Machine Learning the Donor Journey. - Greg Lee; Ajith Kumar Veera Raghavan; Mark Hobbs (Canadian AI, 2020)

Improving the Donor Journey with Convolutional and Recurrent Neural Networks. - Greg Lee; Ajith Kumar Veera Raghavan; Mark Hobbs (ICMLA, 2020)

PeriPy - A High Performance OpenCL Peridynamics Package. - Benjamin Boys; Tim J. Dodwell; Mark Hobbs; Mark Girolami (CoRR, 2021)

Optimizing the Feature Set for Machine Learning Charitable Predictions. - Greg Lee; Jordan Pippy; Mark Hobbs (AI, 2022)

A probabilistic peridynamic framework with an application to the study of the statistical size effect. - Mark Hobbs; Hussein Rappel; Tim J. Dodwell (CoRR, 2022)

Adding Time and Subject Line Features to the Donor Journey. - Greg Lee; Ajith Kumar Veera Raghavan; Mark Hobbs (ICAART (2), 2023)

Effects of Feature Types on Donor Journey. - Greg Lee; Ajith Kumar Veera Raghavan; Mark Hobbs (ICAART (Revised Selected Paper), 2023)

Predicting Major Donor Prospects Using Machine Learning. - Greg Lee; Aishwarya Vaishali Sathyamurthi; Mark Hobbs (ICAART (2), 2024)

Deep and Shallow Machine Learning for Predicting Major Donors. - Greg Lee; Aishwarya Vaishali Sathyamurthi; Mark Hobbs (ICAART (Revised Selected Paper, 2), 2024)

Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids. - Saurabh Deshpande; Hussein Rappel; Mark Hobbs; Stéphane P. A. Bordas; Jakub Lengiewicz (CoRR, 2024)