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Research from Dr. Shah's lab creates novel intersections between deep learning, engineering, and medicine to discover novel biological and clinical knowledge to diagnose and treat cancer and infectious diseases. 

 Dr. Shah leads research projects that  build an unified scaffolding of novel deep learning, biological and statistical reasoning methods to resolve distinct hypothesis driven research problems including, for example, how to diagnose and treat cancer disease, how to leverage causal structures in big observational data for unbiased patient centered medicine, and how infectious microbes and host cells adapt during disease – under a single theoretical and methodological framework. The lab's work and findings will establish a new theory and practice for computational medicine research by moving the field from a static snapshot of biomedical research to a fully dynamic and living-systems wide perspective of molecular and clinical processes that are supported by meaningful and equitable medical technology for improving health and managing diseases.

Key goals are:

  • Novel medical technologies for translational clinical and biomedical research and real world impact
  • Augmenting artificial intelligence, machine learning, medical imaging and neural network capabilities for personalized digital medicines and improving health outcomes
  • Empowering patients, physicians, researchers, and regulators for making informed and equitable healthcare decisions