Rate My Professor Daniel Schmidt

Schmidt's research specializations include Bayesian inference for high-dimensional regression models such as linear and generalized linear models; information theoretic statistics through Minimum Message/Description Length principles; statistical genomics for risk prediction and variant discovery in cancer genomics; and machine learning applications with mammography to improve breast cancer risk stratification for personalized screening. He co-developed a toolbox for state-of-the-art Bayesian shrinkage priors in high-dimensional regression models. His scholarly impact is evidenced by over 16,000 citations on Google Scholar. Key publications comprise 'Adaptive Bayesian Shrinkage Estimation Using Log-Scale Shrinkage Priors' (2018) and 'A Minimum Message Length Criterion for Robust Linear Regression' (2018). Schmidt holds an adjunct Senior Research Fellow position at the University of Melbourne since March 2018. He supervises PhD students and leads projects including ARC Discovery Projects on efficient time series classification (2024–2027), quantum information technology (2023–2027), and colloidal perovskite nucleation (2026–2030). In 2025, he received the Faculty of Information Technology Dean's Award for Research Excellence in Machine Learning. His contributions align with UN Sustainable Development Goal 3: Good Health and Well-being.