Extending the software-defined network boundary. - Oliver Michel; Michael Coughlin; Eric Keller (SIGCOMM, 2014)

Apps with Hardware: Enabling Run-time Architectural Customization in Smart Phones. - Michael Coughlin; Ali Ismail; Eric Keller (USENIX ATC, 2016)

Trusted Click: Overcoming Security issues of NFV in the Cloud. - Michael Coughlin; Eric Keller; Eric Wustrow (SDN-NFV@CODASPY, 2017)

Augmenting cloud architectures to support decentralized applications. - Michael Coughlin; Kelly Kaoudis; Eric Keller (IM, 2017)

Autonomous Real-Time Science-Driven Follow-up of Survey Transients. - Niharika Sravan; Matthew J. Graham; Christoffer Fremling; Michael W. Coughlin (BDA (Astronomy, Science, and Engineering), 2021)

Tails: Chasing Comets with the Zwicky Transient Facility and Deep Learning. - Dmitry A. Duev; Bryce T. Bolin; Matthew J. Graham; Michael S. P. Kelley; Ashish Mahabal; Eric C. Bellm; Michael W. Coughlin; Richard Dekany; George Helou; Shrinivas R. Kulkarni; Frank J. Masci; Thomas A. Prince; et al. (CoRR, 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)

A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics. - Alec Gunny; Dylan S. Rankin; Philip C. Harris; Erik Katsavounidis; Ethan Marx; Muhammed Saleem; Michael Coughlin; William Benoit (FlexScience@HPDC, 2022)

Physics Community Needs, Tools, and Resources for Machine Learning. - Philip C. Harris; Erik Katsavounidis; William Patrick McCormack; Dylan S. Rankin; Yongbin Feng; Abhijith Gandrakota; Christian Herwig; Burt Holzman; Kevin Pedro; Nhan Tran; Tingjun Yang; Jennifer Ngadiuba; et al. (CoRR, 2022)

Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings. - Deep Chatterjee; Philip C. Harris; Maanas Goel; Malina Desai; Michael W. Coughlin; Erik Katsavounidis (CoRR, 2023)

Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning. - Bryce T. Bolin; Michael W. Coughlin (CoRR, 2024)

The Palomar twilight survey of 'Ayló'chaxnim, Atiras, and comets. - Bryce T. Bolin; Frank J. Masci; Michael W. Coughlin; Dmitry A. Duev; Z. Ivezic; R. L. Jones; P. Yoachim; T. Ahumada; V. Bhalerao; H. Choudhary; C. Contreras; Y.-C. Cheng; et al. (CoRR, 2024)

A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run. - Ryan Raikman; Eric A. Moreno; Katya Govorkova; Siddharth Soni; Ethan Marx; William Benoit; Alec Gunny; Deep Chatterjee; Christina Reissel; Malina Desai; Rafia Omer; Muhammed Saleem; et al. (CoRR, 2024)

Rapid likelihood free inference of compact binary coalescences using accelerated hardware. - Deep Chatterjee; Ethan Marx; William Benoit; Ravi Kumar; Malina Desai; Ekaterina Govorkova; Alec Gunny; Eric A. Moreno; Rafia Omer; Ryan Raikman; Muhammed Saleem; Shrey Aggarwal; et al. (Mach. Learn. Sci. Technol., 2024)

GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders. - Ryan Raikman; Eric A. Moreno; Ekaterina Govorkova; Ethan Marx; Alec Gunny; William Benoit; Deep Chatterjee; Rafia Omer; Muhammed Saleem; Dylan S. Rankin; Michael W. Coughlin; Philip C. Harris; et al. (Mach. Learn. Sci. Technol., 2024)

Building Machine Learning Challenges for Anomaly Detection in Science. - Elizabeth G. Campolongo; Yuan-Tang Chou; Ekaterina Govorkova; Wahid Bhimji; Wei-Lun Chao; Chris Harris; Shih-Chieh Hsu; Hilmar Lapp; Mark S. Neubauer; Josephine M. Namayanja; Aneesh Subramanian; Philip C. Harris; et al. (CoRR, 2025)