Emerging deep learning methods enable the integration and analysis of these complex data in order to address real-world problems by designing and discovering successful solutions.
In this project, we aim to boost the predictive power of content-based models by incorporating the distribution of scientists and innovators such that a candidate of discovery will be marked as likely to occur in reality only if there is enough population of researchers studying it.
In this project, we argue that with the fast proliferation of digital data in scientific metadata, AI predictors of scientific discoveries can do much more than just imitating their human counterparts.
In this project, we ask how the amazing capacities of modern science and technology to generate long-lived, highly transformative ideas, are built up from the cognitive and physical capacities that are our shared evolutionary heritage.
This project is developing novel, innovative tools spanning network science, machine learning, natural language processing, and computational social science to yield new insights and answers.
Data-driven models are increasingly used to simulate and make predictions about complex systems, from online shopping preferences and the performance of the stock market to the spread of disease and political unrest.
Science is a complex system. Building on Latour’s actor network theory, we model published science as a dynamic hypergraph and explore how this fabric provides a substrate for future scientific discovery.
This paper examines the structure of linguistic predications in English text. Identified by the copular “is-a” form, predications assert category membership (hypernymy) or equivalence (synonymy) between two words.
Automatically identifying related specialist terms is a difficult and important task required to understand the structure of less prominent portions of the lexicon.
Here, we use information theory to measure cultural holes, and demonstrate our formalism in the context of scientific communication using papers from JSTOR .
Most studies on global health inequality consider unequal health care and socio-economic conditions but neglect inequality in the production of health knowledge relevant to addressing disease burden. We demonstrate this inequality and identify likely causes.