Assist. Prof. Dr. Baosheng Liang, Peking University, China.
Dr. Baosheng Liang is an Assistant Professor in the Department of Biostatistics at Peking University, specializing in biostatistics, survival analysis, and causal inference. With a Ph.D. in Probability Theory & Mathematical Statistics from Beijing Normal University, he has held key research roles, including a postdoctoral position at The University of Hong Kong. His expertise lies in developing robust statistical models and innovative data analysis techniques. Dr. Liang actively contributes to recurrent-event data analysis, meta-analysis, and Bayesian regression research, advancing statistical methodologies in healthcare and medical sciences. 
Publivation Profiles
Education and Experience
Education:
Ph.D. in Probability Theory & Mathematical Statistics, Beijing Normal University (2012-2016)- M.S. in Probability Theory & Mathematical Statistics, Beijing Normal University (2009-2012)
- B.S. in Mathematics & Applied Mathematics, Qingdao University (2005-2009)
Experience:
Assistant Professor, Peking University (2018–Present)- Postdoctoral Researcher, The University of Hong Kong (2016–2018)
Joint Training Ph.D., University of North Carolina at Chapel Hill (2013–2014)
Suitability summary
Dr. Baosheng Liang, an Assistant Professor in the Department of Biostatistics at Peking University, is a distinguished researcher in the field of biostatistics. With an extensive background in probability theory, statistical learning, and causal inference, he has made significant contributions to advancing statistical methodologies for biomedical and clinical research. His academic journey spans prestigious institutions such as the University of North Carolina at Chapel Hill, the University of Hong Kong, and Beijing Normal University, where he honed his expertise in survival analysis, semiparametric models, and robust statistical methods. His research has significantly enhanced the understanding of recurrent-event data analysis and meta-analysis techniques, making him a strong candidate for the Best Researcher Award.
Professional Development
Dr. Liang has significantly contributed to biostatistical research, focusing on survival analysis, semiparametric models, and causal inference. His work extends to recurrent-event data analysis, robust statistical methods, and machine learning applications. Through collaborative projects and academic publications, he has enhanced statistical modeling for healthcare and epidemiology. His methodological advancements in incomplete data analysis and subgroup analysis provide deeper insights into public health and medical research. As an academic mentor and researcher, Dr. Liang continually refines statistical learning techniques, ensuring their adaptability in real-world applications. 
Research Focus