Duke’s Data+ summer research program received the gold prize in the natural sciences category at the Reimagine Education Conference and Awards in Philadelphia.
Duke mathematics professor and iiD faculty member Jonathan Mattingly talks about his work with mathematical modeling and gerrymandering.
On November 4, 2016, key staff and administrators working in Gross Hall gathered to celebrate the renaming of Gross Hall 330 to the Ahmadieh Family Grand Hall.
The Ahmadieh family are longtime supporters of Duke’s programs, and are familiar faces at the many of seminars that take place in Gross Hall. The Ahmadieh Family Grand Hall is named in honor of the family’s generous support and interest in the programs offered by the Information Initiative at Duke.
This article on Duke Today features iiD's Ingrid Debauchies' book Reunited: An Art Historical and Digital Adventure, which accompanies her group's work on the Ghissi exhibit at North Carolina Museum of Art.
How can Internet search results be improved for minority users? A Data+ team sponsored by Sankofa, Inc. worked to figure it out.
In Wired, Ingrid Daubechies shows that math is everywhere, if you know where to look. She explains how her team used it as part of the Ghissi Exhibit at the North Carolina Museum of Art.
See how an iiD Data+ team teamed up with Duke Parking and Transportation to explore how data analysis and visualization can help make parking on campus a breeze.
Siam News highlights a paper, coauthored by iiD's Ingrid Daubechies, recently published in the SIAM Journal on Imaging Sciences describing an algorithm that eliminates the visual discrepancies of cradling in X-rays of paintings.
North Carolina's "bathroom bill," HB2 has stirred passions and led to protests. A Duke Data+ team assessed the discrimination transgender people faced even before the bill was passed.
In a Data+ project led by Leslie Collins, professor of electrical and computer engineering and biomedical engineering at Duke, the team looked at satellite images from four different cities to locate solar panels to train the machine learning algorithm. The goal was to show how solar energy is being adopted on a county, city or neighborhood level.