I am a research faculty member in the Johns Hopkins Mathematical Institute for Data Science (MINDS) and Center for Imaging Science (CIS). My current research is broadly on developing theory and algorithms for processing high-dimensional data at the intersection of machine learning, optimization, and computer vision. In addition to basic research in data science I also work on a variety of applications in medicine, microscopy, and computational imaging.
PhD in Biomedical Engineering
Johns Hopkins University
BS in Electrical Engineering
Georgia Institute of Technology
Deep learning theory | Sparse and low-rank methods | Matrix/tensor factorizations | Subspace clustering | Generative models | Non-linear/manifold clustering | Physics constrained learning
Image/Video segmentation | Image classification | Compressed sensing | Object detection | Multi-object tracking
Non-convex optimization | Low-rank problems | Convex relaxation/lifting methods
Lens-free imaging | Holography | Two-photon imaging
Image analysis for microscopy | Hematology microscopy | Infection detection and monitoring | Digital pathology
A (very incomplete) selection of some of the topics I work on (past and present). More pages under construction.
Understanding the effects of Dropout regularization when training neural networks.