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Multimodal Data Analytics

The Multimodal Data Analytics Group leverages expertise in large-scale biomedical informatics and statistical genetics to build and use tools for healthcare needs and creates scalable AI and machine-learning solutions for multidimensional, multimodal data in high-performance computing environments applied to biomedicine and bioengineering.

Examples include privacy and biomedical informatics for supervised and unsupervised learning with healthcare data, information extraction, medical imaging, and new outcomes.

To see a listing of available jobs, please click [HERE].


Section Head, Advanced Computing Methods for Health Sciences
Anuj J. Kapadia, PhD.