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.
🚨 I am have an open hire for a postdoctoral research scholar!
Email me to learn more! 🚨
Many biological networks are modeled with multivariate discrete dynamical systems. Current theory suggests that the network of interactions captures salient features of system dynamics, but it misses a key aspect of these networks: some interactions are more important than others due to dynamical redundancy and nonlinearity. This unequivalence leads to a canalized dynamics that differs from constraints inferred from network structure alone. To capture the redundancy present in biochemical regulatory and signaling interactions, we present the effective graph, an experimentally validated mathematical framework that synthesizes both structure and dynamics in a weighted graph representation of discrete multivariate systems. Our results demonstrate the ubiquity of redundancy in biology and provide a tool to increase causal explainability and control of biochemical systems.
By studying the publication careers of over 1.5 million scientists, we take a career-focused persepective to identify the factors inhibiting gender equity in STEM. We find that despite an improved trend towards population equality, the gender differences in career-wise productivity and impact have been growing over the last 70 years. Yet, the research outcomes of men and women year to year are essentially equivalent. Third, and most importantly, women are ending their publishing careers at higher rates than men, and this is happening across every stage of their careers.
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.