Machine Learning at the Flatiron Institute
- Center for Computational Mathematics
- Center for Computational Neuroscience
- Center for Computational Astrophysics
- Center for Computational Biology
- Center for Computational Quantum Physics
In recent years machine learning has emerged as an indispensable tool for computational science. It is also an active and growing area of study throughout the Flatiron Institute. Researchers at Flatiron are especially interested in the core areas of deep learning, probabilistic modeling, optimization, learning theory and high dimensional data analysis. They are also applying machine learning to problems in cosmological modeling, quantum many-body systems, computational neuroscience and bioinformatics. Below is a list of researchers who work in these areas; prospective visitors should feel free to contact them for more information.
Researchers in CCM
Alberto Bietti
Research Scientist, CCMAreas of Interest:Learning theory, optimization, deep learning, kernel methods
David Blei
Visiting Scholar, CCMAreas of Interest:Topic models, probabilistic modeling, approximate Bayesian inference
Joan Bruna
Visiting Scholar, CCMAreas of Interest:Learning theory, deep learning, machine learning for science, high dimensional statistics, algorithms
Andreas Buja
Senior Research Scientist, CCMAreas of Interest:Statistical methodology, model misspecification, replicability, causality, applications in the genetics of autism
Bob Carpenter
Senior Research Scientist, CCMAreas of Interest:Probabilistic programming, Markov chain Monte Carlo methods, variational inference
Michael Eickenberg
Research Scientist, CCMAreas of Interest:Machine learning for science, applied statistics and signal processing, deep learning, neuroimaging and computational cognitive neuroscience
Anna Gilbert
Visiting Scholar, CCMAreas of Interest:Theory and algorithms for high dimensional data analysis, metric representations, non-Euclidean embeddings
Robert Gower
Research Scientist, CCMAreas of Interest:Stochastic optimization, interpolation, adaptive methods for deep learning, convergence of algorithms and second order methods
Jiequn Han
Flatiron Research Fellow, CCMAreas of Interest:Multiscale modeling, numerical methods for partial differential equations, machine learning for science
Stephane Mallat
Distinguished Research Scientist, CCMAreas of Interest:Signal processing, harmonic analysis, deep learning
Charles Margossian
Flatiron Research Fellow, CCMAreas of Interest:Probabilistic programming, Markov chain Monte Carlo methods, variational inference
Ruben Ohana
Flatiron Research Fellow, CCMAreas of Interest:Deep learning, randomized algorithms, high dimensional statistics, differential privacy
Loucas Pillaud-Viven
Flatiron Research Fellow, CCMAreas of Interest:Learning theory, optimization, deep learning
Bruno Régaldo-Saint Blancard
Flatiron Research Fellow, CCMAreas of Interest:Machine learning for astrophysics, applied signal processing, generative modeling
Lawrence Saul
Group Leader, Machine Learning, CCMAreas of Interest:High dimensional data analysis, latent variable models, deep learning, variational inference, kernel methods
Neha Wadia
Flatiron Research Fellow, CCMAreas of Interest:Learning theory, continuous-time optimization, high dimensional statistics
Yuling Yao
Flatiron Research Fellow, CCMAreas of Interest:Scalable Bayesian workflows, meta-learning, causal inference
Wenda Zhou
Flatiron Research Fellow, CCMAreas of Interest:Deep learning for structured data (e.g., molecular graphs, CAD models, scientific imaging)
Researchers in CCN
Mitya Chklovskii
Group Leader, Neural Circuits and Algorithms, CCNAreas of Interest:Theoretical neuroscience, connectomics, biologically inspired AI, dynamics and control
SueYeon Chung
Project Leader, Geometric Data Analysis, CCNAreas of Interest:Theoretical neuroscience, statistical physics of learning, high dimensional geometry and statistics
Jenelle Feather
Flatiron Research Fellow, CCNAreas of Interest:Theoretical neuroscience, analysis of high dimensional auditory and visual representations
Siavash Golkar
Associate Research Scientist, Neural Circuits and Algorithms, CCNAreas of Interest:Biological learning, deep learning, machine learning for science
Sarah Harvey
Flatiron Research Fellow, CCNArea of Interest:Theoretical neuroscience, statistical physics, ML methods for neural data analysis
Brett Larsen
Flatiron Research Fellow, CCN/CCMAreas of Interest:Deep learning, optimization, loss-landscape analysis, sparsity, high-dimensional statistics
David Lipshutz
Associate Research Scientist, CCNAreas of Interest:Theoretical neuroscience, neuro-inspired ML, stochastic analysis, dynamical systems
Amin Nejatbakhsh
Flatiron Research Fellow, CCNAreas of Interest:Computational neuroscience, machine learning, statistics, dynamical systems, computer vision
Eero Simoncelli
Director, CCNAreas of Interest:Analysis and representation of visual information in biological and artificial networks. Coding and inference
Tiberiu Tesileanu
Associate Research Scientist, Neural Circuits and Algorithms, CCNAreas of Interest:Biological learning, deep learning
Alex Williams
Associate Research Scientist, Statistical Analysis of Neural Data, CCNAreas of Interest:Unsupervised learning, uncertainty quantification in deep learning, topological data analysis, covariance estimation
Researchers in CCQ
Anna Dawid
Flatiron Research Fellow, CCQAreas of Interest:Machine learning for (quantum) science, interpretability, deep learning theory
Domenico Di Sante
Affiliate Research Fellow, CCQAreas of Interest:Theoretical neuroscience, statistical physics of learning, high dimensional geometry and statistics
Antoine Georges
Director, CCQAreas of Interest:Machine learning for quantum systems
Matija Medvidović
Graduate Student, CCQAreas of Interest:Machine learning for many-body quantum physics, sampling, optimization
Andrew Millis
Co-Director, CCQAreas of Interest:Theoretical condensed matter physics, high-temperature superconductivity, numerical methods for the many-electron problem
Javier Robledo Moreno
Graduate Student, CCQAreas of Interest:Machine learning for many-body quantum physics, neural network representation of quantum states, quantum computing
Anirvan Sengupta
Visiting Scholar, CCQAreas of Interest:Representation learning, dynamics and control, applications to quantum systems, systems neuroscience
加威藏
Graduate Student, CCQAreas of Interest:Machine learning for many-body quantum physics, dimensionality reduction
Researchers in CCB
Xi Chen
Research Scientist, CCBAreas of Interest:Distribution learning, Markov chain Monte Carlo, semi-supervised learning
Adam Lamson
Flatiron Research Fellow, CCBAreas of Interest:Interpretable neural networks, biophysical and genomics modeling, reservoir computing
Suryanarayana Maddu
Flatiron Research Fellow, CCBAreas of Interest:Physics-informed machine learning, statistical learning theory, high dimensional statistics
Zhicheng Pan
Flatiron Research Fellow, CCBAreas of Interest:Deep learning for genomics, graphical neural networks
Christopher Park
Research Scientist, CCBAreas of Interest:Probabilistic modeling, deep learning and statistical genetics
Natalie Sauerwald
Flatiron Research Fellow, CCBAreas of Interest:Machine learning for genomics and genetics, optimization, interpretable models
Rachel Sealfon
Research Scientist, CCBAreas of Interest:Machine learning for genomics, analysis of functional genomic data
Olga Troyanskaya
Deputy Director for Genomics, CCBAreas of interest:Genomics and bioinformatics
Mao Weiguang
Flatiron Research Fellow, CCBAreas of Interest:Deep learning, graphical models, dimensionality reduction
Researchers in CCA
Miles Cranmer
Flatiron Research Fellow, CCAAreas of Interest:Open-source tooling, model interpretability, learning to simulate, learned coarsening, physics-based inductive biases, graph neural networks, sparsity, symbolic regression/program synthesis, model distillation
Daniel Foreman-Mackey
Research Scientist, CCAAreas of Interest:Probabilistic programming, Markov chain Monte Carlo, Gaussian Processes
Shirley Ho
Group Leader, Cosmology X Data Science, CCAAreas of Interest:Machine learning for science, deep learning for simulation, neuro-symbolic models, high dimensional inference
David W. Hogg
Group Leader, Astronomical Data, CCAAreas of Interest:Causal models, enforcing physical symmetries, adversarial attacks, models of cameras and spectrographs
Chirag Modi
Flatiron Research Fellow, Cosmology X Data Science, CCA joint with CCMAreas of Interest:Machine learning for science, differentiable simulations, Markov chain Monte Carlo methods, approximate Bayesian inference
Francisco Villaescusa-Navarro
Research Scientist, CCAAreas of Interest:Neuro-simulations, graph neural netwoks, likelihood-free inference, manifold learning, generative models, symmetries for deep learning.
Kaze Wong
Flatiron Research Fellow, Gravitational Wave Astronomy, CCAAreas of Interest:Deep learning for data analysis and simulation in astrophysics
Events
Machine learning events at Flatiron Institute come in two flavors: one-time events like workshops, conferences or schools and a regular seminar series,ML@Flatiron. You can find incoming events and selected archived past events below.
ML@Flatiron is a seminar series focused on machine learning and its applications to science. It is aimed at Flatiron Institute research scientists and our collaborators. Seminars usually take place every other Tuesday at 3:00 p.m. in the CCN classroom on the fourth floor of 160 Fifth Ave. Each meeting is followed by a reception to encourage inter-center interactions. See thewebsitefor past seminars and their recordings!
For more information, to join the seminar mailing list or to propose speakers for future seminars, please contact the organizers:Shirley Ho,Siavash Golkar,Anna DawidorMichael Eickenberg.
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19Tue - Event3:00 - 5:00 p.m.
Machine Learning at the Flatiron Institute Seminar: Eero Simoncelli
- Event3:00 - 5:00 p.m.
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03Tue - Symposia3:00 - 5:00 p.m.
ML@FI: David Spergel
- Symposia3:00 - 5:00 p.m.
Past Events
Flatiron-Wide Machine Learning (FWML)
2022 Machine Learning at the Flatiron Institute Seminar Series
2021 Machine Learning at the Flatiron Institute Seminar Series
2020 Machine Learning at the Flatiron Institute Seminar Series
Flatiron Machine Learning X Science Summer School
Challenges and Prospects of Machine Learning for the Physical Sciences
Learn the Universe — an ML X Cosmology Workshop
Machine Learning for Quantum Simulation: Virtual Conference
Machine Learning Quantum Matter Data