Michelle Lochner

According to our database, Michelle Lochner authored at least 10 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration.

LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz, Matthew R. Becker, Biswajit Biswas, Rahul Biswas, Boris Bolliet, Adam S. Bolton, Clecio R. Bom, Raphaël Bonnet-Guerrini, Alexandre Boucaud, Jean-Eric Campagne, Chihway Chang, Aleksandra Ciprijanovic, Johann Cohen-Tanugi, Michael W. Coughlin, John Franklin Crenshaw, Juan C. Cuevas-Tello, Juan de Vicente, Seth W. Digel, Steven Dillmann, Mariano Javier de León Dominguez Romero, Alex Drlica-Wagner, Sydney Erickson, Alexander T. Gagliano, Christos Georgiou, Aritra Ghosh, Matthew Grayling, Kirill A. Grishin, Alan Heavens, Lindsay R. House, Mustapha Ishak, Wassim Kabalan, Arun Kannawadi, François Lanusse, C. Danielle Leonard, Pierre-François Léget, Michelle Lochner, Yao-Yuan Mao, Peter Melchior, Grant Merz, Martin Millon, Anais Möller, Gautham Narayan, Yuuki Omori, Hiranya Peiris, Laurence Perreault Levasseur, Andrés Alejandro Plazas Malagón, Nesar Ramachandra, Benjamin Remy, Cécile Roucelle, Jaime Ruiz-Zapatero, Stefan Schuldt, Ignacio Sevilla-Noarbe, Ved G. Shah, Tjitske Starkenburg, Stephen Thorp, Laura Toribio San Cipriano, Tilman Tröster, Roberto Trotta, Padma Venkatraman, Amanda Wasserman, Tim White, Justine Zeghal, Tianqing Zhang, Yuanyuan Zhang

CoRR, January, 2026

2024

A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series.

Rithwik Gupta, Daniel Muthukrishna, Michelle Lochner

CoRR, 2024

A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients.

Rithwik Gupta, Daniel Muthukrishna, Michelle Lochner

CoRR, 2024

2021

Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data.

Daniel Muthukrishna, Kaisey S. Mandel, Michelle Lochner, Sara Webb, Gautham Narayan

CoRR, 2021

Real-time detection of anomalies in large-scale transient surveys.

Daniel Muthukrishna, Kaisey S. Mandel, Michelle Lochner, Sara Webb, Gautham Narayan

CoRR, 2021

Practical Galaxy Morphology Tools from Deep Supervised Representation Learning.

Mike Walmsley, Anna M. M. Scaife, Chris J. Lintott, Michelle Lochner, Verlon Etsebeth, Tobias Géron, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen L. Masters, Kameswara Bharadwaj Mantha, Brooke D. Simmons

CoRR, 2021

Astronomaly: Personalised active anomaly detection in astronomical data.

Michelle Lochner, Bruce A. Bassett

Astron. Comput., 2021

2019

Bayesian Anomaly Detection and Classification.

Ethan Roberts, Bruce A. Bassett, Michelle Lochner

CoRR, 2019

Bayesian Anomaly Detection and Classification for Noisy Data.

Ethan Roberts, Bruce A. Bassett, Michelle Lochner

Proceedings of the Intelligent Systems Design and Applications, 2019

2018

DeepSource: Point Source Detection using Deep Learning.

Alireza Vafaei Sadr, Etienne Eben Vos, Bruce A. Bassett, Zafiirah Hosenie, Nadeem Oozeer, Michelle Lochner

CoRR, 2018