Stefano Allesina (UChicago) - What’s in a Name? List of Names Reveal Differences in Academic SystemsReturn to Full Calendar
- May 17, 2019 at 12:00pm - 1:00pm
- JCL, Rm. 390
- Event Audience:
Speaker: Stefano Allesina Professor, Department of Ecology & Evolution, University of Chicago
My laboratory develops new mathematical, statistical and computational methods for the analysis of ecological data. Common themes in the laboratory are:
The dynamics of large ecological systems
We all know that there are millions of species on Earth. Yet, most ecological models consider small systems composed of only a handful of populations. We use methods from Random Matrix Theory and Spectral Graph Theory to garner ideas on the dynamics of large ecological systems.
Building food webs from scratch
What are the main drivers responsible for the shape of ecological networks? We investigate this question through the use of simple models for building food webs (or other ecological networks) whose shape resembles that of empirical networks. To do so, we rely heavily on statistics, with an emphasis on the problem of model selection.
Response to extinctions
Every day, dozens of species go extinct. Are these extinctions going to trigger "extinction cascades" in which the loss of a primary species results in an avalanche of "secondary" extinctions? We use mathematical and statisitcal models to address this question.
Science of Science
Can we study the scientific endeavor using the same tools we develop for other complex systems? How shall we evaluate the impact of researchers? How shall we fix the peer-review algorithm?
Abstract: What’s in a Name? List of Names Reveal Differences in Academic Systems
Thanks to large, well-organized databases of dissertations, publications, and citations, we can study the scientific endeavor using the same quantitative tools that have advanced other areas of science. I present two projects where I start from a list of names of academics and probe several aspects of academic systems around the world. First, using lists of names of all the professors in Italy, at the CNRS in France, and in R1 public institutions in the US, I show how these data can reveal patterns of mobility, gender imbalance and nepotism. Second, using lists of names of PhD recipients in several countries and across 20 years, I show the differential academic attrition by country, gender, discipline, and prestige of the institution. These results have important policy implications, with the goal of creating a more diverse and talented scientific workforce
Sponsor: Center for Data and Computing