My research program comprises two distinct but related streams. The first research stream focuses on developing highly applied, multiscale mechanistic models of complex biomedical phenomena. These models are motivated by fundamental questions such as understanding the underlying mechanisms driving disease progression, and treatment optimization. A particular emphasis area for me is modeling cancer onset, progression and response to treatment, and biomarker evaluation. More recently, I have started investigating the link between infectious diseases and climate change, and also how heterogeneity at every level and across scales can impact the outcomes of planned interventions.

My second research stream focuses on more theoretical aspects of these models. Complex biology gives rise to complex mathematical models that are rich in dynamical behavior, and part of my research is studying questions of existence and uniqueness, and of asymptotic behavior of the solutions to equations arising from my models. I have also been investigating questions of parameter identifiability and estimability arising from imperfect data. Such issues permeate virtually all models that are data-driven such as in mathematical oncology, and are increasingly being recognized a vital component of the modeling exercise. Most recently, I have been developing quantitative frameworks–SMoRe ParS and SMoRe GloS–for connecting computationally complex mathematical models with noisy, sparse real-world data, and performing global sensitivity analys.