The mission of the Flatiron Institute is to advance scientific research through computational methods, including data analysis, theory, modeling and simulation.
Read MoreFeatured News
Center for Computational Mathematics
Bob Carpenter: Always Learning, Always Innovating
Read More
Bob Carpenter, a senior research scientist at the Center for Computational Mathematics, created Stan, a probabilistic programming language that has revolutionized research and industry.
Our Centers
Center for Computational Biology
Center for Computational Mathematics
Center for Computational Neuroscience
Center for Computational Quantum Physics
Scientific Computing Core
It develops, deploys and maintains computational infrastructure — from supercomputers to desktop PCs — dedicated solely to the use of Flatiron researchers.
Collaborative Work
Underlying all biological processes are molecules and their interactions with each other. However, our ability to understand how these molecules function over biologically relevant scales remains very limited.
- CCB
- CCM
Columbia University, the Flatiron Institute in New York City and the Max Planck Society in Germany have created a partnership, called the Center for Nonequilibrium Quantum Phenomena.
- CCQ
- |
- Columbia University
Flatiron Institute Inclusion, Diversity,
Equity & Advocacy (IDEA) Scholar Program
For distinguished scientists with a particular interest in diversity and inclusion
Scholars may engage in a variety of activities, such as working on scientific projects, starting new collaborations, mentoring junior scientists, and organizing or participating in workshops and career development events.
Software
A major effort of the Flatiron Institute is the development and support of high-quality, open-source software for research.
Research Highlights
Approximating the Gaussian as a Sum of Exponentials and Its Applications to the Fast Gauss Transform
We develop efficient and accurate sum-of-exponential (SOE) approximations for the Gaussian using rational approximation of the exponential function on the…
Communications in Computational PhysicsSimple lessons from complex learning: what a neural network model learns about cosmic structure formation
We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies…
arXiv:2206.04573Field Level Neural Network Emulator for Cosmological N-body Simulations
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists…
arXiv:2206.04594News & Announcements
2021 Annual Report
June 28, 2022
Center for Computational Astrophysics
June 13, 2022
Flatiron Scientist Spotlight
June 08, 2022
