C. David Page Jr.

C. David Page Jr. is a distinguished professor at the University of Wisconsin-Madison, where he holds dual appointments in the Department of Biostatistics and Medical Informatics and the Department of Computer Sciences. His academic journey is marked by a profound dedication to advancing the fields of data mining and machine learning, with a particular emphasis on their application to biomedical data. His work is instrumental in the analysis of de-identified electronic health records and high-throughput genetic and molecular data, which are pivotal in modern healthcare research. Professor Page's scholarly contributions are extensive and varied, encompassing a wide range of topics within bioinformatics and machine learning. He has made significant strides in Inductive Logic Programming (ILP) and Statistical Relational Learning (SRL), areas that are crucial for developing intelligent systems capable of reasoning and learning from complex data sets. His research on Skewing: Learning Correlation Immune Targets has provided valuable insights into creating robust learning algorithms that can handle skewed data distributions. In the realm of pharmacophore discovery, Professor Page's work has been pivotal in advancing drug design methodologies. His research has contributed to the identification of molecular structures that are essential for the development of new therapeutic agents. Additionally, his analysis of high-throughput genetic data has shed light on the genetic underpinnings of various diseases, paving the way for personalized medicine approaches. Beyond his research endeavors, Professor Page is actively involved in the academic community. He has served on numerous committees, including a National Institutes of Health (NIH) Study Section focused on Bio-Data Management and Analysis. His leadership extends to organizing conferences and workshops, where he fosters collaboration and knowledge exchange among researchers and practitioners in the field. Professor Page's work continues to influence the landscape of biomedical informatics, driving innovations that bridge the gap between computational methods and practical healthcare applications. His commitment to advancing the understanding and utilization of complex data sets underscores his role as a leader in the intersection of computer science and biomedicine.