About Me:

I’m a researcher who is actively looking to pursue a career in academia by specializing in dynamics and controls, while contributing to applications spanning engineering, biology, and medicine. I would like to use the knowledge I’ve gained in the field of dynamics and controls, and apply it to biological and mechanical systems. By expanding my knowledge base, I hope to understand the dynamics of naturally occurring systems at a fundamental level. In the process, I hope to contribute to improve the quality of human life through research.

 

I am working as a postdoctoral researcher 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".

 

The past decade of my academic life exposed me to principles of modeling and control theory in the fields of aerospace and biomedical engineering. The former encompassed developing a control-oriented modeling framework for flexible air-breathing hypersonic vehicles, while the latter involved data-driven modeling of patient-specific responses to therapy using bone marrow specimens from multiple myeloma patients. Air-breathing hypersonic vehicles are subjected to extreme aero-thermal loads that result in flexing of the airframe, which lead to control design challenges. My work involved identifying such challenges early-on in the conceptual design phase to reduce downstream costs. During my postdoctoral research, I developed a mathematical framework called EMMA (Ex vivo Mathematical Malignancy Advisor) that relies on patient-specific response measurements in an ex vivo reconstruction of the human bone marrow. EMMA computes patient-specific model parameters that are coupled with pharmacokinetic data from phase-I clinical trials to simulate a patient’s response to monotherapy. However, most multiple myeloma therapies involve combinations. In order to facilitate computing clinical predictions for combinations, I developed a mathematical model called SAM (Synergy Augmented Model) that captures two-way pharmacodynamic interaction between pairs of drugs. EMMA-SAM framework simulates patient-specific response for upto 31 drugs within five days post biopsy to help inform clinical decisions. The dichotomy of modeling approaches, physics-based and data-driven, in high-speed transportation systems and biomedical systems exposed me to numerous deterministic and stochastic modeling techniques.