The University of Chicago

To accelerate scientific discovery by using statistical machine learning to enable advanced search of mathematical literature

  • Amount $900,000
  • City Chicago, IL
  • Investigator John Lafferty
  • Year 2015
  • Program Digital Technology
  • Sub-program Scholarly Communication

Mathematical formulas are undiscoverable by modern search engines. If you are looking for a famous theorem or an equation with a name, standard search engines like Google or online encyclopedias like Wikipedia can direct you to it. But if what you are looking for is an equation that expresses one variable in terms of another, you are out of luck. Because the consumer base for such information is small and because the task of programming computers to recognize mathematical formulas is difficult, no major search engine has prioritized mathematical search. Yet from a societal point of view, the benefits of accelerating discoveries by providing such search capabilities could surely be enormous.   This grant funds a project by John Lafferty from the University of Chicago and David Blei from Columbia University to advance the field of mathematical search by developing a software program that uses sophisticated pattern recognition and statistical machine learning techniques to recognize and identify mathematical formulas on the web.

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