Research Areas:
- Meta-Research
- Biostatistics
- Bioinformatics
- Evidence-Based Medicine
Research focuses on using genomic datasets from next-generation sequencing and array technologies in combination with clinical data to identify processes driving disease.
Research Areas:
- Bioinformatics
- Computational Biology
- Systems Biology
- Machine Learning
- Cancer
- Neurodegenerative Disease
- Genomics
- Proteomics
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