Bio:

Rohit Supekar is a Senior Data Scientist at The New York Times. He develops and deploys real-time machine learning models for subscription-related problems, such as serving a paywall at optimal moments or personalizing messages to drive subscriptions. These models are often constrained and multi-objective in nature, sitting atop causal machine learning and contextual bandits. As a part of his full-stack role, he builds production scale applications from ML models to support high throughput inference. His work has been awarded a silver medal in the INMA Global Media Awards of 2023 and has been covered by VentureBeat, a popular technology news website.

Prior to The NYT, Rohit obtained a Ph.D. from MIT in 2021, specializing in scientific machine learning, mathematical modeling of active fluids, and numerical computation. He built a computational inference framework to learn continuum models from microscopic data to predict collective phenomena in systems such as bacterial suspensions. During an internship at Amazon, he worked on Debiased Machine Learning to estimate the causal impact of Amazon’s advertising products.

Outside of work, Rohit enjoys long-distance running in the summer and skiing in the winter.