Chris Danforth
Professor, Department of Mathematics and Statistics
PRONOUNS he/him
- Ph.D., University of Maryland, College Park
- B.S. in Mathematics & Physics, Bates College
Department of Mathematics and Statistics
BIO
Chris is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts and developed a real-time remote sensor of global happiness using messages from Twitter. Danforth co-runs the Computational Story Lab with Peter Dodds.
Courses
- MATH 2522 - Linear Algebra
- MATH 3737 - Numerical Analysis
- MATH 3766 - Chaos, Fractals & Dynamical Systems
- MATH 5230 - Graduate Ordinary Differential Equations
- MATH 6989 - Graduate Seminar
Area(s) of expertise
Computational Social Science, Complex Systems, Chaos
Bio
Chris is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts and developed a real-time remote sensor of global happiness using messages from Twitter. Danforth co-runs the Computational Story Lab with Peter Dodds.
Courses
- MATH 2522 - Linear Algebra
- MATH 3737 - Numerical Analysis
- MATH 3766 - Chaos, Fractals & Dynamical Systems
- MATH 5230 - Graduate Ordinary Differential Equations
- MATH 6989 - Graduate Seminar
Areas of Expertise
Computational Social Science, Complex Systems, Chaos
Projects
: a visual comparison of phrase popularity in 150 billion tweets
: a population scale measure of daily happiness
Research & Press
“Inside the lab that’s quantifying happiness”
Profile of our research group in
“Has Twitter just had its saddest fortnight ever?”
Story on Hedonometer in
“Instagram photos reveal predictive markers of depression”
in EPJ Data Science, by New York Times
“The emotional arcs of stories are dominated by six basic shapes”
in EPJ Data Science, by The Atlantic
“Human language reveals a universal positivity bias”
in PNAS, by New York Times