Image credit: Lingyu Li
Abstract
The Peking University Biostatistics Summer School is jointly sponsored by the Peking University School of Public Health and the Beijing International Center for Mathematical Research, and hosted by the Department of Biostatistics of the Peking University School of Public Health. This summer school invited two internationally renowned biostatisticians from the University of North Carolina at Chapel Hill to focus on the frontier of survival analysis. Through online classes + offline Q&A and tutoring, it aims to accelerate the cross-integration of biostatistics with mathematics, preventive medicine, pharmacy, health big data and other disciplines, and provide a world-class university learning experience for outstanding graduate students and young scholars who love biostatistics and are interested in related research. The theme of the 2021 summer school is Survival Analysis. Survival analysis is a discipline that studies survival phenomena and response time data and their statistical laws. Specifically, it is a method to analyze and infer the survival time or outcome event time of interest based on experimental or survey data, and to study the relationship between survival or outcome time and many influencing factors. Survival analysis is the most frequently used basic tool discipline in biostatistics for studying complex data analysis. It has extensive and important practical applications in biology, clinical medicine, public health, vaccine and other drug development, precision medicine, insurance actuarial science, reliability research and other fields. This summer school is open to young teachers or scientific researchers, postdoctoral fellows, doctoral students, master students and excellent senior undergraduates in statistics, biostatistics, epidemiology and health statistics, public health, data science and health care big data analysis and other related majors at home and abroad. Applicants must have a good foundation in mathematics, statistics and data science, have a strong interest in statistics, medicine or health care data science, be willing to engage in academic research in this field, and have strong academic research ability. "
Lingyu Li
Postdoctoral Fellow
Focus on bioinformatics, including but not limited to spatial transcriptomics analysis, sparse statistical learning and biomarker identification.