Regular biography
Vira Semenova is an Assistant Professor in the Department of Economics at the University of California, Berkeley. Her research focuses on econometrics, with an emphasis on econometrics and machine learning. Semenova holds a PhD from MIT, awarded in 2018. She is affiliated with the UC Berkeley Economics department and maintains a profile page at https://www.econ.berkeley.edu/profile/vira-semenova. Her teaching and research activities are aligned with the department's academic goals in econometrics.
Scholar-generated biography
Vira Semenova is a researcher at the University of California, Berkeley, specializing in Artificial Intelligence, Econometrics, Machine Learning, and Statistics. Her work focuses on developing statistical methods for causal inference, particularly in the context of treatment effects and policy learning. She has contributed to the development of debiased machine learning techniques for estimating conditional average treatment effects and other causal functions. Her research also includes inference on heterogeneous treatment effects in dynamic panels and welfare analysis in dynamic models. Semenova's publications highlight her expertise in combining econometric theory with machine learning to address complex policy and economic questions.