{"id":101927,"date":"2023-04-18T15:04:40","date_gmt":"2023-04-18T19:04:40","guid":{"rendered":"https:\/\/www.simonsfoundation.org\/?page_id=101927"},"modified":"2026-05-08T15:01:26","modified_gmt":"2026-05-08T19:01:26","slug":"machine-learning-at-the-flatiron-institute","status":"publish","type":"page","link":"https:\/\/www.simonsfoundation.org\/flatiron\/machine-learning-at-the-flatiron-institute\/","title":{"rendered":"Machine Learning at the Flatiron Institute"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"author":21,"featured_media":97241,"parent":82,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"templates\/flatiron_collaborations.php","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","footnotes":""},"class_list":["post-101927","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":{"block_editor":[{"acf_fc_layout":"image","type":"image_generic","image":97241,"title":"","caption":"","caption_full":"","less_margin":false},{"acf_fc_layout":"text","text":"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."},{"acf_fc_layout":"title_text","title":"Machine Learning Visiting Scholar Program","large_title":true,"border_under_title":false,"text":"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.\r\n\r\nThe 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\u2019s sabbatical leave policies. Visiting scholars will be offered subsidized housing, salary and benefits as appropriate, travel and other research-related expenses.","border_top":true,"margin_top":true},{"acf_fc_layout":"three_column_text","title":"Researchers in CCM","smaller_title":false,"alt_display":false,"column_number":"Three","introduction":"","column":[{"title":"Lawrence Saul<\/u><\/a>","has_image":false,"image":null,"copy":"Group Leader, Machine Learning, CCM
\r\nAreas of Interest:<\/b> High dimensional data analysis, latent variable models, deep learning, variational inference, kernel methods","link":""},{"title":"Alberto Bietti<\/u><\/a>","has_image":false,"image":null,"copy":"Research Scientist, CCM
\r\nAreas of Interest:<\/b> Learning theory, optimization, deep learning, kernel methods","link":""},{"title":"Joan Bruna<\/u><\/a>","has_image":false,"image":null,"copy":"Visiting Scholar, CCM
\r\nAreas of Interest:<\/b> Learning theory, deep learning, machine learning for science, high dimensional statistics, algorithms\r\n","link":""},{"title":"Diana Cai<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCM
\r\nAreas of Interest:<\/b> Probabilistic modeling, robust Bayesian inference, machine learning for science\r\n","link":""},{"title":"Jeremy Cohen<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCM
\r\nAreas of Interest:<\/b> optimization for deep learning, science of deep learning","link":""},{"title":"Nikhil Ghosh<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCM
\r\nAreas of Interest:<\/b> Deep Learning, Optimization","link":""},{"title":"Mark Goldstein<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCM
\r\nAreas of Interest:<\/b> generative modeling, causality, machine learning for physical sciences","link":""},{"title":"Daniel D. Lee<\/u><\/a>","has_image":false,"image":null,"copy":"Visiting Scholar, CCM
\r\nAreas of Interest:<\/b> High dimensional statistics and geometry, computational neuroscience, robotics","link":""},{"title":"Tetiana Parshakova<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCM
\r\nAreas of Interest:<\/b> Convex Optimization, Deep Learning, Numerical Linear Algebra","link":""},{"title":"Soledad Villar<\/u><\/a>","has_image":false,"image":null,"copy":"Visiting Scholar, CCM
\r\nAreas of Interest:<\/b> Learning theory, deep learning, equivariant machine learning, graph neural networks, machine learning for science, high dimensional probability, optimization.","link":""},{"title":"Denny Wu<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCM
","link":""},{"title":"Luhuan Wu<\/u><\/a>","has_image":false,"image":null,"copy":"Associate Research Scientist, CCM
\r\nAreas of Interest:<\/b> Probabilistic modeling, generative modeling, approximate inference, and biophysical applications.","link":""}]},{"acf_fc_layout":"three_column_text","title":"Researchers in CCN","smaller_title":false,"alt_display":false,"column_number":"Three","introduction":"","column":[{"title":"Eero Simoncelli<\/u><\/a>","has_image":false,"image":null,"copy":"Director, CCN
\r\nAreas of Interest:<\/b> Analysis and representation of visual information in biological and artificial networks. Coding and inference","link":""},{"title":"Abdul Canatar<\/u><\/a> ","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCN
\r\nAreas of Interest:<\/b> Physics of learning, theoretical neuroscience, geometry of high-dimensional representations","link":""},{"title":"Mitya Chklovskii<\/u><\/a>","has_image":false,"image":null,"copy":"Group Leader, Neural Circuits and Algorithms, CCN
\r\nAreas of Interest:<\/b> Theoretical neuroscience, connectomics, biologically inspired AI, dynamics and control","link":""},{"title":"Chi-Ning Chou<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCN
\r\nAreas of Interest:<\/b> Computational neuroscience, high-dimensional geometry and statistics, science of deep learning","link":""},{"title":"SueYeon Chung<\/u><\/a>","has_image":false,"image":null,"copy":"Project Leader, Geometric Data Analysis, CCN
\r\nAreas of Interest:<\/b> Theoretical neuroscience, statistical physics of learning, high dimensional geometry and statistics","link":""},{"title":"Xuehao Ding<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCN
\r\nAreas of Interest:<\/b> NeuroAI, computational neuroscience, physics of intelligence","link":""},{"title":"Sarah Harvey","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCN
\r\nAreas of Interest:<\/b> Theoretical neuroscience, statistical physics, ML methods for neural data analysis","link":""},{"title":"Philip Kidd<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCN
\r\nAreas of Interest:<\/b> Animal behavior, dynamical systems, optimal prediction and control","link":""},{"title":"Sebastian Lee<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCN
\r\nAreas of Interest:<\/b> Continual learning, reinforcement learning, NeuroAI","link":""},{"title":"Josh Pugh-Sanford<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCN
\r\nAreas of Interest:<\/b> Dynamical systems theory and interpretable models of learning","link":""},{"title":"Yuhai Tu<\/u><\/a>","has_image":false,"image":null,"copy":"Senior Research Scientist, CCN
\r\nAreas of Interest:<\/b> Statistical physics, molecular\/cellular biology, neuroscience, and machine learning","link":""},{"title":"Alex Williams<\/u><\/a>","has_image":false,"image":null,"copy":"Associate Research Scientist, Statistical Analysis of Neural Data, CCN
\r\nAreas of Interest:<\/b> Unsupervised learning, uncertainty quantification in deep learning, topological data analysis, covariance estimation","link":""}]},{"acf_fc_layout":"three_column_text","title":"Researchers in CCQ","smaller_title":false,"alt_display":false,"column_number":"Three","introduction":"","column":[{"title":"Antoine Georges<\/u><\/a>","has_image":false,"image":null,"copy":"Director, CCQ
\r\nAreas of Interest:<\/b> Machine learning for quantum systems; fermionic neural quantum states; machine learning for \r\nquantum embedding solvers; hamiltonian learning quantum simulators","link":""},{"title":"Andrew Millis<\/u><\/a>","has_image":false,"image":null,"copy":"Co-Director, CCQ
\r\nAreas of Interest:<\/b> Machine learning for quantum embedding solvers; discovery of quantum correlations; ML for data analysis","link":""},{"title":"Miguel Morales<\/u><\/a>","has_image":false,"image":null,"copy":"Research Scientist, CCQ
\r\nAreas of Interest:<\/b> Neural quantum states, variational Monte Carlo, strong correlations, ab-initio electronic structure.","link":""},{"title":"Alev Orfi","has_image":false,"image":null,"copy":"Graduate Student, CCQ
\r\nAreas of Interest:<\/b> Neural quantum states, variational Monte Carlo, sampling algorithms","link":""},{"title":"Olivier Parcollet<\/u><\/a>","has_image":false,"image":null,"copy":"Senior Research Scientist, CCQ
\r\nAreas of Interest:<\/b> Machine learning for the quantum many-body problem, quantum impurity solvers.","link":""},{"title":"Ina Park<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCQ
\r\nAreas of Interest:<\/b> Machine learning for many-body problem and quantum materials, first-principles calculation, and impurity solver. Materials database","link":""},{"title":"Riccardo Rende<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCQ
\r\nAreas of Interest:<\/b> Transformers, Feature Learning, Neural Quantum States, Variational Monte Carlo","link":""},{"title":"Christopher Roth<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCQ
\r\nAreas of Interest:<\/b> Neural quantum states, superconductivity, quantum spin liquids, variational Monte Carlo","link":""},{"title":"Anirvan Sengupta<\/u><\/a>","has_image":false,"image":null,"copy":"Visiting Scholar, CCQ
\r\nAreas of Interest:<\/b> Representation learning, dynamics and control, applications to quantum systems, systems neuroscience","link":""},{"title":"Conor Smith","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCQ
\r\nAreas of Interest:<\/b> Neural Quantum States, Attention Mechanisms, Strongly Correlated Systems, 2D Materials","link":""},{"title":"Miles Stoudenmire<\/u><\/a>","has_image":false,"image":null,"copy":"Research Scientist, CCQ
\r\nAreas of Interest:<\/b> Novel tensor-based algorithms and architectures. Applications to natural sciences, dynamics and optimization","link":""},{"title":"Jaylyn C. Umana","has_image":false,"image":null,"copy":"Graduate Student, CCQ
\r\nAreas of Interest:<\/b> Symbolic regression, neural networks, optimization","link":""},{"title":"Agnes Valenti","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCQ
\r\nAreas of Interest:<\/b> Neural quantum states; Variational Monte Carlo; Training dynamics","link":""},{"title":"Roeland Wiersema","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCQ
\r\nAreas of Interest:<\/b> Neural quantum states, riemannian optimization, quantum information","link":""},{"title":"Shiwei Zhang<\/u><\/a>","has_image":false,"image":null,"copy":"Senior Research Scientist, CCQ
\r\nAreas of Interest:<\/b> 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. ","link":""}]},{"acf_fc_layout":"three_column_text","title":"Researchers in CCB","smaller_title":false,"alt_display":false,"column_number":"Three","introduction":"","column":[{"title":"Olga Troyanskaya<\/u><\/a>","has_image":false,"image":null,"copy":"Deputy Director for Genomics, CCB
\r\nAreas of interest:<\/b> Genomics and bioinformatics","link":""},{"title":"Lisa M. Brown<\/u><\/a>","has_image":false,"image":null,"copy":"Senior Data Scientist, CCB
\r\nAreas of Interest: Computer Vision, in particular, segmentation, isotropic reconstruction, registration, tracking and lineage construction\r\nfor 3D microscopy","link":""},{"title":"Xi Chen<\/u><\/a>","has_image":false,"image":null,"copy":"Research Scientist, CCB
\r\nAreas of Interest:<\/b> Distribution learning, Markov chain Monte Carlo, semi-supervised learning","link":""},{"title":"Hayden Nunley<\/u><\/a>","has_image":false,"image":null,"copy":"Associate Research Scientist, CCB
\r\nAreas of Interest:<\/b> Machine learning for computer vision: segmentation and lineage construction, modeling of cell lineage trees","link":""},{"title":"Christopher Park<\/u><\/a>","has_image":false,"image":null,"copy":"Research Scientist, CCB
\r\nAreas of Interest:<\/b> Probabilistic modeling, deep learning and statistical genetics","link":""},{"title":"Natalie Sauerwald<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCB
\r\nAreas of Interest:<\/b> Machine learning for genomics and genetics, optimization, interpretable models ","link":""},{"title":"Rachel Sealfon<\/u><\/a>","has_image":false,"image":null,"copy":"Research Scientist, CCB
\r\nAreas of Interest:<\/b> Machine learning for genomics, analysis of functional genomic data","link":""}]},{"acf_fc_layout":"three_column_text","title":"Researchers in CCA","smaller_title":false,"alt_display":false,"column_number":"Three","introduction":"","column":[{"title":"Shirley Ho<\/u><\/a>","has_image":false,"image":null,"copy":"Group Leader, Cosmology X Data Science, CCA
\r\nAreas of Interest:<\/b> Machine learning for science, deep learning for simulation, neuro-symbolic models, high dimensional inference","link":""},{"title":"Lucia Perez<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCA
\r\nAreas of Interest:<\/b> simulation-based inference, machine learning for science, accelerated forward modeling of galaxies","link":""},{"title":"Francisco Villaescusa-Navarro<\/u><\/a>","has_image":false,"image":null,"copy":"Research Scientist, CCA
\r\nAreas of Interest:<\/b> Neuro-simulations, graph neural netwoks, likelihood-free inference, manifold learning, generative models, symmetries for deep learning. ","link":""},{"title":"Helen Qu<\/u><\/a>","has_image":false,"image":null,"copy":"Flatiron Research Fellow, CCA
\r\nAreas of Interest:<\/b> Machine learning for science, reinforcement learning, self-supervised learning, physics of learning.","link":""}]}]},"_links":{"self":[{"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/pages\/101927"}],"collection":[{"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/comments?post=101927"}],"version-history":[{"count":120,"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/pages\/101927\/revisions"}],"predecessor-version":[{"id":136539,"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/pages\/101927\/revisions\/136539"}],"up":[{"embeddable":true,"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/pages\/82"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/media\/97241"}],"wp:attachment":[{"href":"https:\/\/www.simonsfoundation.org\/wp-json\/wp\/v2\/media?parent=101927"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}