معرفی
Dr. Chong Ho "Alex" Yu is a Professor and Program Director of Data Science and Artificial Intelligence at Hawaii Pacific University, where he is affiliated with the College of Natural and Computational Sciences and the Department of Mathematics. With dual Ph.D.s in Measurement, Statistics and Methodological Studies and Philosophy from Arizona State University, Dr. Yu brings a unique interdisciplinary perspective to his work at the intersection of data science, statistics, and philosophical inquiry.
Dr. Yu's educational background includes:
- Ph.D. in Measurement, Statistics and Methodological Studies, Arizona State University
- Ph.D. in Philosophy, Arizona State University
- M.A. in Theology, Azusa Pacific University
- M.Ed. in Educational Psychology, University of Oklahoma
- MHR in Human Relations, University of Oklahoma
- B.S. in Mass Communication, Bemidji State University
- B.A. in Visual Arts, Bemidji State University
- Graduate Certificate in Public Health, University of Arizona
- Graduate Certificate in Liberal Studies, Hamline University
- Certificate in History and Philosophy of Science, Arizona State University
Dr. Yu's research interests span multiple domains with a focus on data science and machine learning methodologies. He is particularly interested in alternate and emerging research methods including exploratory data analysis, data visualization, and item response theory. His work also examines philosophical aspects of research methodologies such as causal inferences, the nature of latent constructs, and abductive reasoning. Additionally, he conducts cross-cultural comparisons in STEM education using international assessments like PISA, PIAAC, and TIMSS. As Vice President for Statistics Education and President-elect of the Southern California Chapter of the American Statistical Association (SCASA), he actively disseminates knowledge through a weekly newsletter and manages the statistics and data science newsroom on SCASA's website.
Dr. Yu's extensive publication record demonstrates his expertise across multiple disciplines. His recent work shows a clear trend toward applying data science and machine learning techniques to diverse fields including healthcare, education, and psychology. He has made significant contributions to understanding the relationship between well-being and academic performance using large-scale assessment data, as well as developing applications of machine learning in educational science. His work often bridges theoretical statistical concepts with practical applications, reflecting his commitment to making data science accessible and relevant across disciplines.
Dr. Yu has received numerous professional accolades including:
- Three-time winner of the SAS faculty scholarship (2016, 2017 SAS Global Forum and 2017 Western Users of SAS Software Conference)
- Distinguished SAS Educator Award in 2021
- Vice President for Statistics Education of the Southern California Chapter of the American Statistical Association
- President-elect of the Southern California Chapter of the American Statistical Association
As an educator, Dr. Yu is committed to staying current with industry developments to provide students with cutting-edge skills. He regularly attends conferences and workshops run by leading data analytics companies including SAS Institute, Salesforce (Tableau), and Amazon Web Services. His dedication to connecting academic knowledge with industry needs has resulted in a curriculum that prepares students for real-world data science challenges. Dr. Yu teaches courses including Statistics, Pre-Calculus, Probability, Applied Statistics, Data Science and Machine Learning, Data Visualization, Data Ethics, and Text Mining and Unstructured Data.
Dr. Yu maintains active research collaborations across multiple institutions and disciplines. His work often involves interdisciplinary teams examining topics at the intersection of data science, healthcare, and education. Through his leadership as Program Director of Data Science, he has developed initiatives that connect students with industry professionals and real-world data challenges.
