TOM, Harvard Business School, and
Affiliate Faculty of Department of Statistics
I am an assistant professor of business administration in the Technology and Operations Management unit at Harvard Business School. I am currently teaching the first-year Technology and Operations Management course.
My research interest is at the interface of causal inference, experimental design, and large-scale computing with the overall goal of democratizing statistical methods to help firms innovate and grow. I am particularly interested in understanding the value brought by experimentation and data-driven decisions in achieving a competitive advantage.
Before joining Harvard Business School, I worked as a data scientist leading the causal inference effort within the Applied Research Group at LinkedIn. I have a Ph.D. and an MA in Statistics from Harvard and an MSci in Mathematics from King’s College London.
Bojinov, I., Sait-Jacques, G., and Tingley, M. (2020).
Harvard Business Review 98, no. 2,March–April.
Bojinov, I., Pillai, N., and Rubin, D. (2020).
Biometrika, Volume 107, Issue 1, March 2020, Pages 246–253.
Bojinov, I. and Shephard, N., (2019).
Journal of the American Statistical Association, pp.1-36.
Hollenbach F., Bojinov I., Minhas S., Metternich N., Ward M., and Volfovsky A. (2018).
Sociological Methods & Research p.0049124118799381.
Bojinov I., and Bornn L. (2016).
Procedings of MIT Sloan Sports Analytics.
Bojinov, I., Rambachan, A., & Shephard, N. (2020).
Wu A., Airoldi E., Basse, G., and Bojinov I. (2020+).
The potential bias from ignoring neighborhood covariates in observational studies on networks.
Bojinov I. (2020+).
Diagnostics tools for missing data: A guide through the jungle.
Bojinov I., Tu I., Liu M., and Xu, Y. (2018).