浙江农林大学生物统计模型及应用国际会议会议安排

一、会议时间

2025年12月6日-12月7日

二、会议地点

12月6日下午，报到，地点：学11-503

12月7日上午，特邀报告，会议地点：学11-304

12月7日下午，主题报告，会议地点：学11-304

三、会议简介

本次会议由浙江农林大学主办，浙江农林大学数学与计算机科学学院承办。本次会议以“生物统计模型及应用”为主旨,邀请国内外著名专家学者出席并分享前沿研究成果和应用案例。会议从统计模型及其应用角度出发，围绕统计模型、生物统计、图书情报以及在农林领域的研究热点与难点展开讨论，为统计、大数据、图书情报与农林产业的深度融合提供理论与实践路径。本次大会旨在推进我校数学学科发展，助力科技创新与青年人才培育，深化统计、大数据技术与农林领域的交叉融合，加强国内外学者交流与合作，明确学科未来发展方向，促进科研创新。

四、会议安排

五、特邀报告专家简介

1.Yuhui Weng was trained as a forester in China and later in Canada, earning his Ph.D. in forest quantitative genetics from the University of New Brunswick in 2009. He worked for many years as a forester in China and subsequently served in government forestry agencies in Ontario and New Brunswick, Canada. In 2008, he became the scientific leader of New Brunswick’s provincial tree improvement program and was promoted to Senior Research Scientist in 2015.

In 2016, Dr. Weng joined the Arthur Temple College of Forestry and Agriculture at Stephen F. Austin State University in Texas, USA. He is currently an Associate Professor, teaching courses in forest quantitative management and graduate-level biometrics for master’s and doctoral students. His research focuses on forest quantitative management, forest genetics, and data analysis, and he has published more than 80 peer-reviewed SCI journal articles.

Dr. Weng currently serves as an Associate Editor for Tree Genetics and Genomes (Springer Nature) and Frontiers in Forests and Global Change.

2.HaiYing Wang is an Associate Professor in the Department of Statistics at the University of Connecticut. Prior to his current position, he was an Assistant Professor of Statistics at the University of New Hampshire from 2013 to 2017. He received his Ph.D. in Statistics from the University of Missouri and his M.S. from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, in 2006. Dr. Wang’s research interests include informative subdata selection for big data, model selection and averaging, measurement error models, and semi-parametric regression. His work has been published in leading statistics and machine learning journals, such as Biometrika, IEEE Transactions, ASA, and JMLR, as well as at premier conferences including ICML and NeurIPS.

3.Lifeng Han is a Professor of Practice in the Department of Mathematics at Tulane University, where he teaches and coordinates Statistics for Scientists and focuses on helping students build strong quantitative and critical thinking skills for a data-driven world. His research is in mathematical biology, with applications to cancer growth and treatment, immunotherapy, and cellular transport processes. Before joining Tulane, he was a postdoctoral fellow at the U.S. Food and Drug Administration, working on mathematical modeling of cancer immunotherapy and hybrid PK/PD–machine learning approaches for COVID-19 monoclonal antibody treatment. He received his PhD in applied mathematics from Arizona State University, where he studied spatio-temporal and stochastic models of cancer invasion. His work has appeared in journals such as Mathematical Medicine and Biology, Bulletin of Mathematical Biology and Mathematical Biosciences.

4.Chengjun Zhang, as a professor and doctoral supervisor at Zhejiang Agriculture and Forestry University, mainly engages in bioinformatics, genome evolution, and the industrialization of new plant varieties. He graduated from Huazhong Agricultural University in 2011. After completing his postdoctoral research at the University of Chicago in 2014, he served as a researcher and doctoral supervisor at the Kunming Institute of Botany, Chinese Academy of Sciences. He won the First Prize of Yunnan Province Technology Invention Award. He has published over 40 papers in internationally renowned journals such as Nature Genetics, PNAS, Genome Biology, MBE, and PBJ, and has obtained 2 authorized software copyrights.