Anke Meyer-Baese

Short CV

Anke Meyer-Baese received the M.S. degree in electrical and computer engineering from the Technical University of Darmstadt in 1990 and the Ph.D. degree in 1995 in the same field from the same university. From 1995 to 2001, she was a postdoctoral fellow with the Federal Institute of Neurobiology in Magdeburg, Germany, and a visiting research professor with the Department of Electrical and Computer Engineering at the University of Florida. In 2001, she joined the electrical and computer engineering faculty at Florida State University and became a full professor in 2011 in Scientific Computing at the same university. She wrote 3 research monographs, published 250 technical papers and supervised the study of 9 Ph.D. dissertations and 7 postdoctoral students. She served as the technical program chair of the SPIE Sensing and Analysis Technologies for Biomedical and Cognitive Applications in 2016 and as the Guest Associate Editor for Frontiers in Computational Neuroscience from 2016 to 2022 and for Cancers from 2021 to present.

Selected Awards

• 2022, Eleonore Trefftz Guest Professorship
• 2016, Fulbright U.S. Scholar
• 2014, Marie-Curie-Fellow
• 2014, DAAD Visiting Professor
• 2013, Senior Member IEEE
• 2006, College of Engineering Research Award
• 2004, Alexander von Humboldt Research Fellowship 

Research Interests

Graph dynamical systems, machine learning, artificial intelligence, computer-aided diagnosis research, brain cancer and dementia research.

Selected Publications

• A. Moradi Amani, A. Tahmassebi, A. Stadlbauer, U. Meyer-Baese, H. Malberg and A. Meyer-Baese. Controllability of Functional and Structural Brain Networks. Complexity, 1:7402894, 2024.
• A. Stadlbauer, K. Nikolic, S. Oberndorfer, F. Marhold, T. M. Kinfe, A. Meyer-Baese. D. A. Bistrian, O. Schnell and A. Doerfler. Machine Learning-Based Prediction of Glioma IDH Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study). Cancers, 16: 1102, 2024.
• A. Meyer-Baese, K. Juetten, U. Meyer-Baese, A. Stadlbauer, T. M. Kinfe, H. Malberg and C-H. Na. Controllability and Robustness of Functional and Structural Connectomic Networks in Glioma Patients, Cancers, 15:10, 2714, 2023.
• J. Hu, Q. Zhang, A. Meyer-Baese and M. Ye. Finite-time stability and optimal control of a stochastic reaction-diffusion model for Alzheimer’s disease with impulse and time-varying delay. Applied Mathematical Modelling, 102, 511-539, 2022.
• J. Hu, A. Meyer-Baese and Q. Zhang. Analysis of a stochastic reaction–diffusion Alzheimer’s disease system driven by space–time white noise. Applied Mathematics Letters, 134, 108308, 2022.
• J. Hu, Q. Zhang, A. Meyer-Baese and M. Ye. Stability in distribution for a stochastic Alzheimer’s disease model with reaction diffusion. Nonlinear Dynamics, 108:4243-4260, 2022.
• A. Stadlbauer, F. Marhold, S. Oberndorfer, G. Heinz, M. Buchfelder, T. M. Kinfe and A. Meyer-Baese. Radiophysiomics: brain tumors classification by machine learning and physiological MRI data. Cancers, 14, 2363, 2022.
• A. Meyer-Baese, L. Morra, A. Tahmassebi, M. Lobbes, U. Meyer-Baese and K. Pinker. (invited) AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer. Journal of Magnetic Resonance Imaging, 2020.
• A. Meyer-Baese, A. Moradi Amani, U. Meyer-Baese, S. Foo, A. Stadlbauer and W. Yu. Pinning observability of competitive neural networks with different time–constants. Neurocomputing. Vol. 329, 97-102, 2019.
• A. Meyer-Baese, R. Roberts, I. Illan, U. Meyer-Baese, M. Lobbes, A. Stadlbauer and K. Pinker-Domenig. Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks. Frontiers in Computational Neuroscience, 11:87, 2017.