Machine Learning at the Flatiron Institute

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.

Machine Learning Visiting Scholar Program

Directed by Lawrence Saul (Center for Computational Mathematics) and an advisory committee consisting of Bob Carpenter (Center for Computational Mathematics), Mitya Chklovskii (Center for Computational Neuroscience), Antoine George (Center for Computational Quantum Physics), David Hogg (Center for Computational Astronomy), Olga Troyanskaya (Center for Computational Biology) and Jutta Rogal (Initiative for Computational Catalysis), the program is designed to spur further collaboration between researchers at the Flatiron Institute and in academia applying machine learning to problems of scientific interest. The program will provide support to current faculty members who wish to spend sabbaticals or leaves of absence at Flatiron and to junior researchers who are able to defer their start date at a tenure-track position in academia in order to spend a research year at the Flatiron Institute.

The program will support up to four visiting scholars at a time, with visits up to one year in length. Junior researchers will be hired as associate research scientists with one-year appointments; senior researchers will typically have a visiting appointment compatible with their university’s sabbatical leave policies. Visiting scholars will be offered subsidized housing, salary and benefits as appropriate, travel and other research-related expenses.

Researchers in CCM

Lawrence Saul

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

Alberto Bietti

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

Joan Bruna

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

Diana Cai

Flatiron Research Fellow, CCM
Areas of Interest: Probabilistic modeling, robust Bayesian inference, machine learning for science

Jeremy Cohen

Flatiron Research Fellow, CCM
Areas of Interest: optimization for deep learning, science of deep learning

Nikhil Ghosh

Flatiron Research Fellow, CCM
Areas of Interest: Deep Learning, Optimization

Mark Goldstein

Flatiron Research Fellow, CCM
Areas of Interest: generative modeling, causality, machine learning for physical sciences

Zahra Kadkhodaie

Flatiron Research Fellow, CCM
Areas of Interest: Science of deep learning, learning and sampling from densities in high dimensions, evaluation methods for models generalization and robustness

Daniel D. Lee

Visiting Scholar, CCM
Areas of Interest: High dimensional statistics and geometry, computational neuroscience, robotics

Tetiana Parshakova

Flatiron Research Fellow, CCM
Areas of Interest: Convex Optimization, Deep Learning, Numerical Linear Algebra

Soledad Villar

Visiting Scholar, CCM
Areas of Interest: Learning theory, deep learning, equivariant machine learning, graph neural networks, machine learning for science, high dimensional probability, optimization.

Denny Wu

Flatiron Research Fellow, CCM

Luhuan Wu

Associate Research Scientist, CCM
Areas of Interest: Probabilistic modeling, generative modeling, approximate inference, and biophysical applications.

Researchers in CCN

Eero Simoncelli

Director, CCN
Areas of Interest: Analysis and representation of visual information in biological and artificial networks. Coding and inference

Abdul Canatar

Flatiron Research Fellow, CCN
Areas of Interest: Physics of learning, theoretical neuroscience, geometry of high-dimensional representations

Mitya Chklovskii

Group Leader, Neural Circuits and Algorithms, CCN
Areas of Interest: Theoretical neuroscience, connectomics, biologically inspired AI, dynamics and control

Chi-Ning Chou

Flatiron Research Fellow, CCN
Areas of Interest: Computational neuroscience, high-dimensional geometry and statistics, science of deep learning

SueYeon Chung

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

Xuehao Ding

Flatiron Research Fellow, CCN
Areas of Interest: NeuroAI, computational neuroscience, physics of intelligence

Sarah Harvey

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

Philip Kidd

Flatiron Research Fellow, CCN
Areas of Interest: Animal behavior, dynamical systems, optimal prediction and control

Sebastian Lee

Flatiron Research Fellow, CCN
Areas of Interest: Continual learning, reinforcement learning, NeuroAI

Josh Pugh-Sanford

Flatiron Research Fellow, CCN
Areas of Interest: Dynamical systems theory and interpretable models of learning

Yuhai Tu

Senior Research Scientist, CCN
Areas of Interest: Statistical physics, molecular/cellular biology, neuroscience, and machine 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

Antoine Georges

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

Andrew Millis

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

Miguel Morales

Research Scientist, CCQ
Areas of Interest: Neural quantum states, variational Monte Carlo, strong correlations, ab-initio electronic structure.

Alev Orfi

Graduate Student, CCQ
Areas of Interest: Neural quantum states, variational Monte Carlo, sampling algorithms

Olivier Parcollet

Senior Research Scientist, CCQ

Ina Park

Flatiron Research Fellow, CCQ
Areas of Interest: Machine learning for many-body problem and quantum materials, first-principles calculation, and impurity solver; materials database

Riccardo Rende

Flatiron Research Fellow, CCQ
Areas of Interest: Transformers, Feature Learning, Neural Quantum States, Variational Monte Carlo

Christopher Roth

Flatiron Research Fellow, CCQ
Areas of Interest: Neural quantum states, equivariant models, variational Monte Carlo

Anirvan Sengupta

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

Miles Stoudenmire

Research Scientist, CCQ
Areas of Interest: Tensor-based learning & interpolation methods. Linear algebra techniques including low-rank decompositions, interpretable factorizations, and sketching.

Jaylyn C. Umana

Graduate Student, CCQ
Areas of Interest: Symbolic regression, neural networks, optimization

Shiwei Zhang

Senior Research Scientist, CCQ
Areas of Interest: Computational method development for quantum physics. Monte Carlo methods. Neural network and machine learning for quantum systems. Condensed matter and many-body physics. High-performance computing.

Researchers in CCB

Olga Troyanskaya

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

Lisa M. Brown

Senior Data Scientist, CCB

Xi Chen

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

Hayden Nunley

Associate Research Scientist, CCB
Areas of Interest: Machine learning for computer vision: segmentation and lineage construction, modeling of cell lineage trees

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

Researchers in CCA

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

Lucia Perez

Flatiron Research Fellow, CCA
Areas of Interest: simulation-based inference, machine learning for science, accelerated forward modeling of galaxies

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.

Helen Qu

Flatiron Research Fellow, CCA
Areas of Interest: Machine learning for science, reinforcement learning, self-supervised learning, physics of learning.

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@FI. You can find incoming events and selected archived past events below.

ML@FI 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. Each meeting is followed by a reception to encourage inter-center interactions. See the website for 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, Alberto Bietti, and Carolina Cuesta-Lazaro

The Machine Learning New York City (ML-NYC) Speaker Series is a monthly event for machine learning practitioners, researchers, and students to meet and watch talks from leading researchers in the field. Each event will feature a New York City-based speaker presenting their work. The ML-NYC Speaker Series is open to anyone interested in machine learning, and we encourage everyone to attend, whether you are a beginner or an expert in the field.

For more information or to propose speakers for future seminars, please contact the organizers: Lawrence Saul, David Blei and Joan Bruna

March 2026