In the face of ever escalating complexity in medicine, integrating informatics solutions is the only credible approach to systematically address challenges in healthcare. Tapping into real-world clinical data streams like electronic medical records with machine learning and data analytics will reveal the community’s latent knowledge in a reproducible form. Delivering this back to clinicians, patients, and healthcare systems as clinical decision support will uniquely close the loop on a continuously learning health system. My group seeks to empower individuals with the collective experience of the many, combining human and artificial intelligence approaches to medicine that will deliver better care than what either can do alone.
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.
Research Areas:
- Machine Learning
- Medical Imaging
- Informatics
- Computer Vision
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.
Research Areas:
- Systems Biology
- Machine Learning
- Artificial Intelligence
- Multiomics Profiling
- Single Cell Biology
- Electronic Health Records
- Wearable Devices
- Maternal and Child Health
- Perioperative Care
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.
Research Areas:
- Bioinformatics
- Statistical Genetics
- Cancer Risk Prediction
- Genetic Epidemiology
- Epidemiology Methods




