Deep multi-view models for glitch classification. - Sara Bahaadini; Neda Rohani; Scott Coughlin; Michael Zevin; Vicky Kalogera; Aggelos K. Katsaggelos (ICASSP, 2017)

Direct: Deep Discriminative Embedding for Clustering of Ligo Data. - Sara Bahaadini; Neda Rohani; Aggelos K. Katsaggelos; Vahid Noroozi; Scott Coughlin; Michael Zevin (ICIP, 2018)

Machine learning for Gravity Spy: Glitch classification and dataset. - Sara Bahaadini; Vahid Noroozi; Neda Rohani; Scott Coughlin; Michael Zevin; Joshua R. Smith 003; Vicky Kalogera; Aggelos K. Katsaggelos (Inf. Sci., 2018)

Teaching citizen scientists to categorize glitches using machine learning guided training. - Corey Brian Jackson; Carsten S. Østerlund; Kevin Crowston; Mahboobeh Harandi; Sarah Allen; Sara Bahaadini; Scotty Coughlin; Vicky Kalogera; Aggelos K. Katsaggelos; Shane L. Larson; Neda Rohani; Joshua R. Smith 003; et al. (Comput. Hum. Behav., 2020)

Knowledge Tracing to Model Learning in Online Citizen Science Projects. - Kevin Crowston; Shane L. Larson; Neda Rohani; Joshua R. Smith 003; Laura Trouille; Michael Zevin; Carsten S. Østerlund; Tae Kyoung Lee; Corey Brian Jackson; Mahboobeh Harandi; Sarah Allen; Sara Bahaadini; et al. (IEEE Trans. Learn. Technol., 2020)

CSD-CMAD: Coupling Similarity and Diversity for Clustering Multivariate Astrophysics Data. - Xu Teng; Thomas Beckler; Bradley Gannon; Benjamin Huinker; Gabriel Huinker; Koushhik Kumar; Christina Marquez; Jacob Spooner; Goce Trajcevski; Prabin Giri; Aaron Dotter; Jeff J. Andrews; et al. (SIGSPATIAL/GIS, 2021)

CACSE: Context Aware Clustering of Stellar Evolution. - Xu Teng; Adam Corpstein; Joel Holm; Willis Knox; Becker Mathie; Philip R. O. Payne; Ethan Vander Wiel; Prabin Giri; Goce Trajcevski; Aaron Dotter; Jeff J. Andrews; Scott Coughlin; et al. (SSTD, 2021)

Enhancing gravitational-wave science with machine learning. - Elena Cuoco; Jade Powell; Marco Cavaglià; Kendall Ackley; Michal Bejger; Chayan Chatterjee; Michael Coughlin; Scott Coughlin; Paul Easter; Reed Essick; Hunter Gabbard; Timothy Gebhard; et al. (Mach. Learn. Sci. Technol., 2021)

Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations. - Kyle Akira Rocha; Jeff J. Andrews; Christopher P. L. Berry; Zoheyr Doctor; Pablo Marchant; Vicky Kalogera; Scott Coughlin; Simone S. Bavera; Aaron Dotter; Tassos Fragos; Konstantinos Kovlakas; Devina Misra; et al. (CoRR, 2022)

Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO. - Pablo Morales-Álvarez; Pablo Ruiz 002; Scott Coughlin; Rafael Molina 001; Aggelos K. Katsaggelos (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Probabilistic fusion of crowds and experts for the search of gravitational waves. - Pablo Ruiz 002; Pablo Morales-Álvarez; Scott Coughlin; Rafael Molina 001; Aggelos K. Katsaggelos (Knowl. Based Syst., 2023)