My research explores how interconnectedness and organizations shapes the scientific, artistic and business world around us. I employ a highly multidisciplinary approach—combining tools and techniques from Computational Social Science, Data Science, and Network Science with theory from Sociology. My current focus is in the Science of Science where my group analyzes and models how organizational structure and strategic decisions impact innovation, creativity, and success.
Before arriving at UVA, I received a joint Ph.D. degree in Informatics (focusing on complex networks) and Cognitive Science from Indiana University, Bloomington, an MSc from King’s College London in complex systems modeling and a BA in mathematics from Cornell University.
[05/08/24] Gates organized the inagural Data Is Art competition at UVA. Congratulations to Julia Daser and Pepi Ng for their winning animatronic data visualization!
[04/24/24] New paper Mapping Philanthropic Support of Science published in Scientific Reports.
[02/22/24] New paper Translating interdisciplinary knowledge for gender equity: Quantifying the impact of NSF ADVANCE published in Social Science Quarterly.
[02/16/24] Invited presentation on The increasing fragmentation of global science limits the diffusion of ideas at SDS-Darden Collaboratory for Applied Data Science in Business.
[11/28/23] New paper A network-based normalized impact measure reveals successful periods of scientific discovery across disciplines featured on the cover of PNAS.
[10/30/23] New paper Quantifying Hierarchy and Prestige in US Ballet Academies as Social Predictors of Career Success published in Scientific Reports.
[09/27/23] Invited presentation on Beyond Core-Periphery: Uncovering the Impacts of Scientific Networks on Resources and Recognition at Northwester Institute on Complex Systems.
[08/01/23] Welcome Jianjian Gao as a new Postdoc in our lab!
[05/31/23] Congratulations Dr. Yessica Herrera-Guzman who successfully defended her disseration with highest honors!
[03/08/23] New paper Reproducible Science of Science at scale: pySciSci published in Quantitative Science Studies.
In the recent decade, we have seen major progress in quantifying the behaviors and the impact of scientists, resulting in a quantitative toolset capable of monitoring and predicting the career patterns of the profession. It is unclear, however, if this toolset applies to other creative domains beyond the sciences. In particular, while performance in the arts has long been difficult to quantify objectively, research suggests that professional networks and prestige of affiliations play a similar role to those observed in science, hence they can reveal patterns underlying successful careers. To test this hypothesis, here we focus on ballet, as it allows us to investigate in a quantitative fashion the interplay of individual performance, institutional prestige, and network effects. We analyze data on competition outcomes from 6363 ballet students affiliated with 1603 schools in the United States, who participated in the Youth America Grand Prix (YAGP) between 2000 and 2021. Through multiple logit models and matching experiments, we provide evidence that schools’ strategic network position bridging between communities captures social prestige and predicts the placement of students into jobs in ballet companies. This work reveals the importance of institutional prestige on career success in ballet and showcases the potential of network science approaches to provide quantitative viewpoints for the professional development of careers beyond science.
The impact of a scientific publication is often measured by the number of citations it receives from the scientific community. However, citation count is susceptible to well-documented variations in citation practices across time and discipline, limiting our ability to compare different scientific achievements. Previous efforts to account for citation variations often rely on a priori discipline labels of papers, assuming that all papers in a discipline are identical in their subject matter. Here, we propose a network-based methodology to quantify the impact of an article by comparing it with locally comparable research, thereby eliminating the discipline label requirement. We show that the developed measure is not susceptible to discipline bias and follows a universal distribution for all articles published in different years, offering an unbiased indicator for impact across time and discipline. We then use the indicator to identify science-wide high impact research in the past half century and quantify its temporal production dynamics across disciplines, helping us identifying breakthroughs from diverse, smaller disciplines, such as geosciences, radiology, and optics, as opposed to citation-rich biomedical sciences. Our work provides insights into the evolution of science and paves a way for fair comparisons of the impact of diverse contributions across many fields.
Quantifying how the NSF ADVANCE program potentially facilitated the exchange of expertise and knowledge about gender equity and organizational transformation.
pySciSci: A python package for the science of science.
Quantifying systemic gender and nationality inequality in science and art.
CluSim: a package for calculating clustering similarity.
A deep dive into clustering similarity.
Quantifying canalization & control in complex dynamical systems.
CANAlization: Control & Redundancy in Boolean Networks.
Emergent individuals as organizations.
Quantifying how the NSF ADVANCE program potentially facilitated the exchange of expertise and knowledge about gender equity and organizational transformation.
pySciSci: A python package for the science of science.
Quantifying systemic gender and nationality inequality in science and art.
CluSim: a package for calculating clustering similarity.
A deep dive into clustering similarity.
Quantifying canalization & control in complex dynamical systems.
CANAlization: Control & Redundancy in Boolean Networks.
Emergent individuals as organizations.