Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive Models. - Alexander New; Curt M. Breneman; Kristin P. Bennett (IJCNN, 2018)

Semantically-aware population health risk analyses. - Alexander New; Sabbir M. Rashid; John S. Erickson; Deborah L. McGuinness; Kristin P. Bennett (CoRR, 2018)

Semantically-targeted analytics for reproducible scientific discovery. - Alexander New; Shruthi Chari; Miao Qi; Sabbir M. Rashid; John S. Erickson; Deborah L. McGuinness; Kristin P. Bennett (AIDR, 2019)

Supervised Mixture Models for Population Health. - Xiao Shou; Georgios Mavroudeas; Alexander New; Kofi Arhin; Jason Nicholas Kuruzovich; Malik Magdon-Ismail; Kristin P. Bennett (BIBM, 2019)

Automating Population Health Studies Through Semantics and Statistics. - Alexander New; Miao Qi; Shruthi Chari; Sabbir M. Rashid; Oshani Seneviratne; Jamie P. McCusker; John S. Erickson; Deborah L. McGuinness; Kristin P. Bennett (SAWSemStats@ISWC, 2019)

A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models. - Alexander New; Kristin P. Bennett (IEEE J. Biomed. Health Informatics, 2020)

Continual learning benefits from multiple sleep stages: NREM, REM, and Synaptic Downscaling. - Brian S. Robinson; Clare W. Lau; Alexander New; Shane M. Nichols; Erik C. Johnson; Michael Wolmetz; William G. Coon (IJCNN, 2022)

Lifelong Learning Metrics. - Alexander New; Megan M. Baker; Eric Q. Nguyen; Gautam K. Vallabha (CoRR, 2022)

L2Explorer: A Lifelong Reinforcement Learning Assessment Environment. - Erik C. Johnson; Eric Q. Nguyen; Blake Schreurs; Chigozie S. Ewulum; Chace Ashcraft; Neil M. Fendley; Megan M. Baker; Alexander New; Gautam K. Vallabha (CoRR, 2022)

Latent Properties of Lifelong Learning Systems. - Corban G. Rivera; Chace Ashcraft; Alexander New; James Schmidt; Gautam K. Vallabha (CoRR, 2022)

Curvature-informed multi-task learning for graph networks. - Alexander New; Michael J. Pekala; Nam Q. Le; Janna Domenico; Christine D. Piatko; Christopher D. Stiles (CoRR, 2022)

Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations. - David Witman; Alexander New; Hicham Alkendry; Honest Mrema (CoRR, 2022)

Continual learning benefits from multiple sleep mechanisms: NREM, REM, and Synaptic Downscaling. - Brian S. Robinson; Clare W. Lau; Alexander New; Shane M. Nichols; Erik C. Johnson; Michael Wolmetz; William G. Coon (CoRR, 2022)

Tunable complexity benchmarks for evaluating physics-informed neural networks on coupled ordinary differential equations. - Alexander New; Benjamin Eng; Andrea C. Timm; Andrew S. Gearhart (CISS, 2023)

Evaluating the diversity and utility of materials proposed by generative models. - Alexander New; Michael J. Pekala; Elizabeth A. Pogue; Nam Q. Le; Janna Domenico; Christine D. Piatko; Christopher D. Stiles (CoRR, 2023)

Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows. - Victor J. Leon; Noah Ford; Honest Mrema; Jeffrey Gilbert; Alexander New (CoRR, 2023)

Data-efficient operator learning for solving high Mach number fluid flow problems. - Noah Ford; Victor J. Leon; Honest Mrema; Jeffrey Gilbert; Alexander New (CoRR, 2023)

A domain-agnostic approach for characterization of lifelong learning systems. - Megan M. Baker; Alexander New; Mario Aguilar-Simon; Ziad Al-Halah; Sébastien M. R. Arnold; Eseoghene Ben-Iwhiwhu; Andrew P. Brna; Ethan Brooks; Ryan C. Brown; Zachary Daniels; Anurag Reddy Daram; Fabien Delattre; et al. (Neural Networks, 2023)

Self-supervised learning for crystal property prediction via denoising. - Alexander New; Nam Q. Le; Michael J. Pekala; Christopher D. Stiles (CoRR, 2024)

Equation identification for fluid flows via physics-informed neural networks. - Alexander New; Marisel Villafañe-Delgado; Charles Shugert (CoRR, 2024)