MIND Lab Members

martin.reuter (at) dzne.de
0000-0002-2665-9693
Scholar Citations
@deepmilab
deepmi

Martin is Assistant Professor of Radiology and of Neurology at Harvard Medical School and Massachusetts General Hospital (Assistant in Neuroscience, Dept. of Radiology and Dept. of Neurology). He is Director of AI in Medical Imaging (Deep-MI Lab) at the German Center for Neurodegenerative Diseases (DZNE), affiliated to the Martinos Center for Biomedical Imaging and the MIT Computer Science and Artificial Intelligence Lab.

Martin graduated in mathematics with a second major in computer science and a minor in business informatics in 2001 at Leibniz University, Hanover, Germany. He obtained his PhD (2005) in the area of computational and differential geometry from the department of electrical engineering and computer science with summa cum laude and was awarded a prize for his outstanding scientific accomplishments by the University of Hanover.

Supported by a Feodor-Lynen fellowship of the Alexander von Humboldt Foundation he moved to MIT (2006-08) and contributed novel methods for non-rigid shape analysis and processing, receiving a most cited paper award of the Computer-Aided Design journal for his ground-breaking manuscript on spectral shape analysis (ShapeDNA). Martin then moved to MGH/Harvard Medical School in 2008 and began his independent faculty career as Instructor at HMS in 2009/10 transitioning his focus to more applied medical image analysis topics. He contributed novel methods for unbiased longitudinal image processing of brain MRI and structural shape analysis for computer-aided diagnosis and prognosis. His methods are widely employed as part of the open source FreeSurfer software package to study neurodegeneration and assess disease modifying therapies, e.g., by the Alzheimer’s Disease Neuroimaging Initiative, the Rhineland Study, and other large cohort studies.

After receiving a competitive NIH Career Award (2014) for his research on computational methods for medical image analysis, Martin was appointed Assistant Professor by both the Radiology and the Neurology Departments at Harvard Medical School in 2015. He, further, accepted (2017) the offer to direct the AI in Medical Imaging Lab at the German Centerfor Neurodegenerative Diseases (DZNE) in Bonn, Germany. During his career so far, Martin has attracted approximately $3 mio worth of 3rd party funding.

xxy1302@mavs.uta.edu
https://shawey94.github.io/
Scholar Citations
Shawey94

Xiaowei Yu received the B.E. degree from Northwestern Polytechnical University, China, in 2016 and the M.E. degree from Shanghai Jiao Tong University, China, in 2019. He is currently pursuing a Ph.D. in the Department of Computer Science and Engineering at UT Arlington, Texas, USA. His research focuses on developing novel machine learning and deep learning algorithms and their applications in brain science and biomedical image analysis.

kersten.diers (at) dzne.de
kdiers

Kersten graduated from TU Dresden with a degree in Psychology and University of Heidelberg with a degree in Medical Biometry / Biostatistics. His work is at the intersection of applied methods development and empirical research, with a particular focus on shape analysis and statistical modeling of neuroimaging data.

Hippocampal Thickness Analysis, Shape Asymmetry, Harmonization, Quality Assessment, Bio-Statistics

cxc0366@mavs.uta.edu
Scholar Citations
HenryVarro666

Chao Cao is currently a second-year PhD student in the Department of Computer and Engineering at the University of Texas at Arlington. He received his B.E. degree from the Nanjing University of Posts and Telecommunications and his M.S. degree from the University of Maryland at College Park. His research focuses on medical imaging, brain disease diagnosis, and foundation model.

jxz7537@mavs.uta.edu
Scholar Citations

Jing focuses on the development of Multimodal Large Models for brain image analysis, with a specific focus on brain MRI and PET scans. Additionally, she is also interested in DTI-fMRI image analysis, dedicated to unraveling the complex relationships between brain function and structure through deep learning technologies. Her work employs advanced machine learning techniques aimed at facilitating the early detection of neurodegenerative diseases. Outside of the lab, she enjoys traveling, photography, and reading. If you also appreciate the literature of Ernest Hemingway and Raymond Chandler, she would be delighted to discuss them with you.

txc5603@mavs.uta.edu
0009-0000-2800-1476

Tong Chen is a second-year PhD student in the Department of Computer Science and Engineering. His research primarily focuses on neuroimaging, specifically on studying neurodegenerative diseases such as Alzheimer’s Disease and Lewy Body Dementia. His work involves applying advanced machine learning models and state-of-the-art techniques in neuroimage analysis to better understand the brain alterations associated with these conditions, aiming to improve diagnostic accuracy and differentiate between various forms of dementia.

mxc2442@mavs.uta.edu
0009-0009-3926-9289
Scholar Citations
m1nhengChen

Minheng Chen is a PhD student in the Department of Computer Science and Engineering at the University of Texas at Arlington. Prior to that, he was an undergraduate student in the School of Computer Science and Engineering at Southeast University. His research focuses on developing advanced computational methods for imaging-based brain biomarkers and brain disease diagnosis.

mxc2442@mavs.uta.edu
0009-0002-7029-3702
Scholar Citations
Lake233

Yan Zhuang is a PhD student in the Department of Computer Science and Engineering at the University of Texas at Arlington. He received his B.E. degree from the University of Electronic Science and Technology of China. His research interests include machine learning and deep learning algorithms, with a focus on neuroimaging and brain disease analysis.