My research explores how interconnectedness shapes the social, scientific, 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.
joint Ph.D. in Informatics (Complex Systems & Networks) and Cognitive Science, 2017
Indiana University, Bloomington
M.Sc. in Mathematical Modelling for Complex System, 2012
King's College London
BA in Mathematics & Physics, 2009
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.