Research focuses on using genomic datasets from next-generation sequencing and array technologies in combination with clinical data to identify processes driving disease.
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Akshay’s primary research interest lies at the intersection of artificial intelligence and medical imaging. His group develops new techniques for accelerated MRI acquisition and downstream image analysis, extracting prognostic insights from already-acquired CT imaging. To enable these goals, his group develops new multi-modal deep learning algorithms for healthcare that leverage computer vision, natural language, and medical records, with a large emphasis on data-efficiency and model robustness.
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Our laboratory uses machine learning in various clinical settings to predict and improve patients’ outcomes. This includes integrative “multiomics” analysis across genomics, proteomics, metabolomics, and single-cell technologies, as well as quantitative clinical phenotyping using wearable devices.
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The Witte Lab is a computational and epidemiological research group investigating the genetic and environmental contributions to disease risk and progression. We develop and apply novel genetic epidemiological methods to decipher the mechanisms underlying complex diseases. Current areas of research include the following: 1) Evaluating disease risk with novel approaches to polygenic risk scores and hierarchical models; 2) Assessing the shared genetic basis across different diseases and traits (pleiotropy); and 3) Finding genetic risk factors, improving screening, and reducing disparities in cancer.
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