Pranjal Rawat
Hi, I'm Pranjal
I'm an incoming Applied Scientist at Uber working on algorithmic pricing in the Delivery Marketplace. I have a PhD in Econometrics and Quantitative Economics from Georgetown University and three years of full-time work experience in applied and data science (Amex, RecSys).
My research interests lie in ML-powered scalable methodologies for causal inference & experimentation, choice & preference modelling, reinforcement learning & dynamic programming, and optimization problems.
During my PhD, my advisors were John Rust and Nathan Miller and I also interned/worked with the following tech firms: Roblox, Uber and Topsort. Before my PhD, I worked as an Assistant Manager (Data Science) at American Express for 3 years on productionalizing marketing rec-sys and graph embedding systems. Before that, I worked on time series econometrics during my first Masters and at the Reserve Bank of India.
I serve as a reviewer for Control and Cybernetics.
Dynamic Discrete Choice and Inverse Reinforcement Learning
The monograph, its code and slides, and a package for dynamic choice estimation.
2. Two Cultures of Sequential Decision-Making
3. A Brief History of Reinforcement Learning
4. Reinforcement Learning Algorithms
6. Structural Estimation with Reinforcement Learning
7. Reinforcement Learning for Macroeconomic Models
8. Reinforcement Learning in Games
9. Bandits and Dynamic Pricing
12. Causal Inference for Reinforcement Learning
13. Reinforcement Learning for Causal Inference
14. Quantile, Robust and Constrained Reinforcement Learning
Appendix A. Mathematical Preliminaries
survey-of-reinforcement-learning-in-economics
Dynamic Discrete Choice and Inverse Reinforcement Learning
Notes on economics, machine learning, and whatever I'm reading. Subscribe →