John ChenProfessor John T. Chen has been teaching mathematical statistics since 1998 at various universities including, The University of Sydney (1996–1997, Australia), McMaster University (1997–1998 Canada), University of Pittsburg (1998–2000), Bowling GreenState University (2000–present), University of Michigan (2010 fall) and University of California, Berkeley (2017 fall). He has published two books, one on multivariate Bonferroni inequalities and another on prediction and statistical learning. Dr Chen’s research comprises of theoretical topics on probability inequalities, distribution theory and simultaneous inference. This aspect is featured by papers published in Biometrika, the Annals of the Institute of Mathematical Statistics, Journal of Applied Probability, among others. Besides theoretical statistics, his research also embraces applications of statistical methodologies to medical investigations and biostatistical consulting. This is reflected by papers published in Biometrics, the Annals of Neurology, The Annals of Thoracic Surgery, Journal of Vascular Surgery, among others. Dr. Chen enjoys cooperating rigorous research thinking and cutting-edge applications of statistical practices into classrooms to inspire students. With his experience and teaching efforts, Dr. Chen has earned teaching-related awards including, Teaching Excellence Awards by the Kappa Mu Epsilon Mathematics Honorary Society (2002 and 2006, BGSU chapter), Appreciations of Faculty Excellence (2019, 2020, 2021, BGSU), Certificate in Effective College Instruction recognized by the Association of College and University Educators and the American Council on Education (2023) and BGSU president’s Innovation award in AI teaching and learning (2024). Part of the materials in this book stem from his teaching notes and lesson plans accumulated over years of his enriched teaching experience. Read More Read Less