into how the body signals T2D risk. The integrated model achieved an AUC of 0.944, demonstrating strong potential for scalable, real-world precision health. Organizers: Institute of Health Data Science ※ Registration needed.  

Curious what top engineers in the U.S. really do day to day? Want to hear authentic insights from a Google AI Platform engineer and a former Machine Learning Engineering Manager at Accenture? Don’t miss this event! Join Stephen and Rohisha as they share what life is truly like as engineers in the U.S., giving you a clearer picture of careers in AI/ML and data science—and inspiring your own path forward. Seats are limited, so sign up now! Time: December 4, 2025 (Thursday), 12:10 – 14:00 p.m. Venue: 14th Floor, CoIC Conference Room, Management Building Registration: https://forms.office.com/r/VBSgJpV4L8 or scan QR code on poster  About the Speaker:   Stephen Koo is a Staff Software Engineer at Google specializing in AI and backend systems. He leads the evaluation infrastructure for AI features on the Workspace AI Platform team and previously worked on the core serving stack for Generative AI in Workspace. Before that, he was on the Gmail & Chat team focusing on reliable email processing and smart features. He also developed deep learning models for speech recognition and helped rebuild Stanford AI’s CodaLab platform. He holds an M.S. and B.S. from Stanford.   Rohisha Adke was most recently a Machine Learning Engineering Manager at Accenture, leading teams that built data science and machine learning solutions for clients across energy, retail, healthcare, and tech. She began as a data scientist in Accenture’s global Data Science Center of Excellence, working with clients and tech partners to develop data and ML solutions. She also enjoyed co-presenting at conferences and mentoring aspiring technologists. Rohisha holds an M.S. in Statistics and a B.S. in Mathematical & Computational Science from Stanford and is currently on sabbatical. Abstract: Join us to hear from Stephen Koo, an AI engineer at Google, and Rohisha Adke, former Machine Learning Engineering Manager at Accenture. They will share their experiences, key skills, and challenges working as engineers in the U.S. tech industry across AI, data science, and machine learning fields. Organizers: CoIC ※ Registration needed.

Topic: Metaverse-enabled Precision Health Speaker: Tony Hsiu-Hsi Chen, PhD. Distinguished Professor, Institute of Health Data Analytics and Statistics, National Taiwan University Time: November 25, 2025 (Tuesday), 12:10 – 14:00 p.m. Venue: The Management Building, 11F, AI Lecture Hall or Livestreaming https://reurl.cc/oKx6Dg Registration: https://forms.office.com/r/nBdAaDsgsZ or scan QR code on poster  About the Speaker:   Prof. Hsiu-Hsi Chen specializes in evaluating population-based cancer screening and vaccination programs using advanced statistical models. His work addresses complex issues and develops health economic decision models. Key publications include studies on gastric, colorectal, breast, and oral cancer screening in top journals like JAMA and Gut. He has contributed significantly to stochastic process methods in cancer screening evaluation, published in leading statistics journals. Since 2004, he has chaired the International Asian Cancer Screening Conference Network and collaborated internationally, earning the Finland Distinguished Professor award. Prof. Chen has published over 460 peer-reviewed articles by 2025. Abstract: Metaverse-enabled Precision Health combines AI, digital twin technology, and immersive AR/MR to create a precision screening and prevention system across diseases. For example, COVID-19 digital twins simulate infection dynamics for real-time outbreak prediction and resource optimization. In chronic disease, hypertension digital twins integrate monitoring and lifestyle data for personalized risk and prevention. Cancer models use AI to analyze imaging and genetics, improving early detection and screening schedules. This approach enables dynamic, personalized health management by linking predictive modeling with clinical and public health settings, advancing P4 medicine and fostering an integrated digital health ecosystem for precision prevention. Organizers: Graduate Institute of Health Data Science ※ Registration needed.