The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) NLP for Mapping Physics Research with Matteo Chinazzi - #353
Mar 2, 2020
Matteo Chinazzi, an associate research scientist at Northeastern University, is at the forefront of using machine learning to revolutionize physics research and computational epidemiology. He discusses his innovative methods for mapping research dynamics using Word2Vec, predicting future expertise in cities, and exploring the economic impacts of scientific work. Additionally, Matteo details the Starspace algorithm's optimization techniques and how assessing relative strengths in research sheds new light on publication trends. A fascinating blend of science and technology!
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Mapping Physics Research
- Matteo Kinazzi maps the physics research space using word embeddings.
- He uses publication data from the American Physical Society and the StarSpace algorithm.
Principle of Relatedness
- The research identified a "principle of relatedness" in physics research.
- Cities with expertise in one area are more likely to develop expertise in related areas.
City Specializations
- Brussels specializes in nuclear physics, while Grenoble focuses on condensed matter physics.
- These specializations are visually observable in the embedding space.