Data-driven modeling of scientific migration

Day - Time: 03 April 2017, h.16:00
Place: Area della Ricerca CNR di Pisa - Room: C-29
  • Charlotte James (University of Bristol, UK)

Fosca Giannotti


In the academic community there is a widely accepted belief that movement between institutions is beneficial to, possibly even essential for, a successful career. From doctorate to post-doc, lecturer to professor, many individuals relocate at some point in their career. Despite its common occurrence, it remains unclear how a researcher looking to relocate selects their next institution and at which point in time they decide to make this move.

The goal of this project is construct a mathematical model to describe the migration of researchers between institutions. The model may be used to determine both the probability that a researcher will migrate (i.e., change institution) and the probability to relocate to a given institution (i.e., the possible destinations).

Using the APS publication database which consists of over 400,000 papers, we aim to reconstruct the career trajectories of scientists to determine the driving forces behind their decisions to change institutions. The driving forces we will consider include relative performance of both the researcher and the institutions and researcher's collaboration networks, amongst others. We will apply methods originating from machine learning in order to determine which factors are most influential in a researcher's decision to relocate.

We hope that the insight gained from this work will provides us with a deeper understanding of the factors that influence the migration decisions of researchers, along side a general modelling approach to describe migration dynamics.