Iavor Bojinov
About Iavor Bojinov
Iavor Bojinov is an Assistant Professor at Harvard Business School, specializing in causal inference, experimental design, and large-scale computing. He has a PhD in Statistics from Harvard University and has held various positions at notable companies including Google and LinkedIn.
Current Position at Harvard Business School
Iavor Bojinov serves as an Assistant Professor at Harvard Business School since 2019. In this role, he leads research initiatives that focus on the intersection of causal inference, experimental design, and large-scale computing. His work aims to democratize statistical methods to assist firms in their innovation and growth efforts. Bojinov's expertise in data-driven decision-making is integral to his teaching and research at the institution, contributing to the academic community's understanding of these critical areas.
Educational Background
Iavor Bojinov has an extensive educational background in mathematics and statistics. He studied at King's College London, where he earned an MSci in Mathematics from 2009 to 2013. He then attended the University of California, Berkeley, for a year in 2011-2012, focusing on Mathematics. Bojinov completed his Doctor of Philosophy (PhD) in Statistics at Harvard University from 2013 to 2018, where he also served as a Ph.D. candidate during this period.
Professional Experience
Bojinov has held various positions in the tech and academic sectors. He completed a network charging internship at RWE npower in 2011 for two months. In 2016, he worked as a Quantitative Analyst at Google in Mountain View for three months. He also served as a Statistician at LinkedIn in 2017 for three months. Following his PhD, he worked as a Data Scientist at LinkedIn from 2018 to 2019 in the San Francisco Bay Area. His diverse experiences contribute to his current research and teaching focus.
Research and Publications
Iavor Bojinov's research has been published in leading statistics journals, including Biometrika and the Journal of the American Statistical Association. His work emphasizes the value of experimentation and data-driven decisions in achieving competitive advantages for firms. He is involved in research related to digital transformation and has co-authored a case study on the digital transformation of Pernod Ricard, highlighting his commitment to applying statistical methods in real-world contexts.
Areas of Interest and Expertise
Bojinov's primary areas of interest include causal inference, experimental design, and the application of artificial intelligence in automating processes. He focuses on how statistical methods can be utilized to enhance innovation and growth within organizations. His expertise in data-driven decision-making positions him as a thought leader in the field, particularly in the context of digital transformation.