BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20260513T131341EDT-5373m2HZuu@132.216.98.100 DTSTAMP:20260513T171341Z DESCRIPTION:Li-Pang Chen\, PhD\n\nAssociate Professor\, Dept of Statistics\ , National Chengchi University (NCCU)\n\nNote: Meet & Greet Prof Li-Pang C hen from 3-3:30pm in Room 1140\; Prior to seminar 3:30-4:30pm\n\nWHEN: Wed nesday\, September 17\, 2025\, from 3:30 to 4:30 p.m.\n WHERE: Hybrid | 200 1 McGill College Avenue\, Rm 1140\; Zoom\n NOTE: Li-Pang Chen will be prese nting in-person\n\nAbstract\n\nIn medical studies and bioinformatics\, an important research direction is the analysis of time-to-event data\, where the main challenges often arise from incompleteness due to censoring mech anisms. With the growing ease of data collection\, it is now common to enc ounter datasets with a large number of variables. Among these\, even rare variables may carry valuable information. Another major challenge is measu rement error\, a typical feature of noisy data. In my presentation\, I wil l introduce my recent work on survival analysis with multivariate or high- dimensional error-prone variables from the perspective of statistical mach ine learning. Specifically\, I will first present graphical proportional h azards models\, which incorporate network structures among variables. To s imultaneously handle variable selection and network detection\, I propose a penalized likelihood approach with a double-penalty function. Next\, I w ill introduce the accelerated failure time model for interval-censored sur vival data. To perform variable selection when the estimating functions ar e possibly non-differentiable\, a boosting algorithm is developed to ident ify informative variables and provide associated estimation. A key advanta ge of this approach is that it avoids optimization with penalty functions. Finally\, real data applications will be presented.\n\nSpeaker Bio\n\nDr. Li-Pang Chen is an Associate Professor in the Department of Statistics at National Chengchi University (NCCU)\, Taiwan. He received his Ph.D. in St atistics from the University of Waterloo\, Canada\, in 2019\, and subseque ntly held a postdoctoral fellowship at the University of Western Ontario f rom 2019 to 2020. His research focuses on developing and applying statisti cal methodologies in biostatistics\, high-dimensional data analysis\, meas urement error models\, and statistical machine learning. In addition to hi s research\, Dr. Chen serves as a Guest Editor for the special issue Stati stical Analysis and Data Science for Complex Data in the journal Mathemati cs\, and as an Associate Editor for The New England Journal of Statistics in Data Science (Methodology Section). Further details are available on Dr . Li-Pang Chen’s website.\n DTSTART:20250917T193000Z DTEND:20250917T203000Z SUMMARY:Statistical Machine Learning Methods for Noisy Survival Data Analys is URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/statistical-machi ne-learning-methods-noisy-survival-data-analysis-367107 END:VEVENT END:VCALENDAR