Rate My Professor David Dowe

Associate Professor David Leonard Dowe serves in the Department of Data Science and Artificial Intelligence, Faculty of Information Technology, at Monash University. His academic journey began with a Bachelor of Science with Honours in Mathematics from the University of Melbourne in 1982, followed by a Master of Science in Econometrics from the London School of Economics and Political Science in 1987, and culminated in a Doctor of Philosophy in Mathematics from Monash University in 1991. At Monash, Dowe has held various teaching roles, including chief examiner for units such as FIT4009 Advanced Topics in Intelligent Systems and FIT5158 Customer Relationship Management and Data Mining. He has lectured in subjects like FIT1004 Data Management, FIT5047 Intelligent Systems, FIT5097 Business Intelligence Modelling, and FIT3036 Computer Science Project, demonstrating his expertise in intelligent systems, data management, and business decision-making.

Dowe's research specializes in human and artificial intelligence, with a particular emphasis on Minimum Message Length (MML) inference applied to machine learning, statistical modeling, clustering, mixture modeling, inductive inference, knowledge discovery, econometrics, and data mining. His interests also encompass philosophy of science, philosophy of inference, Turing tests, philosophy of mind, Bayesian networks, decision trees, decision graphs, and support vector machines, building upon foundational contributions from Chris Wallace, Ray Solomonoff, and Jorma Rissanen. Key publications include "Intrinsic classification by MML - the Snob program" (Wallace and Dowe, 1994, Proc. 7th Australian Joint Conference on Artificial Intelligence), which introduced the Snob program for MML-based clustering; "Minimum Message Length and Kolmogorov Complexity" (Wallace and Dowe, 1999, The Computer Journal); "Bayes Not Bust! Why Simplicity is no problem for Bayesians" (Dowe, Gardner, and Oppy, 2007, British Journal for the Philosophy of Science); "Measuring Universal Intelligence: Towards an Anytime Intelligence Test" (Hernandez-Orallo and Dowe, 2010, Artificial Intelligence); and recent works such as "A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification" (Yap et al., 2024, IJCAI) and "Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra" (Nguyen et al., 2024, mSystems). Dowe has supervised numerous honours and postgraduate students, contributing to advancements in AI and inference techniques.