most citedEnabling real-time multi-messenger astrophysics discoveries with deep learning

85 citations · 102 across the 5 of their papers we have counts for

collaborators

5 papers

gr-qc201985 cited

Enabling real-time multi-messenger astrophysics discoveries with deep learning

E. A. Huerta, Gabrielle Allen, Igor Andreoni +57

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that…

cs.DC2019

Cloud Futurology

Blesson Varghese, Philipp Leitner, Suprio Ray +9

The Cloud has become integral to most Internet-based applications and user gadgets. This article provides a brief history of the Cloud and presents a researcher's view of the prosp…

astro-ph.IM201911 cited

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

Gabrielle Allen, Igor Andreoni, Etienne Bachelet +45

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrop…

cs.DL20161 cited

Capturing the "Whole Tale" of Computational Research: Reproducibility in Computing Environments

Bertram Ludaescher, Kyle Chard, Niall Gaffney +4

We present an overview of the recently funded "Merging Science and Cyberinfrastructure Pathways: The Whole Tale" project (NSF award #1541450). Our approach has two nested goals: 1)…

cs.DC20165 cited

A Secure Data Enclave and Analytics Platform for Social Scientists

Yadu N. Babuji, Kyle Chard, Aaron Gerow +1

Data-driven research is increasingly ubiquitous and data itself is a defining asset for researchers, particularly in the computational social sciences and humanities. Entire career…