Approaches to Assessing the Impact of E-Learning on Attainment II: The English Further Education Secor. - Rachel Harris; Alison Muirhead; John Hall (CELDA, 2004)

Diversity creation methods: a survey and categorisation. - Gavin Brown 001; Jeremy L. Wyatt; Rachel Harris; Xin Yao 001 (Inf. Fusion, 2005)

A comparative study of high-performance computing on the cloud. - Aniruddha Marathe; Rachel Harris; David K. Lowenthal; Bronis R. de Supinski; Barry Rountree; Martin Schulz 001; Xin Yuan 001 (HPDC, 2013)

Exploiting redundancy for cost-effective, time-constrained execution of HPC applications on amazon EC2. - Aniruddha Marathe; Rachel Harris; David K. Lowenthal; Bronis R. de Supinski; Barry Rountree; Martin Schulz 001 (HPDC, 2014)

Exploiting Redundancy and Application Scalability for Cost-Effective, Time-Constrained Execution of HPC Applications on Amazon EC2. - Aniruddha Marathe; Rachel Harris; David K. Lowenthal; Bronis R. de Supinski; Barry Rountree; Martin Schulz 001 (IEEE Trans. Parallel Distributed Syst., 2016)

Comparing Machine Learning and Optimization Approaches for the N - k Interdiction Problem Considering Load Variability. - Alejandro Owen Aquino; Rachel Harris; Alyssa Kody; Daniel K. Molzahn (HICSS, 2023)

PowerModelsADA: A Framework for Solving Optimal Power Flow using Distributed Algorithms. - Mohannad Alkhraijah; Rachel Harris; Carleton Coffrin; Daniel K. Molzahn (CoRR, 2023)

Detecting Shared Data Manipulation in Distributed Optimization Algorithms. - Mohannad Alkhraijah; Rachel Harris; Samuel Litchfield; David Huggins; Daniel K. Molzahn (CoRR, 2023)

Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow. - Rachel Harris; Mohannad Alkhraijah; Daniel K. Molzahn (CoRR, 2023)

Detecting and Mitigating Data Integrity Attacks on Distributed Algorithms for Optimal Power Flow using Machine Learning. - Rachel Harris; Daniel K. Molzahn (HICSS, 2024)