The mission of the Flatiron Institute is to advance scientific research through computational methods, including data analysis, theory, modeling and simulation.

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Featured News

At first glance, this image is dominated by the vibrant glow of the swirling spiral to the lower left of the frame. However, this galaxy is far from the most interesting spectacle here — behind it sits a galaxy cluster. Galaxies are not randomly distributed in space; they swarm together, gathered up by the unyielding hand of gravity, to form groups and clusters. The Milky Way is a member of the Local Group, which is part of the Virgo Cluster, which in turn is part of the 100 000-galaxy-strong Laniakea Supercluster. The galaxy cluster seen in this image is known as SDSS J0333+0651. Clusters such as this can help astronomers understand the distant — and therefore early — Universe. SDSS J0333+0651 was imaged as part of a study of star formation in far-flung galaxies. Star-forming regions are typically not very large, stretching out for a few hundred light-years at most, so it is difficult for telescopes to resolve them at a distance. Even using its most sensitive and highest-resolution cameras, Hubble cannot resolve very distant star-forming regions, so astronomers use a cosmic trick: they search instead for galaxy clusters, which have a gravitational influence so immense that they warp the spacetime around them. This distortion acts like a lens, magnifying the light of galaxies sitting far behind the cluster and producing elongated arcs like the one seen to the left of centre in this image.

Center for Computational Astrophysics Artificial Intelligence Discovers Secret Equation for ‘Weighing’ Galaxy Clusters

Center for Computational Neuroscience Computational Model Uncovers New Insights Into How Our Brains Store Information

Center for Computational Mathematics Stepping in the Right Direction: Optimizing Machine Learning

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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

Machine Learning at the Flatiron Institute

Machine learning has become an indispensable tool for computational science, and it is an active and growing area of research throughout the Flatiron Institute.

Structural and Molecular Biophysics Collaboration

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.

Learning the Universe

This collaboration, directed by Greg Bryan of Columbia University, aims to understand and determine the evolution and initial conditions of our universe, using observations via a Bayesian forward modeling approach.

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| Institute D'Astrophysique de Paris
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| Carnegie Mellon University
| Max-Planck Institute for Astrophysics

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.

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A major effort of the Flatiron Institute is the development and support of high-quality, open-source software for research.

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Research Highlights

February 1, 2023

Stable Solid Molecular Hydrogen above 900 K from a Machine-Learned Potential Trained with Diffusion Quantum Monte Carlo

Hongwei Niu, Yubo Yang, Scott Jensen, Markus Holzmann, Carlo Pierleoni, David M. Ceperley

We survey the phase diagram of high-pressure molecular hydrogen with path integral molecular dynamics using a machine-learned interatomic potential trained…

Physical Review Letters

February 1, 2023

Stable Solid Molecular Hydrogen above 900 K from a Machine-Learned Potential Trained with Diffusion Quantum Monte Carlo

Hongwei Niu, Yubo Yang, Scott Jensen, Markus Holzmann, Carlo Pierleoni, David M. Ceperley

We survey the phase diagram of high-pressure molecular hydrogen with path integral molecular dynamics using a machine-learned interatomic potential trained…

Physical Review Letters

December 25, 2022

A geometrical connection between sparse and low-rank matrices and its application to manifold learning

L. Saul

We consider when a sparse nonnegative matrix S can be recovered, via an elementwise nonlinearity, from a real-valued matrix L…

Transactions on Machine Learning Research

News & Announcements

Center for Computational Astrophysics

March 23, 2023

Artificial Intelligence Discovers Secret Equation for ‘Weighing’ Galaxy Clusters

Flatiron Scientist Spotlight

March 20, 2023

Advancing Astronomy With Statistics By Mara Johnson-Groh

Center for Computational Biology

March 13, 2023

Why Don’t Disease-Linked Genetic Mutations Always Result in Disease? A Study in Fruit Flies Offers New Clues By Susan Reslewic-Keatley, Ph.D.

Leadership

Leslie Greengard, M.D., Ph.D.

Director, CCM

Leslie Greengard joined the Simons Foundation in 2013 as founding director of the Simons Center for Data Analysis, now called the Center for Computational Biology. Prior to that, Greengard served as director of the Courant Institute of Mathematical Sciences at New York University.

Nick Carriero, Ph.D.

Co-Director, SCC

Nick Carriero came to the Simons Foundation in 2014 from Yale University, where he was a senior research scientist in computer science and co-director of the W.M. Keck Biotechnology Resource Laboratory’s high-performance computing resource.

Julianne Dalcanton, Ph.D.

Director, CCA

Julianne Dalcanton joined the Simons Foundation in September 2021 as the director of the Flatiron Institute’s Center for Computational Astrophysics (CCA). Her research specializes in the origins and evolution of galaxies.

Ian Fisk, Ph.D.

Co-Director, SCC

Ian Fisk came to the Simons Foundation in 2014 after working in the computing division at the Fermi National Accelerator Laboratory in Illinois since 2003. Fisk was the Compact Muon Solenoid (CMS) experiment’s computing coordinator at CERN from 2010 to 2013, overseeing operations and improvements…

Antoine Georges is a professor of physics at the Collège de France, where he holds the chair in condensed matter physics. He also has joint appointments with École Polytechnique and the University of Geneva. He received his Ph.D. from the École Normale Supérieure in 1988.…

Andrew Millis, Ph.D.

Collaboration Director

Andrew Millis is co-director of the Center for Computational Quantum Physics and associate director for Physics at the Simons Foundation. He has done fundamental research on heavy fermion compounds, quantum phase transitions, ‘colossal’ magnetoresistance materials and high transition-temperature superconductivity.

Michael Shelley, Ph.D.

Director, CCB

Michael Shelley joined the Simons Foundation in 2016 to work on the modeling and simulation of complex systems arising in physics and biology.

Eero P. Simoncelli, Ph.D.

Alumni

Eero P. Simoncelli is the inaugural director of the Center for Computational Neuroscience. Eero P. Simoncelli’s research interests span a wide range of topics in the representation and analysis of visual images and sounds in both machine and biological vision systems.