My career goal is to contribute to making personalized medicine a reality in the clinic, where the choice of therapy for each patient would be informed by their molecular and pathological features, known as predictive biomarkers. Identifying such biomarkers would require matching a cancer patient’s response to a given therapy with the patient’s molecular features. Most anti-cancer therapies involve two-to-four drug combinations that may confound the molecular features associated with favorable outcomes to each of the constituent drugs. As a postdoctoral fellow at Moffitt, I contributed to the development of a bench-to-bedside computational framework that identifies predictive biomarkers for several standard-of-care and experimental drugs to treat Multiple Myeloma (MM), an incurable but treatable bone marrow resident plasma cell malignancy. There are five main components to this framework:
Estimating each MM patient’s tumor sensitivity to individual drugs and combinations
Using paired molecular features (disease state, gene expression, mutational and cytogenetic statuses) and patient tumor sensitivity to identify molecular signatures associated with improved drug response
Validating these molecular signatures using real-world clinical data such as Electronic Health Records (EHR)
Identifying drug pairs with complementary predictive biomarkers as candidates for sequential therapy
Discovering novel combination therapies to reverse or overcome resistance to standard-of-care drugs.
I am a research instructor in the department of cancer physiology at H. Lee Moffitt Cancer Center. I received my doctoral degree from Virginia Tech in aerospace engineering, on the topic “Control-Oriented Modeling of an Air-breathing Hypersonic Vehicle under Extreme Aero-thermal Loads ”. I have a masters degree from Indian Institute of Technology Kanpur in flight mechanics from the department of aerospace engineering with a focus on "Optimal Aero Assisted Orbital Transfer with Predictive Time-linear Control and Adaptation".
Over the past decade of my academic life, I’ve gained exposure in the fields of computational and data sciences through the lens of electrical engineering as an undergraduate student, aerospace engineering as a graduate student, and biomedical sciences as an applied postdoctoral fellow and research scientist. As diverse as these fields maybe, they rely on using computational methods and real-world data to deepen our understanding of naturally occurring phenomena. This diverse academic background has helped me hone my skills in scientific problem solving and developing novel methodological approaches to solve complex problems in cancer research. In the process, I hope to contribute to improve the quality of human life through research.