Challenges and Opportunities in Neuro-Symbolic Composition of Foundation Models. - Susmit Jha; Anirban Roy; Adam D. Cobb; Alexander M. Berenbeim; Nathaniel D. Bastian (MILCOM, 2023)

Uncertainty-Quantified, Robust Deep Learning for Network Intrusion Detection. - Joshua A. Wong; Alexander M. Berenbeim; David A. Bierbrauer; Nathaniel D. Bastian (WSC, 2023)

Measuring Classification Decision Certainty and Doubt. - Alexander M. Berenbeim; Iain J. Cruickshank; Susmit Jha; Robert H. Thomson 001; Nathaniel D. Bastian (CoRR, 2023)

Post-hoc Uncertainty Quantification for Neurosymbolic Artificial Intelligence. - Alexander M. Berenbeim; Ramneet Kaur; Adam D. Cobb; Anirban Roy; Susmit Jha; Nathaniel D. Bastian (MILCOM, 2024)

Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI. - Ramneet Kaur; Colin Samplawski; Adam D. Cobb; Anirban Roy; Brian Matejek; Manoj Acharya; Daniel Elenius; Alexander M. Berenbeim; John A. Pavlik; Nathaniel D. Bastian; Susmit Jha (CoRR, 2024)

Zero-Shot Detection of Out-of-Context Objects Using Foundation Models. - Anirban Roy; Adam D. Cobb; Ramneet Kaur; Sumit Jha 001; Nathaniel D. Bastian; Alexander M. Berenbeim; Robert H. Thomson 001; Iain Cruickshank; Alvaro Velasquez; Susmit Jha (WACV, 2025)

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding. - Trilok Padhi; Ramneet Kaur; Adam D. Cobb; Manoj Acharya; Anirban Roy; Colin Samplawski; Brian Matejek; Alexander M. Berenbeim; Nathaniel D. Bastian; Susmit Jha (CoRR, 2025)