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Chris Wiggins
The New York Times
Chris Wiggins is the Chief Data Scientist at The New York Times (NYT) and
Associate Professor of Applied Mathematics at Columbia University. He applies
machine learning techniques in both roles, albeit to answer very different questions.
In his role at the NYT, Wiggins is creating a machine learning group to analyze both
the content produced by reporters and the data generated by readers consuming
articles, as well as data from broader reader navigational patterns—with the over-
arching goal of better listening to NYT consumers as well as rethinking what journal-
ism is going to look like over the next 100 years.
At Columbia University, Wiggins focuses on the application of machine learning
techniques to biological research with large data sets. This includes analysis of
naturally occurring networks, statistical inference applied to biological time-series
data, and large-scale sequence informatics in computational biology. As part of his
work at Columbia, he is a founding member of the university's Institute for Data
Sciences and Engineering (IDSE) and Department of Systems Biology.
Wiggins is also active in the broader New York tech community, as co-founder and
co-organizer of hackNY—a nonprofit organization that guides and mentors the next
generation of hackers and technologists in the New York innovation community.
Wiggins has held appointments as a Courant Instructor at the New York University
Courant Institute of Mathematical Sciences and as a Visiting Research Scientist
at the Institut Curie (Paris), Hahn-Meitner Institut (Berlin), and the Kavli Institute
for Theoretical Physics (Santa Barbara). He holds a PhD in Physics from Princeton
University and a BA in Physics from Columbia, minoring as an undergraduate in
religion and in mathematics.
Wiggins's diverse accomplishments demonstrate how world-class data science
skills wedded to extraordinarily strong values can enable an individual data scien-
 
 
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