SOMz: photometric redshift PDFs with self organizing maps and random atlas. - Matias Carrasco Kind; Robert J. Brunner (CoRR, 2013)

Crowdsourcing quality control for Dark Energy Survey images. - Peter Melchior; Erin Sheldon; Alex Drlica-Wagner; Eli S. Rykoff; Timothy M. C. Abbott; Filipe B. Abdalla; Sahar Allam; Aurélien Benoit-Lévy; David D. Brooks; Elizabeth Buckley-Geer; Aurelio Carnero Rosell; Matias Carrasco Kind; et al. (Astron. Comput., 2016)

Vizic: A Jupyter-based interactive visualization tool for astronomical catalogs. - Weixiang Yu; Matias Carrasco Kind; Robert J. Brunner (Astron. Comput., 2017)

Batch and online anomaly detection for scientific applications in a Kubernetes environment. - Sahand Hariri; Matias Carrasco Kind (ScienceCloud@HPDC, 2018)

DES science portal: Computing photometric redshifts. - Julia Gschwend; A. Carnero Rossel; Ricardo Ogando; Angelo Fausti Neto; Marcio A. G. Maia; Luiz Nicolaci da Costa; Marcos Lima; Paulo S. S. Pellegrini; Riccardo Campisano; C. Singulani; C. Adean; Christophe O. Benoist; et al. (Astron. Comput., 2018)

DES science portal: Creating science-ready catalogs. - Angelo Fausti Neto; Luiz Nicolaci da Costa; Aurelio Carnero Rosell; Julia Gschwend; R. L. C. Ogando; Flavia Sobreira; Marcio A. G. Maia; Basillio X. Santiago; Rogério Rosenfeld; C. Singulani; C. Adean; L. D. P. Nunes; et al. (Astron. Comput., 2018)

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era. - Gabrielle Allen; Igor Andreoni; Etienne Bachelet; G. Bruce Berriman; Federica B. Bianco; Rahul Biswas; Matias Carrasco Kind; Kyle Chard; Minsik Cho; Philip S. Cowperthwaite; Zachariah B. Etienne; Daniel George; et al. (CoRR, 2019)

Enabling real-time multi-messenger astrophysics discoveries with deep learning. - Eliu A. Huerta; Gabrielle Allen; Igor Andreoni; Javier Mauricio Antelis; Etienne Bachelet; G. Bruce Berriman; Federica B. Bianco; Rahul Biswas; Matias Carrasco Kind; Kyle Chard; Minsik Cho; Philip S. Cowperthwaite; et al. (CoRR, 2019)

easyaccess: Enhanced SQL command line interpreter for astronomical surveys. - Matias Carrasco Kind; Alex Drlica-Wagner; Audrey Koziol; Donald Petravick (J. Open Source Softw., 2019)

Optical follow-up of gravitational wave triggers with DECam during the first two LIGO/VIRGO observing runs. - Ken Herner; James Annis; Dillon Brout; Marcelle Soares-Santos; Richard Kessler; Masao Sako; Ray F. Butler; Zoheyr Doctor; Antonella Palmese; Sahar Allam; Douglas L. Tucker; Flavia Sobreira; et al. (Astron. Comput., 2020)

Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects. - Ben Henghes; Ofer Lahav; David W. Gerdes; E. Lin; Robert Morgan; Timothy M. C. Abbott; Michel Aguena; Sahar Allam; James Annis; Santiago Ávila; Emmanuel Bertin; David D. Brooks; et al. (CoRR, 2020)

Survey2Survey: A deep learning generative model approach for cross-survey image mapping. - Brandon Buncher; Awshesh Nath Sharma; Matias Carrasco Kind (CoRR, 2020)

AGNet: Weighing Black Holes with Machine Learning. - Joshua Yao-Yu Lin; Sneh Pandya; Devanshi Pratap; Xin Liu; Matias Carrasco Kind (CoRR, 2020)

A machine learning approach to galaxy properties: Joint redshift - stellar mass probability distributions with Random Forest. - Sunil Mucesh; William G. Hartley; Antonella Palmese; Ofer Lahav; L. Whiteway; A. Amon; Keith C. Bechtol; Gary M. Bernstein; Aurelio Carnero Rosell; Matias Carrasco Kind; A. Choi; K. Eckert; et al. (CoRR, 2020)

flimview : A software framework to handle, visualize and analyze FLIM data. - Matias Carrasco Kind; Mantas Zurauskas; Aneesh Alex; Marina Marjanovic; Prabuddha Mukherjee; Minh Doan; Darold R. Spillman Jr.; Steve Hood; Stephen A. Boppart (F1000Research, 2020)

More than Meets the Eye: Towards an Artificial Intelligence Observatory. - Matias Carrasco Kind (DeLTA, 2021)

AGNet: Weighing Black Holes with Deep Learning. - Joshua Yao-Yu Lin; Sneh Pandya; Devanshi Pratap; Xin Liu; Matias Carrasco Kind; Volodymyr V. Kindratenko (CoRR, 2021)

Extended Isolation Forest. - Sahand Hariri; Matias Carrasco Kind; Robert J. Brunner (IEEE Trans. Knowl. Data Eng., 2021)