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, CCM
Areas of Interest: Learning theory, optimization, deep learning, kernel methods

David Blei

Visiting Scholar, CCM
Areas of Interest: Topic models, probabilistic modeling, approximate Bayesian inference

Joan Bruna

Visiting Scholar, CCM
Areas of Interest: Learning theory, deep learning, machine learning for science, high dimensional statistics, algorithms

Andreas Buja

Senior Research Scientist, CCM
Areas of Interest: Statistical methodology, model misspecification, replicability, causality, applications in the genetics of autism

Bob Carpenter

Senior Research Scientist, CCM
Areas of Interest: Probabilistic programming, Markov chain Monte Carlo methods, variational inference

Michael Eickenberg

Research Scientist, CCM
Areas of Interest: Machine learning for science, applied statistics and signal processing, deep learning, neuroimaging and computational cognitive neuroscience

Anna Gilbert

Visiting Scholar, CCM
Areas of Interest: Theory and algorithms for high dimensional data analysis, metric representations, non-Euclidean embeddings

Robert Gower

Research Scientist, CCM
Areas of Interest: Stochastic optimization, interpolation, adaptive methods for deep learning, convergence of algorithms and second order methods

Jiequn Han

Flatiron Research Fellow, CCM
Areas of Interest: Multiscale modeling, numerical methods for partial differential equations, machine learning for science

Stephane Mallat

Distinguished Research Scientist, CCM
Areas of Interest: Signal processing, harmonic analysis, deep learning

Charles Margossian

Flatiron Research Fellow, CCM
Areas of Interest: Probabilistic programming, Markov chain Monte Carlo methods, variational inference

Ruben Ohana

Flatiron Research Fellow, CCM
Areas of Interest: Deep learning, randomized algorithms, high dimensional statistics, differential privacy

Loucas Pillaud-Viven

Flatiron Research Fellow, CCM
Areas of Interest: Learning theory, optimization, deep learning

Bruno Régaldo-Saint Blancard

Flatiron Research Fellow, CCM
Areas of Interest: Machine learning for astrophysics, applied signal processing, generative modeling

Lawrence Saul

Group Leader, Machine Learning, CCM
Areas of Interest: High dimensional data analysis, latent variable models, deep learning, variational inference, kernel methods

Neha Wadia

Flatiron Research Fellow, CCM
Areas of Interest: Learning theory, continuous-time optimization, high dimensional statistics

Yuling Yao

Flatiron Research Fellow, CCM
Areas of Interest: Scalable Bayesian workflows, meta-learning, causal inference

Wenda Zhou

Flatiron Research Fellow, CCM
Areas 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, CCN
Areas of Interest: Theoretical neuroscience, connectomics, biologically inspired AI, dynamics and control

SueYeon Chung

Project Leader, Geometric Data Analysis, CCN
Areas of Interest: Theoretical neuroscience, statistical physics of learning, high dimensional geometry and statistics

Jenelle Feather

Flatiron Research Fellow, CCN
Areas of Interest: Theoretical neuroscience, analysis of high dimensional auditory and visual representations

Siavash Golkar

Associate Research Scientist, Neural Circuits and Algorithms, CCN
Areas of Interest: Biological learning, deep learning, machine learning for science

Sarah Harvey

Flatiron Research Fellow, CCN
Area of Interest: Theoretical neuroscience, statistical physics, ML methods for neural data analysis

Brett Larsen

Flatiron Research Fellow, CCN/CCM
Areas of Interest: Deep learning, optimization, loss-landscape analysis, sparsity, high-dimensional statistics

David Lipshutz

Associate Research Scientist, CCN
Areas of Interest: Theoretical neuroscience, neuro-inspired ML, stochastic analysis, dynamical systems

Amin Nejatbakhsh

Flatiron Research Fellow, CCN
Areas of Interest: Computational neuroscience, machine learning, statistics, dynamical systems

Eero Simoncelli

Director, CCN
Areas 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, CCN
Areas of Interest: Biological learning, deep learning

Alex Williams

Associate Research Scientist, Statistical Analysis of Neural Data, CCN
Areas of Interest: Unsupervised learning, uncertainty quantification in deep learning, topological data analysis, covariance estimation

Researchers in CCQ

Anna Dawid

Flatiron Research Fellow, CCQ
Areas of Interest: Machine learning for (quantum) science, interpretability, deep learning theory

Domenico Di Sante

Affiliate Research Fellow, CCQ
Areas of Interest: Theoretical neuroscience, statistical physics of learning, high dimensional geometry and statistics

Antoine Georges

Director, CCQ
Areas of Interest: Machine learning for quantum systems

Matija Medvidović

Graduate Student, CCQ
Areas of Interest: Machine learning for many-body quantum physics, sampling, optimization

Andrew Millis

Co-Director, CCQ
Areas of Interest: Theoretical condensed matter physics, high-temperature superconductivity, numerical methods for the many-electron problem

Javier Robledo Moreno

Graduate Student, CCQ
Areas of Interest: Machine learning for many-body quantum physics, neural network representation of quantum states, quantum computing

Anirvan Sengupta

Visiting Scholar, CCQ
Areas of Interest: Representation learning, dynamics and control, applications to quantum systems, systems neuroscience

Jiawei Zang

Graduate Student, CCQ
Areas of Interest: Machine learning for many-body quantum physics, dimensionality reduction

Researchers in CCB

Xi Chen

Research Scientist, CCB
Areas of Interest: Distribution learning, Markov chain Monte Carlo, semi-supervised learning

Adam Lamson

Flatiron Research Fellow, CCB
Areas of Interest: Interpretable neural networks, biophysical and genomics modeling, reservoir computing

Suryanarayana Maddu

Flatiron Research Fellow, CCB
Areas of Interest: Physics-informed machine learning, statistical learning theory, high dimensional statistics

Zhicheng Pan

Flatiron Research Fellow, CCB
Areas of Interest: Deep learning for genomics, graphical neural networks

Christopher Park

Research Scientist, CCB
Areas of Interest: Probabilistic modeling, deep learning and statistical genetics

Natalie Sauerwald

Flatiron Research Fellow, CCB
Areas of Interest: Machine learning for genomics and genetics, optimization, interpretable models

Rachel Sealfon

Research Scientist, CCB
Areas of Interest: Machine learning for genomics, analysis of functional genomic data

Olga Troyanskaya

Deputy Director for Genomics, CCB
Areas of interest: Genomics and bioinformatics

Mao Weiguang

Flatiron Research Fellow, CCB
Areas of Interest: Deep learning, graphical models, dimensionality reduction

Researchers in CCA

Daniel Foreman-Mackey

Research Scientist, CCA
Areas of Interest: Probabilistic programming, Markov chain Monte Carlo, Gaussian Processes

Shirley Ho

Group Leader, Cosmology X Data Science, CCA
Areas of Interest: Machine learning for science, deep learning for simulation, neuro-symbolic models, high dimensional inference

David W. Hogg

Group Leader, Astronomical Data, CCA
Areas 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 CCM
Areas of Interest: Machine learning for science, differentiable simulations, Markov chain Monte Carlo methods, approximate Bayesian inference

Mariel Pettee

Visiting Scholar, CCA
Areas of Interest: Machine learning for particle physics and cosmology, anomaly detection, graph neural networks, equivariant representations

Francisco Villaescusa-Navarro

Research Scientist, CCA
Areas 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, CCA
Areas of Interest: Deep learning for data analysis and simulation in astrophysics
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