Victor Chernozhukov (Виктор Викторович Черножуков) is a Russian-American statistician and economist currently at Massachusetts Institute of Technology.
His current research focuses on mathematical statistics and machine learning for causal structural models in high-dimensional environments.
He delivered the invited Cowles (2009, inaugural), Fisher-Shultz (2019), Hannan (2016), and Sargan (2017) lectures at the Econometric Society Meetings.
Chernozhukov's presentations were primarily based on several mathematical and econometric concepts, such as Uniform Post Selection Inference, Z-Estimation, Treatment Effects, High-Dimensional Data, Central Limit Theorems, and Gaussian Approximations among others.
Chernozhukov has published papers covering 11 Major themes including Central Limit Theorems and Bootstrap with p>>n, Big Data: Post-Selection Inference for Causal Effects, Big Data: Prediction Methods, High-Dimensional Models, Policy Analysis, Shape Restrictions, Partial Identification and Inference on Sets, Laplacian and Bayesian Inference, Quantiles and Multivariate Quantiles, Endogeneity, and Extremes and Non-Regular Models.