Dr. Daniel Rowe

Dr. Daniel RoweMarquette University

Cudahy Hall, 313

MilwaukeeWI53201United States of America(414) 288-5228daniel.rowe@marquette.eduPersonal Website

Professor, Data Science Co-Director

Department of Mathematical and Statistical Sciences

Courses Taught

• Statistical Machine Vision
• Computational Statistics
• Statistical Simulation
• Biostatistical Methods and Models
• Modern Elementary Statistics

Research Interests

• Bayesian Statistics
• Computational Neuroscience
• Multivariate Big Data Analysis
• Statistical Data Science
• Statistical Image Processing & Estimation

Publications

• Adrian D.W., Maitra R., Rowe D.B., Rice-Distributed Autoregressive Time Series Modeling and Improved Activation Estimation in FMRI Studies. Annals of Applied Statistics. In Press, 2024.
• Sakitis C.J., Rowe D.B., Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power. Magnetic Resonance Imaging, In Press, 2024.
• Wang Z., Rowe D.B., Li X., Brown D.A., Efficient Fully Bayesian Approach to Brain Activity Mapping with Complex-Valued fMRI Data. Journal of Applied Statistics, In Press, 2024.
• Sakitis C.J., Rowe D.B., A Bayesian Approach to GRAPPA Parallel FMRI Image Reconstruction Increases SNR and Power of Task Detection. Annals of Applied Statistics, In Press, 2024.
• Mathew S., Sra J., Rowe D.B., Generation of Patient Specific Cardiac Chamber Models Using Generative Neural Networks Under a Bayesian Framework for Electroanatomical Mapping. Journal of Statistical Theory and Practice, In Press, 2024.
• Sakitis C.J.,Brown D.A., Rowe D.B., A Bayesian Complex-Valued Latent Variable Model Applied to FMRI. Journal of the Royal Statistical Society-Series C, In Press, (2024).
• Wang Z, Rowe D.B., Li X, Brown DA, A Fully Bayesian Approach for Comprehensive Mapping of Magnitude and Phase Brain Activation in Complex-Valued fMRI Data. Magnetic Resonance Imaging, 109:271-285, 2024.