Welcome to join this lecture and learn how data science and AI technologies can help advance medical research and clinical care toward a smarter future!

📌This lecture will introduce a fast and efficient approach for semiparametric multivariate joint modeling, designed to overcome the computational challenges commonly encountered when analyzing large-scale biobank datasets. The method enables accurate simultaneous analysis of multiple longitudinal biomarkers and competing risk events, and its practical performance will be demonstrated using UK Biobank data. Participants may apply this modeling framework in their own research through the accompanying R package, FastJM.

The package is available for download at: https://CRAN.R-project.org/package=FastJM

 📅 Lecture Information

▪️ Topic: Scalable Joint Modeling of Multiple Longitudinal Biomarkers and Competing Risks Time-to-Event Data: with Applications to Mega-Scale Health Research
▪️ Speaker: Gang Li (Professor of Biostatistics and Computational Medicine, University of California, Los Angeles)
▪️ Date & Time: December 26, 2025 (Friday), 12:10–13:30
▪️ Venue: Room IR630, 6th Floor, International Academic Research Building, Kaohsiung Medical University

▪️ Registration Link: https://forms.gle/94Pxhyy1h5oCGvtn7
▪️ Registration Deadline: Until 12:00 PM, December 23, 2025 (Monday); online registration only
▪️ Format: Hybrid (On-site: 60 participants; Online: unlimited)
▪️ Organizer: Biomedical Artificial Intelligence Academy, Kaohsiung Medical University

▪️ Note: ☕ Lunch will be provided for on-site participants. Everyone is welcome to join!

※ This lecture will be eligible for KMU faculty growth credits. Participants must complete both sign-in/out and the satisfaction survey to receive credit points.

We look forward to your participation!