Dawn Holmes

Dr. Dawn E. Holmes is a Teaching Professor at the University of California, Santa Barbara (UCSB), affiliated with the Department of Statistics and Applied Probability and the College of Letters and Science. She holds dual office locations: South Hall 5506 (primary) and Cheadle Hall 1117F. Her work focuses on Bayesian Networks, Maximum Entropy Formalism, Machine Learning, and statistical education innovation. She received the Academic Senate Distinguished Teaching Award 2007-2008 for her contributions to pedagogy.

Research Interests: Holmes' research spans foundational topics in Bayesianism, data mining, and intuitionistic mathematics. She has pioneered methods for handling incomplete information in causal networks using entropy optimization and has contributed to probabilistic decision support systems. Her work bridges theoretical advancements with practical applications in education and healthcare.

Editorial Contributions: Holmes serves on editorial boards for journals such as The Reasoner, Journal of Supercomputing, and International Journal of Knowledge-Based and Intelligent Information Systems. She has guest-edited special issues on maximum entropy applications and authored/co-edited influential books in data mining and Bayesian networks.

Teaching and Mentorship: Holmes teaches both undergraduate and graduate courses in statistics, stochastic processes, and Bayesian methods. She has supervised PhD students and internships, emphasizing statistical computing and pedagogical innovation. Her courses include PSTAT 5A (Statistics), PSTAT 160A/B (Stochastic Processes), and specialized topics like Bayesian Networks (PSTAT 262BN).

Awards and Recognition: Beyond her teaching award, Holmes has published over 50 peer-reviewed articles and authored/co-edited eight books. Her work appears in venues like Statistical Methods in Medical Research and Bayesian Inference and Maximum Entropy Methods.

Labs and Teams: While no specific lab is named, her research collaborations involve interdisciplinary teams addressing challenges in data mining, healthcare analytics, and educational technology. Her editorial roles reflect active participation in global academic networks.