Misha Teplitskiy
About Misha Teplitskiy
Misha Teplitskiy is a Postdoctoral Fellow at Harvard Business School and an Assistant Professor at the University of Michigan, focusing on the intersection of evaluation practices in science and innovation. He applies computational tools to analyze large-scale observational data and conducts field experiments to study scientific discovery processes.
Work at Harvard Business School
Misha Teplitskiy has been a Postdoctoral Fellow at the Laboratory for Innovation Science at Harvard Business School since 2016. In this role, he applies computational tools to analyze large-scale observational data, contributing to research that intersects innovation and science. His work in this position spans over eight years, during which he has engaged in various projects aimed at understanding the dynamics of innovation within scientific contexts.
Current Position at University of Michigan
Since 2019, Misha Teplitskiy has served as an Assistant Professor at the University of Michigan. In this capacity, he focuses on the impact of evaluation practices in science on innovation. His research aims to uncover how different assessment methods influence scientific progress and the development of new ideas, further bridging the gap between scientific evaluation and innovation.
Education and Expertise
Misha Teplitskiy earned his PhD in Sociology from the University of Chicago, where he studied from 2008 to 2015. His academic background also includes a Bachelor of Science in Physics and Mathematics from Rice University, completed from 2003 to 2007. This diverse educational foundation equips him with a unique perspective on the intersection of science, sociology, and innovation.
Research Methodologies
Teplitskiy utilizes field experiments with scientists to study scientific discovery processes. His research methodologies involve applying computational tools to analyze observational data, allowing for a comprehensive understanding of how scientific practices influence innovation outcomes. This approach highlights the importance of empirical data in evaluating the effectiveness of various scientific evaluation practices.