The Forum for Integrated Research

Uniting science and the humanities to reimagine our future

About

FIR is an interdivisional intellectual hub dedicated to bringing a multiplicity of forms of investigation into the increasingly complex world we live in. We hold that the separation of the humanistic fields from science since the 1800s has led us to an increasingly untenable situation for both fields and for society at large. Through FIR’s innovative, non-departmental structure, we connect scholars from the humanities, social sciences, and natural sciences to shape the future of knowledge formation and its application in addressing contemporary challenges.

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Our Thinkers

Our thinkers include preeminent names in their fields from Stanford, Harvard, UChicago and many more U.S. universities and institutes, including Nobel Prize winners, Guggenheim laureates, and MacArthur “Genius grant” recipients.

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Case studies

at the frontiers of knowledge

Read our essays on the necessity and virtue of cross-disciplinary collaboration in these key fields:

Digital History

Jo Guldi’s research outlines how Digital History merges historical perspectives with data science, enhancing the analysis of both long-term and immediate changes.

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The Symbiosis of Science and Humanities

My project is to show that the humanities and sciences are interlaced, and that recognition of this fact is necessary if we are to better determine what is good for us as a society—and even as a species.

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Papers

Bonfire of the humanities

by Jo Guldi

Public debate is afflicted by short-term thinking – how did history abdicate its role of inspiring the longer view?

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The Earth for Man

by Jo Guldi

Redistributing land was once central to global development efforts—and it should be today.

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Historians use data science to mine the past

by Jo Guldi

Historians today can perform computational tasks as they dig for insights—using, for example, computational models to reveal how often two words appear together in texts, launching network analyses to link individuals who appear in the same documents, or training computer vision models to recognize key features on digitized maps.

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