New York University (Current)

  • Machine Learning for Economists

This is a graduate course in the exciting and growing literature on machine learning. We will cover theory, methods, and models: VC Analysis, bias-variance tradeoff, regression and classification models, regularization, validation, neural networks, support vector machines, unsupervised learning, and Bayesian learning. R is the preferred programming language. Lectures are based on materials from books and journal articles including Hastie, et. al., and Varian.

  • Advanced Econometrics: Causal Inference

Uncovering causal relationships is of great importance in a wide array of fields spanning public policy, E-commerce, business research, sciences and medicine. In this graduate-level course we will study several widely used techniques that have revolutionized how data is used by applied economists to estimate causal relationships: randomized control trials, matching and propensity score, differences-in-differences, and regression discontinuity. The exciting and growing literature on machine learning has made its way into causal inference. We will study this interface, particularly the role of machine learning models and methods in estimation of counterfactuals. Lectures will be based on materials from books and journal articles including Angrist and Pischke, James, et. al., Imbens, G. W., and J. M. Wooldridge. R is the preferred programming language.

  • Advanced Econometrics: Applied Microeconometrics

This is a graduate course that focuses on econometric methods and models used in empirical microeconomics research. The course material is divided into two sections: Methods and Models. The Methods section covers topics relating to estimation methodology including generalized method of moments, maximum likelihood, quantile regressions, Bayesian MCMC methods, testing and bootstrap methods. The second section covers models including dynamic panel data, discrete choice, hazard and count data models. The emphasis of the course is on how and when to apply the models. The text book for the course is Cameron and Trivedi and R is used extensively.

  • Applied Statistics and Econometrics II

This course is the second part of a two-semester first-year graduate sequence designed to teach applied statistics and econometric techniques for quantitative research and analysis. The course material is divided into five sections. The first section covers estimation methods including Generalized Least Squares, Maximum Likelihood and an introduction to Generalized Method of Moments. The second section covers topics in Micro-econometrics. The third section focuses on causal inference. The fourth section focuses on topics in Macro-econometrics. The last section covers machine learning as it pertains to prediction in economics. The textbook for the course is Econometric Analysis, 8th edition, by William H. Greene, Prentice Hall (2018). James et. al. will be used for the section on machine learning. R is used for lab sessions and homework assignments assignments.

Duke University

    • Investments

In this graduate course we will analyze the structure and workings of the financial markets, as well as developing tools for pricing and assessing the risk of a broad range of financial products. While we will apply some relatively technical and theoretical tools, we will try to maintain at all times a practical approach to finance. The goal of the class is to give students a solid foundation on how financial assets are priced in the marketplace, and what questions investors should ask (both of themselves and their financial advisors) prior to making investment decisions. The textbook for the course is Investments by Bodie,Z., Kane, A., and Alan Marcus.

  • Fixed Income Microstructure

This is a graduate course on the industrial organization of the government securities market. It covers the workings of the markets (primary, secondary, when-issued, repo and futures) connected with sovereign debt, and the nuts and bolts of designing a system to sell debt instruments. The demand-side of this market will be explored and causal links between the issuance practices and the sources of demand will be explored with a view to rationalizing the different government debt issuance practices observed around the world. Lectures will be based on journal articles, unpublished research, and class notes. Students are encouraged to familiarize themselves with Market Microstructure. Excellent references for the same are surveys by surveys by Daniel F. Spulber and Biais, B., Glosten, L., C. Spatt


University of Toronto

  • Teaching assistant for undergraduate and graduate econometrics

  • Teaching assistant for undergraduate statistics


University of Delhi, India

  • Tenured Associate Professor of Economics at Shri Ram College of Commerce: courses taught include undergraduate microeconomics, mathematical economics and statistics