Members

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Regular Members

Member Institution Research Areas
Ethan Anderes UC Davis
Theoretical and applied methods for spatial statistics
James Annis Fermilab Bayesian inference, deep learning
Luis Ricardo Arantes Filho Lab for Computing and Applied Math - INPE Deep learning, supernovae
G. Jogesh Babu Penn State University Statistical methods and theory
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
Federica Bianco University of Delaware Transients and variable stars
Kirk Borne Booz Allen Large databases, data mining, knowledge discovery
Alexandre Boucaud IN2P3/APC Image processing, deep learning, mlops
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
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
Melissa DeLucchi Carnegie Mellon University High performance data analysis
Mariano Dominguez IATE Galactic and extragalactic astronomy
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
Eric Feigelson Penn State University X-ray studies of star formation, cross-disciplinary astrostatistics
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
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  
Leanne Guy AURA/LSST Mining alert stream and catalogues
Nina Hernitschek Vanderbilt University Large time-domain data sets, classifcation of variable sources
Jon Hakkila College of Charleston Gamma ray bursts
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
Zeljko Ivezic University of Washington Large survey astronomy
Rafael Izbicki Federal University of Sao Carlos Nonparametric methods, high-dimensional inference
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  
Kevin Knuth University at Albany Information physics, Bayesian data analysis, source separation
Simon Krughoff LSST/AURA Astronomical survey planning, data reduction, simulation
Hermine Landt Durham University Time series, Gaussian processes
Francois Lanusse University of California, Berkeley Weak gravitational lensing, deep learning, sparsity based methods
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
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
Ashish Mahabal Caltech Astrophysical transients
Alex Malz New York University Uncertainty quantification and propagation, experimental design and metrics
Kaisey Mandel University of Cambridge Supernova cosmology, time domain and transient astronomy, Bayesian modelling
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
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
Alejandra Munozar Arancibia IFA-UV  
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
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
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
Gordon Richards Drexel University Extragalactic astrophysics, AGN
Joseph Richards GE Digital Statistical and machine learning methods for noisy, high-dimensional data
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
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
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
Colin Slater University of Washington Image differencing algorithms, Milky Way structure
Jennifer Sobeck University of Washington Large survey datasets and databases, data mining, stellar populations
Aleksandra Solarz National Center for Nuclear Research Classification
Keivan Stassun Vanderbilt University Formation of stars and planetary systems
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
Tilman Troester ETH Zurich Spatial statistics, Bayesian inference in high dimensions, deep learning
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 Columbia 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

 

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