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Regular Members

Member Institution Research Areas
Tatiana Acero-Cuellar University of Delaware machine learning, computer vision, transient and variable phenomena
Ethan Anderes UC Davis Theoretical and applied methods for spatial statistics
James Annis Fermilab Bayesian inference, deep learning
Alejandra Munozar Arancibia IFA-UV  
Eric Aubourg CNRS/IN2P3/APC Paris Deep learning, Bayesian methods, Image processing
Luis Ricardo Arantes Filho Lab for Computing and Applied Math - INPE Deep learning, supernovae
G. Jogesh Babu Penn State University Statistical methods and theory
Arash Bahramian Curtin Institute of Radio Astronomy Bayesian inference, Time series analysis
Anastasia Baluta Lomonosov Moscow State University supernovae, anomaly detection
Luan Orion Baraúna Lab for Computing and Applied Math - INPE Deep Learning, Time Series prediction and classification, Computer Vision, Radio Transients
Jacek Becla SLAC Systems for managing extreme-scale data sets
Wilson Beebe University of Washington / LINCC Frameworks Software engineering
Saptashwa Bhattacharyya University of Nova Gorica Deep Learning, Computer Vision
Federica Bianco University of Delaware Transients and variable stars
Bryce Bolin Goddard Space Flight Center Deep learning
Adam Bolton NSF’s NOIRLab Strong lensing, spectroscopic surveys, end-to-end data systems
Kirk Borne Booz Allen Large databases, data mining, knowledge discovery
Alexandre Boucaud IN2P3/APC Image processing, deep learning, mlops
Doug Branton University of Washington Software Engineering, Data Science
Robert Brunner University of Illinois Observational cosmology, transient and variable phenomena
James Buchanan Lawrence Livermore National Laboratory Bayesian inference, weak lensing, galaxy detection and deblending
Tamas Budavari Johns Hopkins University Computational statistics, low-dimensional embeddings, scientific databases
Douglas Burke Harvard CFA Galaxy clusters, galaxy evolution, virtual observatory, semantic astronomy
Nathaniel Butler Arizona State University Astrophysical transients
Juan Cabral CIFASIS, IATE, CONICET Classification Methods, machine learning, software engeenering
Guillermo Cabrera Millenium Institute of Astrophysics Machine learning, computer vision, data-science, astroinformatics
Jean-Eric Campagne CNRS/IN2P3/IJCLab Deep Learning, auto-diff with JAX
Sandro Campos Carnegie Mellon University Software engineering, machine learning
Siddharth Chaini University of Delaware Deep Learning, Distance Metrics
Yen-Chi Chen University of Washington Nonparametric statistics, cluster analysis, statistical learning theory, large scale structure
David Chernoff Cornell University Cosmology, statistics and numerical methods for physics
Aleksandra Ciprijanovic Fermilab Deep learning, domain adaptation, algorithm robustness and uncertainties
Jessica Cisewski Kehe University of Wisconsin - Madison Statistical methods, topological data, approximate Bayesian computation
James Cordes Cornell University Radio astronomy, neutron stars, pulsars, signal processing techniques
John Franklin Crenshaw University of Washington Deep learning, photometric redshifts
Shar Daniels University of Delaware deep learning, anomaly detection
Melissa DeLucchi Carnegie Mellon University High performance data analysis
Mariano Dominguez IATE Galactic and extragalactic astronomy
Cyrille Doux CNRS/IN2P3 Cosmology, Bayesian Inference, Deep Learning
Jordan Dowdy Bellarmine University Algorithm development, Phosim
George Djorgovski Caltech Computational, data-intensive science, development of cyberinfrastructure
Marina Dunn University of California, Riverside / Lawrence Livermore National Laboratory Machine Learning/Deep Learning, Bayesian methods, galaxy morphology
Pedro Antonio Escarate Monetta Universidad Técnica Federico Santa María Electro-optics systems, spectroscopy, astronomical instrumentation
Susana Eyheramendy Pontifica Universidad Catolica de Chile Methods for Big Data
Maria Luiza Falci Universidade Federal Fluminense  
Eric Feigelson Penn State University X-ray studies of star formation, cross-disciplinary astrostatistics
Agnès Ferté SLAC/KIPAC Bayesian inference, dimensionality reduction
Karin S. F. Fornazier Guimaraes Instituto de Física da Universidade de São Paulo Bayesian inference, machine learning
Francisco Forster Center for Mathematical Modelling / Millennium Institute for Astrophysics, Chile Time series classification
Peter Freeman Carnegie Mellon University Implementation of statistical methods in astronomy
Shih Ching Fu Curtin University Bayesian inference, Gaussian Processes, Time series
Alex Gagliano University of Illinois, Urbana-Champaign Classification, generative models, simulation-based inference
Christopher Genovese Carnegie Mellon University Statistical methods and theory, nonparametric methods
Aritra Ghosh Yale University Deep learning, uncertainty quantification
Karl Glazebrook Swinburne University of Technology Observational cosmology, the formation and evolutionary history of galaxies
Matthew Graham Caltech Data representation, visualization, storage, interpretation
Alexander Gray IBM Scalable machine learning
Carlo Graziani University of Chicago  
Julia Gschwend LIneA photometric redshifts, object classification, machine learning
Leanne Guy AURA/LSST Mining alert stream and catalogues
Jon Hakkila College of Charleston Gamma ray bursts
Nina Hernitschek Vanderbilt University Large time-domain data sets, classifcation of variable sources
Daniel Hestroffer Paris Observatory / PSL Bayesian inference; regression; parameter estimation
Timothy Holt Southwest Research Institute Solar system body analysis
Jianhua Huang Texas A&M Gaussian process, functional data analysis, spatial temporal statistics, Bayesian statistics
Amr Ibrahim Laboratorio Interinstitucional de e-Astronomia (LIneA) Data science, informatics, programming
Emille Ishida CNRS/LPC-Clermont Anomaly detection, active learning
Zeljko Ivezic University of Washington Large survey astronomy
Rafael Izbicki Federal University of Sao Carlos Nonparametric methods, high-dimensional inference
Allan Jackson Rubin DP0.2 delegate LLM, deep learning, K-means
Andrew Jaffe Imperial College Statistical cosmology, testing cosmological theories, Bayesian methods
Daniel Leonardo Jasbick Laboratório Interinstitucional de e-Astronomia (LineA) / Universidade Federal Fluminense Data provenance, scientific workflows
Elise Jennings Argonne National Laboratory Computational cosmology, large scale structure
Andres Jordan Pontifica Universidad Catolica de Chile Exoplanets, early-type galaxies
Bryce Kalmbach University of Washington Machine learning, photometric redshifts
Vinay Kashyap Harvard CFA  
Somayeh Khakpash Rutgers University Microlensing , Time Series Classification, Machine Learning
Kevin Knuth University at Albany Information physics, Bayesian data analysis, source separation
Sthabile Kolwa University of Johannesburg Unsupervised learning for anomaly detection; Bayesian inference; source classification
Simon Krughoff LSST/AURA Astronomical survey planning, data reduction, simulation
Jeremy Kubica Carnegie Mellon University Software engineering, machine learning, algorithms
Hermine Landt Durham University Time series, Gaussian processes
Francois Lanusse CNRS/INSU Weak gravitational lensing, deep learning, generative modeling
Marcelo Lares IATE Astrostatistics, data pipelines, visualization
Ilin Lazar University of Hertfordshire Galaxy classification using unsupervised machine learning
Ann Lee Carnegie Mellon University Statistical and machine learning methods, high-dimensional data
Chris Lintott University of Oxford Machine learning, citizen science, serendipity
Xin Liu UIUC AI for Astronomy, Astronomy for AI, Foundation models
Michelle Lochner University of the Western Cape/ South African Radio Astronomy Observatory unsupervised learning, radio galaxies, transients
James Long MD Anderson Cancer Center Machine learning, signal frequency estimation, measurement error models, functional data analysis
Thomas Loredo Cornell University Statistical methods, Bayesian analysis, high energy astrophysics
Olivia Lynn Carnegie Mellon University / LINCC Frameworks Software engineering
Ashish Mahabal Caltech Astrophysical transients
Konstantin Malanchev University of Illinois Urbana-Champaign anomaly detection, light-curve feature extraction, light-curve classification
Alex Malz Carnegie Mellon University Uncertainty quantification and propagation, experimental design and metrics
Kaisey Mandel University of Cambridge Supernova cosmology, time domain and transient astronomy, Bayesian modelling
Luca Masserano Carnegie Mellon University Likelihood-Free Inference, Deep Learning, Uncertainty Quantification
Justyn Maund Unversity of Sheffield Massive stars to supernovaes
Juan Carlos Maureira Universidad de Chile Discrete systems simulation
Francesca Mauro MAS/Universidad de Concepcion  
Jon McAuliffe University of California, Berkeley Machine learning, statistical prediction, variational inference
Bruce McCollum Caltech  
Jason McEwen University College London Harmonic and wavelet transforms, compressed sensing, Bayesian inference
Summer McLaughlin University of Sheffield Classification methods, Gaussian Processes, Bayesian statistics
Simona Mei Universite de Paris Large-scale structure, galaxy evolution
Ismael Mendoza University of Arizona Bayesian Inference, Deep Generative Models, Galaxy Blending
Christopher Miller University of Michigan Large-scale structure, galaxy clusters, astroinformatics
Anais Moller CNRS / LPC Clermont-Ferrand Reproducibility, statistical coherence, transient classification
Arrykrishna Mootoovaloo University of Oxford Bayesian methods, deep learning, data compression
Marcelo Mora Pontifica Universidad Catolica de Chile Galaxy formation
Daniel Mortlock Imperial College London Bayesian inference applied to astronomy
Roberto Pablo Munoz Pontifica Universidad Catolica de Chile Formation and evolution of galaxies in high-density environments
Craig Pellegrino University of Virginia Classification methods
Silvia Pietroni INAF-OAC classification methods, machine learning, monitoring and data analyzes
Agnieszka Pollo NCBJ & UJ Poland classification, regression, deep learning, anomaly search
Becky Nevin Fermilab Deep learning, Bayesian inference, uncertainty in ML
Bob Nichol ICG Portsmouth Large-scale structure, supernovae, advanced statistical methods
Brian Nord Fermilab and University of Chicago Simulation-based inference, uncertainty quantification, deep learning
Franc O Northeastern University Classification Methods, Geometric Deep Learning
Drew Oldag University of Washington / LINCC Frameworks LINCC Frameworks
Daniel de Oliveira Universidade Federal Fluminense Databases, distributed systems, scientific workflows
Giuliano Pignata Universidad Andres Bello Supernovae, cosmology, solar system
Kara Ponder SLAC SN cosmology, Classification and Follow-up for Transients, Bayesian inference
Maria Pruzhinskaya Laboratoire de Physique de Clermont, IN2P3/CNRS supernovae, anomaly detection
Andrew Ptak NASA GSFC Extragalatic X-ray astrophysics, software development
Troy Raen University of Pittsburgh Classification methods
Fernando Rannou Universidad de Santiago de Chile Image Synthesis, Big Data, Big Compute, ML
Markus Michael Rau Argonne National Laboratory Inverse problems, spatial statistics, machine learning
Umaa Rebbapragada NASA JPL Astronomical optical transient vetting, active learning
Karthik Reddy University of Maryland, Baltimore County Extragalactic jets, X-ray and radio astronomy
Jeffrey Regier University of Michigan Bayesian inference, deep learning, deblending
Eniko Regos Konkoly Large scale structure
Benjamin Remy CEA Paris-Saclay Bayesian Inference, Machine Learning, Generative Models
Giuseppe Riccio INAF - Astronomical Observatory of Capodimonte (Napoli - Italy) machine learning, web applications, software developer
Gordon Richards Drexel University Extragalactic astrophysics, AGN
Joseph Richards GE Digital Statistical and machine learning methods for noisy, high-dimensional data
Mickael Rigault CNRS/IN2P3 Forward Modeling, GPU pipeline
Thales Rodrigues Universidade Federal de Juiz de Fora Deep learning, self-attention, computational physics
Reinaldo Rosa Lab for Computing and Applied Math - INPE -MCTI-Brazil Data cubes, machine learning, gradient pattern analysis
David Ruppert Cornell University Functional data analysis, measurement error models, semi-parametric methods, time series; for variable stars and extragalactic astronomy
Vitor Sampaio Universidade Cidade de São Paulo Galaxy evolution, bayesian inference, deep learning
Paula Sanchez Saez Millenium Institute of Astrophysics / Pontificia Universidad Classification of time series
Nikolina (Niko) Sarcevic Newcastle University Bayesian inference
Rubens Sautter Lab for Computing and Applied Math - INPE Pattern recognition, statistics, data mining
Jeffrey Scargle NASA Ames High-energy astrophysics, time series and image analysis
Chad Schafer Carnegie Mellon University Statistical methods, cosmological parameter estimation
Samuel Schmidt UC Davis Photometric redshifts
Nima Sedaghat University of Washington / LSST Deep learning, computer vision, transient detection
Lior Shamir Lawrence Tech Image analysis, galaxy morphology
Raphael Shirley University of Southhampton Bayesian inference, image classification
Aneta Siemiginowska Harvard CFA Supermassive black holes, quasars and active galaxies
Heloisa da Silva Mengisztki Laboratório Interinstitucional de e-Astronomia Photometric redshifts, object classification, machine learning
Colin Slater University of Washington Image differencing algorithms, Milky Way structure
Joshua Speagle University of Toronto Uncertainties in machine learning, scalable inference
Jennifer Sobeck University of Washington Large survey datasets and databases, data mining, stellar populations
Aleksandra Solarz National Center for Nuclear Research Classification
Niharika Sravan Drexel University reinforcement learning, design of experiments
Keivan Stassun Vanderbilt University Formation of stars and planetary systems
Fiorenzo Stoppa Radboud University Statistics, Astrostatistics, Uncertainties characterization in ML
Hyungsuk Tak University of Notre Dame Time series. image data analysis, Bayesian hierarchical modeling
Jefferson Toledo Universidade Federal da Paraiba Ensemble methods, time series analysis, deep learning
Martin Topinka OA Cagliari  
Tilman Troester ETH Zurich Spatial statistics, Bayesian inference in high dimensions, deep learning
Eleni Tsaprazi Imperial College London Bayesian inference, field-level inference, high-order statistics
Anke van Dyk South African Astronomical Observatory Stellar population studies, inference, capture-recapture
Ricardo Vilalta University of Houston Pattern recognition, data mining, artificial intelligence
Ashley Villar Harvard University Transient classification, hierarchical modelling
Lucianne Walkowicz Adler Planetarium Discovery of unusual events, stellar magnetic activity
John Wallin Middle Tennesee State Univ. Gravitational interactions
Sam Ward Institute for Astronomy, University of Cambridge Bayesian Inference, Type Ia supernovae cosmology
Larry Wasserman Carnegie Mellon University Statistical methods and theory
Martin Weinberg University of Massachusetts Galaxy dynamics, simulation models
Robert Wolpert Duke University Statistical methods and theory
Xiaomeng Yan Texas A&M University Time series classification
Ilsang Yoon NRAO Classification and characterization of AGN
Justine Zeghal APC CNRS Deep learning, bayesian inference, implicit inference (or likelihood-free inference / simulation-based inference)
Yuanyuan Zhang NSF’s NOIRLab Bayesian inference, deep learning

Affiliate Members

Member Institution
Stephen Bailey Lawrence Berkeley Laboratory
Niel Brandt Penn State University
Salman Habib Argonne National Laboratory
Lynne Jones University of Washington
Kian-Tat Lim SLAC
Phil Marshall KIPAC
Jeffrey Newman University of Pittsburgh
Joshua Pepper Lehigh University
Cathy Petry AURA/LSST
Jonathan Sick LSST
Michael Strauss Princeton University
Jon Thaler University of Illinois