My research is focused on developing novel statistical machine learning techniques for systems-level characterization of the tumor microenvironment and the underlying regulatory mechanisms. Through integrating single-cell and multi-omics technologies with novel computational methods, I aim to infer dysregulated mechanisms driving cancer stem cells as well as the reprogramming of immune cells leading to immune dysfunction.
My PhD research was on developing integrative models for regulatory programs in microbial organisms, in particular Mycobacterium tuberculosis, the causative agent of tuberculosis, the fungus Neurospora crassa, a model organism for studying circadian rythyms, and Escherichia coli.
I worked on learning network models and developing statistical Bayesian inference, from integration of different data types to identify modular dependency structures and showed theoretical advantages of data integration.
At Microsoft Research, I worked on understanding regulation of the hematopoietic pathway using high-throughput data from Acute Myeloid Leukemia patients.