Skip to main content

University of Michigan School of Information

Misha Teplitsky

Misha Teplitskiy

Assistant Professor of Information, School of Information Email: Phone: 734/764-5876
Office: School of Information/105 S State St Faculty Role: Faculty Potential PhD Faculty Advisor: Yes Personal website


My research is at the intersection of Science of Science and Sociology of Organizations. I study how social and organizational factors affect scientific discovery. I am especially interested in evaluation practices in science, and whether they promote or stifle innovation. My approach relies on field experiments with scientists as they conduct their work, and applying computational tools to large-scale observational data.

Current research projects include

  • Metrics in science and performativity: Do things like citation counts and impact factors proxy quality and influence, or help create them?
  • Social influence among experts: How do experts influence one another's opinions, i.e. during peer review of grant applications?


Areas of interest

Science of science, collective intelligence, computational social science, experiments


Ph.D. in Sociology, University of Chicago

B.S. in Physics and Mathematics, Rice University


"Quoted by PNAS, Assistant professor Misha Teplitskiy, Wealthy nations rake in the citations while poorer countries go under-acknowledged." Headshot of Misha Teplitskiy
Teplitskiy: Geography can influence research citations, but journal quality also matters

Misha Teplitskiy, assistant professor and expert on science metrics, was quoted by PNAS’s Journal Club about new research on research citations. Are scientists from poorer countries really cited less often?

More Info
Headshot of Misha Teplitskiy in a award ribbon. "UMSI logo, Michigan Institute for Data Science, MIDAS Reproducibility Challenge, Misha Teplitskiy, Assistant Professor."
Misha Teplitskiy wins reproducibility challenge for data science research

Misha Teplitskiy receives a MIDAS Reproducibility Challenge award for his research on how to make data science projects more reproducible and research outcomes more robust.

More Info