Members

If you are interested in joining the LSST ISSC, please see the Apply page.

Active Members

Member	Institution	Research Areas
Tatiana Acero-Cuellar	University of Delaware	data science, time series, ML, interpretability, information theory, Monte Carlo, interdisciplinary projects
Tatiana Acero-Cuellar	University of Delaware	data science, time series, ML, interpretability, information theory, Monte Carlo, interdisciplinary projects
Mojgan Aghakhanloo	University of Virginia	Bayesian inference
Michael Albrow	University of Canterbury	Probablistic generative models, Bayesian inference, image processing
Eric Aubourg	CNRS/IN2P3/APC Paris	Deep learning, Bayesian methods, Image processing
Maximilian Autenrieth	University of Cambridge	“Bayesian inference”, “Statistical machine learning”, “Causal inference”
Uzay Aydin	Erciyes University	Machine learning, Astrostatistics, AGN-Starburst with galaxy evolution
G. Jogesh Babu	PENN state University
Arash Bahramian	Curtin Institute of Radio Astronomy	Mechanistic models, non-parametric time-series analysis, Bayesian statistics
Ricardo Baptista	University of Toronto	1. Computational Bayesian Inference, 2. Probabilistic Machine Learning and Generative Models, 3. Inverse Problems and Data Assimilation
Matthew Becker	Argonne National Laboratory	HCM, bayesian inference, GPUs w/ JAX
Wilson Beebe	LINCC Frameworks
Saptashwa Bhattacharyya	University of Nova Gorica
Keerti Bhogaraju	Fortive	Machine learning, Deep learning, Generative AI
Federica Bianco	University of Delaware	anomaly detection
Matias Blaña	Universidad de La Serena	Expertiese:Dynamics, galaxy simulations. Interest: classification
Bryce Bolin	Eureka Scientific
Kirk Borne	Independent business owner: Data Leadership Group LLC	Machine Learning. Anomaly Detection. Galaxy evolution. Transients.
Alexandre Boucaud	APC – IN2P3
Micah Bowles	University of Oxford	deep learning, foundation models, embeddings
Niel Brandt	Penn State University	Fusion methods for AGN selection, AGN variability characterization
Alex Broughton	SLAC/Stanford	Deep learning, data management, anomaly detection
James Buchanan	Lawrence Livermore National Laboratory
Nat Butler	Arizona State University	Anomaly detection, uncertainties, transients
Jean-Eric Campagne	IJCLab CNRS & Paris-Saclay Univ.	JAX-Galsim
Sandro Campos	Carnegie Mellon University	Software engineering, machine learning
Natanael Cardoso	Universidade de São Paulo	machine learning, deep learning, computer vision
Christopher Carroll	Washington State University	Active galactic nuclei
Siddharth Chaini	University of Delaware	anomalies, deep learning, transients, time-domain astronomy
Gabriella Contardo	University of Nova Gorica	Anomaly Detection, Broker classification for TDE
John Franklin Crenshaw	University of Washington
Mi Dai	University of Pittsburgh	dark energy studies using Type Ia supernovae
Shar Daniels	University of Delaware
Melissa DeLucchi	Carnegie Mellon University	Software. Data.
Biprateep Dey	University of Toronto	Deep Learning, Uncertainty Quantification
Steven Dillmann	Stanford University
Mariano Dominguez	IATE-OAC-UNC	Bayesian inference, Likelihood free methods, Anomaly Detection, Foundational Models
Yize Dong	Harvard University	Data reduction, deep learning, transient astronomy
Andrew Engel	The Ohio State University	deep learning, cutout models
Carlos Eduardo Falandes	National Institute for Space Research (INPE)	Deep Learning, Galaxy Morphology Classification, Anomaly Detection in Astronomical Data
Angelo Fausti	AURA/Vera C. Rubin Observatory	Data engineering. Time-series data. Data streaming.
Eric Feigelson	Penn State University
Carlos Eduardo Ferreira Lopes	Atacama University - Chile	classification methods, deep learning, and detection methods
Peter Freeman	Carnegie Mellon University	Currently, as a teaching professor, I’m at best a peripheral member and am not actively working on methods/cases.
Shih Ching Fu	Curtin University
Alex Gagliano	IAIFI	Deep learning and generative AI for science
Emmanuel Gangler	LPCA - CNRS/IN2P3	Anomaly finding
Manuel Garcia-Fernandez	Universidad Europea de Madrid	computer vision, machine-learning, big-data
Aritra Ghosh	University of Washington
Margherita Grespan	Oxford University	deep learning, active learning, strong lensing
Leanne Guy	Rubin Observatory	Anomaly detection, Strong lensing, time series classification, visualization of large data sets, joint survey processing,
Alan Heavens	Imperial College London	I’m generally interested in Bayesian methods (BHMs, SBI), and hope to become more actively involved.
Peter Hoeflich	Florida State University, Dept. of Physics	Transient sciences, statistical and individual link between theory and observations
Lindsay House	The University of Texas Austin	classification methods, dimensionality reduction, citizen science
Arkadiusz Hypki	Adam Mickiewicz University	star clusters, machine learning, AI
Rafael Izbicki	Federal University of Sao Carlos	Simulation-based Inference, Conformal Inference, Calibration, Density Estimation
Allan Jackson	DP0-3 data delgate	Deep Learning in astrophysics, LLM assisted astrophysics coding
Wassim Kabalan	APC	weak lensing + full field inference
Bryce Kalmbach	SLAC	Machine Learning, Information Theory Applications
Pawan Kapur	University of Vaasa	Fuzzy Logic, Bayesian Deep learning, Time Series ML(LSTM)
Tanveer Karim	University of Toronto
Sergey Karpov	Institute of Physics, Czech Academy of Sciences
Vinay Kashyap	Center for Astrophysics | Harvard & Smithsonian	stellar flaring, gravitational lensing, time delay studies
Simran Kaur	University of Michigan	Bayesian Inference, Simulations
Sthabile Kolwa	UNISA (University of South Africa)	Unsupervised learning for anomaly detection; Bayesian inference; source classification
Jeremy Kubica	Carnegie Mellon University	Everything software related
Hermine Landt-Wilman	Durham University	ML, time-series analysis
Anastasia Lavrukhina	Lomonosov Moscow State University
David Law	LJMU	Deep reinforcement learning, data science, digital twins
Ilin Lazar	University of Hertfordshire
Boris Leistedt	Imperial College London	Image processing
Dani Leonard	Newcastle University	Bayesian inference, model misspecification, fast surrogate models
Michelle Lochner	University of the Western Cape	Machine learning, specifically anomaly detection and automated scientific discovery
Thomas Loredo	Cornell University	cosmic demographics (population modeling), time series (regression, classification), photometric redshifts, Bayesian methods, functional data analysis
Yufeng Luo	University of Wyoming, NOIRLab	deep learning, foundation models, classification methods
Olivia Lynn	Carnegie Mellon University / LINCC Frameworks	Software engineering
Delzeen Machhi	Massachusetts Institute of Technology	Deep learning, Generative AI applications, Optimization
Konstantin Malanchev	Carnegie Mellon University	Anomaly detection, similarity search, kernel estimators, uncertainties, differentiability
Alex Malz	Space Telescope Science Institute	Uncertainty quantification and propagation, experimental design and metrics
Jeremy McCormick	SLAC
Sean McGuire	Carnegie Mellon University / LINCC Frameworks	Software engineering, Large scale data analysis
Aaron Meisner	NOIRLab	data mining, rare object searches, image processing
Ismael Mendoza	University of Michigan	Differentiable forward models, JAX, GPUs, Bayesian methods
Ayan Mitra	NCSA, University of Illinois, Urbana Champaign, USA	classification methods, deep learning, symbolic regression
Daniel Mortlock	Imperial College London	Photometric redshifts
Alejandra Muñoz Arancibia	Millennium Institute of Astrophysics	Classification, anomaly detection
Anais Möller	Swinburne University of Technology
Lilianne Nakazono	Observatório Nacional	Statistics, Machine Learning, Deep Learning
Nicola Rosario Napolitano	University of Naples Federico II	Deep learning; galaxy structural parameters
Brian Nord	Fermilab	uncertainty quantification in AI
William O’Mullane	Vera C. Rubin Observatory	streaming solutions
Drew Oldag	LINCC Frameworks, University of Washington
Aarya Patil	Max Planck Institute for Astronomy	time-series analysis, Bayesian inference, machine learning
Karla Peña Ramirez	Vera C. Rubin Observatory	Unsupervised Learning for Anomaly Detection and Clustering.
Giuliano Pignata	Universidad de Tarapaca	Transient photometric classification
Andrés Plazas Malagón	SLAC / KIPAC / Stanford	dark energy and dark matter studies with lensing, astronomical instrumentation
Agnieszka Pollo	National Centre for Nuclear Research (Poland)	Clustering/classification algorithms, anomaly detection applied to galaxy science (properties and images).
Anna Preto	Laboratoire APC	Bayesian neural networks; Cosmic shear analysis
Maria Pruzhinskaya	Université Clermont Auvergne, LPCA, IN2P3/CNRS	Anomaly Detection
Kangming Pu	Monash university	moving object detection; synthetic tracking
Fernando Rannou	Universidad de Santiago de Chile	Machine learning for Anomaly detection
Conor Ransome	Harvard
Jeffrey Regier	University of Michigan	Bayesian methods; variational inference; astronomical cataloging; photo-Z estimation; weak lensing shear inference; galaxy cluster characterization; strong lensing
Giuseppe Riccio	INAF	Machine Learning, Web resources, Data analysis tools
Mickael Rigault	CNRS
Brian Rogers	University of Oxford	deep learning, embeddings, outlier detection
Reinaldo Rosa	INPE-MCTI	XAI and Deep Learning
Cyrille Rosset	CNRS/IN2P3/APC	Deep learning, variational auto-encoder
Cécile Roucelle	APC Paris, France	deep learning, weak lensing
Mariana Rubet	National Institute of Space Research (INPE)	Classification methods
David Ruppert	Cornell University
Mieszko Rutkowski	National Center for Nuclear Research, allegro.com	anomaly detection, multimodal machine learning, General Relativity
Rafael S. de Souza	University of Hertfordshire	Bayesian Statistics, Deep Learning, Unsupervised Learning
Sameer Sameer	University of Oklahoma	Bayesian inference
Paula Sanchez Saez	ESO	anomaly detection, ML and DL modeling and classification
André Santos	Brazilian Center for Research in Physics	Deep Learning, Bayesian Inference, Physics Informed Neural Networks, Pipelines
Argyro Sasli	University of Minnesota - Twin Cities	Anomaly detection with ML, Bayesian Inference, Classification methods
Aryansh Saxena	NIT hamirpur	interested in Nuclear Physics, Astroparticle Physics and high energy physics
Jeffrey Scargle	NASA Ames (retired)	time series analysis
Chad Schafer	Carnegie Mellon University
Lior Shamir	Kansas State University	Machine learning, robust image analysis, computational statistics, fuzzy logic
Aneta Siemiginowska	Center for Astrophysics | Harvard & Smithsonian	time-domain methods, quasars reverberation, transients, uncertainties, calibration uncertainties
Joshua Speagle	University of Toronto
Niharika Sravan	Drexel University	Reinforcement learning
Sreevarsha Sreejith	University of Surrey
Keivan Stassun	Vanderbilt University
Lukas Steinwender	Center of Astrophysics and Supercomputing - Swinburne university of Technology
Steven Stetzler	University of Washington	Scalable data access/processing, algorithm development, astrostatistics
Connor Stone	Université de Montréal	GPU acceleration, Bayesian Inference, Sample testing
Fiorenzo Stoppa	University of Oxford	Anomaly detection
Shenli Tang	University of Southampton	classification methods; deep learning
Michael Tauraso	University of Washington	Anomaly detection
Tilman Troester	ETH Zurich
Eleni Tsaprazi	Imperial College London	Bayesian inference, higher-order statistics
Anastasios (Andy) Tzanidakis	University of Washington
Yousuke Utsumi	NAOJ
Anke van Dyk	South African Astronomical Observatory	Time series analysis, Bayesian inference, Population statistics
Aayushi Verma	University of Connecticut	Computer vision classification, adversarial machine learning, observational planetary astronomy
Ricardo Vilalta	University of Austin	Machine learning, pattern recognition.
Ashley Villar	Harvard University	Anomaly detection; uncertainty quantification
Max West	LINCC Frameworks/University of Washington
Tom J. Wilson	University of Exeter	Bayesian probabilistic methods, statistical analysis, largely anything non-AI/ML
John Wu	STScI	Deep learning, neural inference
Yikun Zhang	University of Washington
Yuanyuan Zhang	NSF NOIRLab	Cosmology and large data exploration/analysis methods
Zhuoyang (Grant) Zhou	Carnegie Mellon University	Deep learning; galaxy structural parameters
Andrija Župić	ICCUB, University of A Coruña	Classification methods, Machine learning for variable and binary stars, Survey data analysis (Gaia, ZTF, LSST)

Contact Us

See any issues with your membership information in the roster? Please reach out to issc.membership.committee@gmail.com