| Member |
Institution |
Research Areas |
| Tatiana Acero-Cuellar |
University of Delaware |
machine learning, computer vision, transient and variable phenomena |
| Patrick Aleo |
UIUC |
Anomaly Detection, Supernova Classification, Similarity Search |
| 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 |
| Matthew Becker |
Argonne National Laboratory |
differentiable simulation-based inference/modeling |
| 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 |
| Alex Broughton |
SLAC/Stanford |
Deep learning, data management, anomaly detection |
| 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 |
| Gabriella Contardo |
SISSA |
Data mining, machine learning |
| James Cordes |
Cornell University |
Radio astronomy, neutron stars, pulsars, signal processing techniques |
| John Franklin Crenshaw |
University of Washington |
Deep learning, photometric redshifts |
| Mi Dai |
University of Pittsburgh |
|
| Shar Daniels |
University of Delaware |
deep learning, anomaly detection |
| Rafael S. de Souza |
University of Hertfordshire |
Bayesian Inference, deep learning, information theory |
| Melissa DeLucchi |
Carnegie Mellon University |
High performance data analysis |
| Boris Demkov |
SNAD project member |
classification methods, anomaly detection, clustering |
| Biprateep Dey |
University of Pittsburgh |
Uncertainty Quantification, Deep Learning |
| Steven Dillman |
Stanford University |
Machine Learning, Citizen Science, Transients |
| 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 |
| Willow Fortino |
University of Delaware |
transformers, generative AI, classification methods |
| 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 |
| Emmanuel Gangler |
LPC-IN2P3 |
Anomaly searches, light-curves, Type Ia supernovae |
| Manuel Garcia-Fernandez |
Universidad Europea de Madrid |
computer vision, machine-learning, big-data |
| 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 |
| Alan Heavens |
Imperial College London |
Bayesian inference, simulation-based inference |
| 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 |
| Lindsay House |
The University of Texas Austin |
classification methods, dimensionality reduction, citizen science |
| 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 |
| Wassim Kabalan |
CNRS/APC |
Bayesian Inference, deep learning, accelerated computing |
| Bryce Kalmbach |
University of Washington |
Machine learning, photometric redshifts |
| Tanveer Karim |
University of Toronto |
Cosmology, Multiprobe Analysis, Bayesian inference |
| Sergey Karpov |
FZU - Institute of Physics, Czech Academy of Sciences |
|
| Vinay Kashyap |
Harvard CFA |
|
| Somayeh Khakpash |
Rutgers University |
Microlensing , Time Series Classification, Machine Learning |
| Ashod Khederlarian |
University of Pittsburgh |
Deep Learning, Photometric Redshifts |
| 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 |
| Harish Krishnakumar |
Princeton University |
deep learning, classification methods |
| 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 |
| Anastasia Lavrukhina |
Lomonosov Moscow State University |
Classification methods, anomaly detection, deep learning |
| Ilin Lazar |
University of Hertfordshire |
Galaxy classification using unsupervised machine learning |
| Ann Lee |
Carnegie Mellon University |
Statistical and machine learning methods, high-dimensional data |
| Boris Leistedt |
Imperial College London |
cosmology (3x2pt, LSS, photo-z’s), astro-statistics, deep learning |
| 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 |
|
| Jeremy McCormick |
SLAC National Accelerator Laboratory |
Data Engineering, Big Data, IVOA Standards |
| Jason McEwen |
University College London |
Harmonic and wavelet transforms, compressed sensing, Bayesian inference |
| Sean McGuire |
Carnegie Mellon University / LINCC Frameworks |
Software engineering, Large scale data analysis |
| Summer McLaughlin |
University of Sheffield |
Classification methods, Gaussian Processes, Bayesian statistics |
| Simona Mei |
Universite de Paris |
Large-scale structure, galaxy evolution |
| Aaron Meisner |
NSF’s NOIRLab |
data-intensive science, anomaly detection |
| 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 |
| Krzysztof Nawrocki |
National Centre for Nuclear Research (NCBJ) |
deep learning, machine learning, object classification |
| 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 |
| William O’Mullane |
Rubin Observatory |
statistical methods for large or streaming data |
| 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 |
| Giuliano Pignata |
Universidad Andres Bello |
Supernovae, cosmology, solar system |
| Agnieszka Pollo |
NCBJ & UJ Poland |
classification, regression, deep learning, anomaly search |
| 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 |
| Lucas Pulgar-Escobar |
Universidad de Concepción |
Bayesian inference, large dataset processing, classification methods |
| Troy Raen |
University of Pittsburgh |
Classification methods |
| Fernando Rannou |
Universidad de Santiago de Chile |
Image Synthesis, Big Data, Big Compute, ML |
| Conor Ransome |
Harvard |
classification methods, transients |
| Markus Michael Rau |
Newcastle University |
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 |
| Timofey Semenikhin |
Lomonosov Moscow State University, Sternberg astronomical institute |
neural networks, computer vision |
| Lior Shamir |
Lawrence Tech |
Image analysis, galaxy morphology |
| Xinyue Sheng |
Queen’s University, Belfast |
classification methods, deep learning |
| 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 |
| Frank Soboczenski |
University of York & King’s College London |
Deep Learning, Large Language Models, Gaussian Processes |
| Aleksandra Solarz |
National Center for Nuclear Research |
Classification |
| Niharika Sravan |
Drexel University |
reinforcement learning, design of experiments |
| Sreevarsha Sreejith |
University of Surrey |
Deep learning, Variational inference, Machine learning in Astronomy |
| Keivan Stassun |
Vanderbilt University |
Formation of stars and planetary systems |
| Connor Stone |
Université de Montréal |
forward model photometry, machine learning, galaxies |
| Fiorenzo Stoppa |
Radboud University |
Statistics, Astrostatistics, Uncertainties characterization in ML |
| Hyungsuk Tak |
University of Notre Dame |
Time series. image data analysis, Bayesian hierarchical modeling |
| Michael Tauraso |
University of Washington |
Bayesian Inference, Machine Learning |
| 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 |
| Anastasios (Andy) Tzanidakis |
University of Washington |
Stellar variability, time-domain, astrostatistics |
| 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 |
| Max West |
University of Washington / LINCC Frameworks |
software engineering, astronomical survey data |
| Tom J. Wilson |
University of Exeter |
Astrostatistics, counterpart assignment, cross-matching |
| Robert Wolpert |
Duke University |
Statistical methods and theory |
| John Wu |
STScI |
Machine learning, Galaxies, Large Scale Structure |
| Huangfei Xiao |
Florida State University |
Bayesian inference; Machine learning |
| 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) |
| Yikun Zhang |
University of Washington |
Nonparametric statistics, optimization on manifolds, large scale structure |
| Yuanyuan Zhang |
NSF’s NOIRLab |
Bayesian inference, deep learning |
| Conghao Zhou |
UC Santa Cruz |
Galaxy clusters |