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Katherine Quinn (Georgetown University)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited Speaker / Conférencier(ère) invité(e)
Understanding the innovation landscape, and tracing how novel or emerging ideas become applied elsewhere in science or in a technological innovation that is then commercialized, is a complex challenge. We gather hundreds of millions of scientific articles and patents from 197 countries and in 165 languages, extract 6 billion links connecting them via citations and text similarity, and use the...
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Prof. Andrew Rutenberg (Dalhousie University)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited Speaker / Conférencier(ère) invité(e)
High-dimensional health data is measured regularly for large study populations. We do physics with this big data, with a particular focus on the complex dynamics of human aging. While we started with flexible deep-learning approaches to predict future health, we have used them to identify simpler stochastic dynamical models within interpretable latent spaces. I will tell you about our methods,...
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Rachael Mansbach (Concordia University)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited Speaker / Conférencier(ère) invité(e)
Peptides are short biomolecules with numerous desirable properties for biomaterials design including multifunctionality and biocompatibility. Over the past decade, there has been an explosion in the use of generative deep learning models for design of general de novo molecular design, including peptides; however, analysis of generative models and design spaces remains an open area of research....
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