Artificial intelligence including deep learning and machine learning for healthcare, natural language processing (NLP) and image processing for healthcare, recommender systems for healthcare
Intersection of systems biology, bioinformatics, and artificial intelligence to understand how biological cells work and react to drugs and environmental signals
Our long-term research goal is to develop and apply innovative spatial, single cell and optical technologies that will transform our understanding of cellular communication in health and disease and use this insight to develop new treatments.
Systems and synthetic biology of the Brain-Immune-Gut super-system; Interactions among hosts and microbes; Deep learning approaches to interpreting biological data and designing biomedical solutions
Physical modeling of cancer progression, metastasis and interaction with the immune system. Most recent interests include the role of metabolic plasticity in these processes and the co-evolution of the tumor and the adaptive immune system. Other areas include spatial organization of the actin cytoskeleton, the mechanics of collective cell motility, and the analysis of genetic circuits involved in cell fate decisions.
Computational systems biology, an integration of mathematical modeling and bioinformatics for studying gene regulatory networks, single cell genomics, epithelial-mesenchymal transition, coarse-graining, reverse engineering, machine learning, stochasticity and heterogeneity in gene expression
Computational modeling of the cardiac myocyte to understand the molecular basis of arrhythmias; machine learning in critical care medicine to identify those patients who require urgent care