Applied ML practitioner and technical leader with experience building production real-time ML systems and conducting academic research in applied mathematics, scientific machine learning, and fluid dynamics.

Leadership

At The New York Times, I lead a team of ML scientists developing and deploying real-time ML models for subscription-related problems, such as serving a paywall at optimal moments or personalizing user-facing messages. Our production models serve millions of users and generate tens of millions of dollars in revenue, using causal ML, contextual bandits and personalization.

Applied ML

As an IC and tech lead, I developed and deployed real-time causal ML models for multi-objective optimization of The Times paywall (see blog post). I also built large-scale production batch models that determined user-level access to our content (see blog post, PyData talk, VentureBeat article). The production systems were built in Python and Go for high-throughput inference on cloud-based containerized infrastructure. I have also worked on causal inference using statistical matching and sequential trial emulation, and designed RCTs for causal ML model training.

Research

My Ph.D. thesis advised by Prof. Jörn Dunkel (Applied Mathematics), combined scientific ML, numerical computation, and fluid dynamics. I built a computational inference framework to learn PDEs from microscopic data of active matter (see PNAS article) and contributed to early demonstrations of Neural Differential Equations (see preprint). I was a recipient of the Mathworks Engineering Fellowship. Master’s at MIT on the fluid dynamics of internal waves. B.Tech at IIT Madras in theoretical fluid dynamics; also built an omni-directional robot for ABU Robocon (that’s me driving!).

Outside of work, I enjoy long-distance running in the summer and skiing in the winter.

See my CV for more details.

Skills

Machine Learning

Supervised and unsupervised machine learning, Contextual bandits, Bayesian methods (Thompson Sampling), Causal Inference, Causal Machine Learning, Neural Differential Equations, Scientific Machine Learning

Mathematical Modeling

Differential Equations, (Fluid) Dynamical Systems, Optimization, Linear Algebra, Numerical Methods for PDEs

Technologies

Programming

Machine Learning

ML Deployment