Research Summary


I study the relationship between fame, creativity and social networks.  Fame is crucial resource as well as performance metric  for a range of actors such as artists, entrepreneurs,  politicians, scientist and social activists. Yet, we know little about the drivers of fame. I conceptualize fame as the extent of attention an innovator receives in public discourse.

In one project, I use a machine learning algorithm (neural nets) to develop a novelty measure of 7000 paintings created by the innovators of the early 20th century modern art. Using this measure, I find that novelty hurts an innovator’s fame. In a related project, I use expert as well as the machine learning based measure of the paintings, to demonstrate that fame of an innovator is associated with the compositional diversity of their local network and not their individual creativity. My findings imply that among the pioneers of a paradigm shift, the more conventional producers are more likely to be recognized as pioneers. Moreover, my findings enrich the existing atomistic view of fame by providing evidence for a structural model of fame. In a related stream of research, I examine the relationship between two key forms of social capital – peer endorsements and fame in the context the jazz industry.

Overall, my work combines literature in organizational theory, sociology, psychology and entrepreneurship with methods from social network analysis and computational social science to understand the determinants and consequences of the social capital and creativity of innovators.

I completed my Ph.D. in Management from Columbia Business School. I graduated from the University of Rochester with a double major in Mathematics (BS) and Economics (BA) (Summa Cum Laude, Phi Beta Kappa).  Before embarking on my PhD, I worked in investment banking and was a research associate in the Strategy division at Harvard Business School.