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
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
Lawrence Saul
Group Leader, Machine Learning, CCM
Areas of Interest: High dimensional data analysis, latent variable models, deep learning, variational inference, kernel methods
Jeremy Cohen
Flatiron Research Fellow, CCM
Nikhil Ghosh
Flatiron Research Fellow, CCM
Mark Goldstein
Flatiron Research Fellow, CCM
Zahra Kadkhodaie
Flatiron Research Fellow, CCM
Tetiana Parshakova
Flatiron Research Fellow, CCM
Denny Wu
Flatiron Research Fellow, CCM
Daniel D. Lee
Visiting Scholar, CCM
Soledad Villar
Visiting Scholar, CCM
Luhuan Wu
Associate Research Scientist, CCM
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
Sarah Harvey
Flatiron Research Fellow, CCN
Area of Interest: Theoretical neuroscience, statistical physics, ML methods for neural data analysis
Eero Simoncelli
Director, CCN
Areas of Interest: Analysis and representation of visual information in biological and artificial networks. Coding and inference
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
Yuhai Tu
Senior Research Scientist, CCN
Areas of Interest: Statistical physics, molecular/cellular biology, neuroscience, and machine learning
Xuehao Ding
Flatiron Research Fellow, CCN
Areas of Interest: NeuroAI, computational neuroscience, physics of intelligence
Philip Kidd
Flatiron Research Fellow, CCN
Areas of Interest: Animal behavior, dynamical systems, optimal prediction and control
Abdul Canatar
Flatiron Research Fellow, CCN
Areas of Interest: Physics of learning, theoretical neuroscience, geometry of high-dimensional representations
Josh Pugh-Sanford
Flatiron Research Fellow, CCN
Areas of Interest: Dynamical systems theory and interpretable models of learning
Chi-Ning Chou
Flatiron Research Fellow, CCN
Areas of Interest: Computational neuroscience, high-dimensional geometry and statistics, science of deep learning
Sebastian Lee
Flatiron Research Fellow, CCN
Areas of Interest: Continual learning, reinforcement learning, NeuroAI
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
Alev Orfi
Graduate Student, CCQ
Areas of Interest: Neural quantum states, variational Monte Carlo, sampling algorithms
Anirvan Sengupta
Visiting Scholar, CCQ
Areas of Interest: Representation learning, dynamics and control, applications to quantum systems, systems neuroscience
Christopher Roth
Flatiron Research Fellow, CCQ
Areas of Interest: Neural quantum states, equivariant models, variational Monte Carlo
Jaylyn C. Umana
Graduate Student, CCQ
Areas of Interest: Symbolic regression, neural networks, optimization
Ina Park
Flatiron Research Fellow, CCQ
Riccardo Rende
Flatiron Research Fellow, CCQ
Miguel Morales
Research Scientist, CCQ
Christopher Roth
Flatiron Research Fellow, CCQ
Shiwei Zhang
Senior Research Scientist, CCQ
Olivier Parcollet
Senior Research Scientist, CCQ
Miles Stoudenmire
Research Scientist, CCQ
Researchers in CCB
Xi Chen
Research Scientist, CCB
Areas of Interest: Distribution learning, Markov chain Monte Carlo, semi-supervised learning
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
Lisa BrownM
Senior Data Scientist, CCB
Hayden Nunley
Associate Research Scientist, CCB
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
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.
Francois Lanusse
Associate Research Scientist, CCA
Lucia Perez
Flatiron Research Fellow, CCA
Helen Qu
Flatiron Research Fellow, CCA
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
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No events are scheduled in January.
Past Events
Cosmic Connections: A ML X Astrophysics Symposium
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