World Model · podcast knowledge graph

NLP for Mapping Physics Research with Matteo Chinazzi - #353

2020-03-02 · 35 min · episode 353 · 14 entities

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Asserted relationships

Entities found in this episode

concepts 6

  • mentioned Science concept
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    evidence rules-v4
    Feed category: Science
  • mentioned Tech News concept
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    Feed category: Tech News
  • discusses Science concept
    0.40
    evidence rules-v4
    Feed category: Science
  • discusses News concept
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    evidence rules-v4
    Feed category: News
  • discusses Tech News concept
    0.40
    evidence rules-v4
    Feed category: Tech News
  • mentioned NLP concept
    0.35
    evidence rules-v4
    NLP

companys 4

  • mentioned TWIML company
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    Feed author/publisher: TWIML
  • mentioned Technology company
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    evidence rules-v4
    Feed category: Technology
  • discusses Technology company
    0.40
    evidence rules-v4
    Feed category: Technology
  • hosted by TWIML company
    0.40
    evidence rules-v4
    Feed author/publisher: TWIML

persons 3

  • mentioned Matteo Chinazzi person
    0.72
    evidence rules-v4
    NLP for Mapping Physics Research with Matteo Chinazzi - #353
  • mentioned Sam Charrington person
    0.70
    evidence rules-v4
    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
    0.55
    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
Predicting the future of science, particularly physics, is the task that Matteo Chinazzi, an associate research scientist at Northeastern University focused on in his paper Mapping the Physics Research Space: a Machine Learning Approach. In addition to predicting the trajectory of physics research, Matteo is also active in the computational epidemiology field. His work in that area involves building simulators that can model the spread of diseases like Zika or the seasonal flu at a global scale.