Oct 2022 – Present · 3 yrs 5 mos · Bengaluru, Karnataka, India

Jan 2020 – Apr 2020 · 3 mos

• • Conducted recitations and office hours for the introductory Fluid Mechanics course, 1.060 taught at MIT.

Dec 2019 – Jun 2022 · 2 yrs 6 mos

• Machine learning aided Covid-19 model: Developed a novel data driven epidemiological model aided with machine learning to estimate Covid-19 spread and role of quarantine policies implemented in more than 75 countries globally.
• Deep Learning Safe Blues: Designed a deep neural network module to simulate virtual virus spread, that can be trained to estimate the real time spread of Covid-19 and also simulate the impact of several lockdown policies. (safeblues.org)
• Probabilistic Machine Learning: The goal of this project is to combine probabilistic programming languages with Neural ODE frameworks to quantify uncertainty in the Neural ODE estimation. This project has offshoots to error quantification in data driven discovery of equations governing dynamical systems.

Jun 2017 – Dec 2019 · 2 yrs 6 mos

• Researcher at the Fluid Dynamics of Disease Transmission Laboratory.
• Designed the experimental setup and developed robust physical models to study the century old classical fluid mechanics problem: splash resulting from the impact of a droplet on a liquid pool.
• Elucidated the mechanism governing contaminant dispersal from the splash in the nosocomial,
• agricultural and healthcare sectors.
• Assisted projects like bubble fragmentation dynanmics and rheological study of biological fluids.

May 2016 – Aug 2016 · 3 mos

• Envisioned and designed a robust roadmap for the solar panel installation of 100 KW capacity at the
• factory location leading to a potential saving of 0.2 million dollars over the lifetime of the solar panels.

May 2015 – Jun 2017 · 2 yrs 1 mo

• Designed the experimental setup and performed flow measurements using high speed cameras(800 fps) integrated with the LaVision DaViS computer software.
• Analyzed more than 10,000 images to detect patterns in data and develop an experimental power law for the radial growth of the circular front so formed.