A Visibility-Based Escort Problem

Priyankari Perali*, Lance G. Fletcher*, Andrew Beathard, and Jason M. O'Kane. A Visibility-Based Escort Problem. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4804–4811. IEEE, 2023. (*equal contribution)

Paper Slides / Poster

Three Challenges in Utilizing Machine Learning to Predict Human Behavior from Observational Data

Thomas Manzini, Priyankari Perali, and Robin R. Murphy. Three Challenges in Utilizing Machine Learning to Predict Human Behavior from Observational Data. In Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 737–739, 2024.

Paper Slides / Poster

CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery

Thomas Manzini, Priyankari Perali, Raisa Karnik, and Robin Murphy. CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery. arXiv:2407.17673, 2024.

Paper Dataset

Now You See It, Now You Don’t: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery

Thomas Manzini, Priyankari Perali, Jayesh Tripathi, and Robin Murphy. Now You See It, Now You Don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT), pages 1998–2008, 2025.

Paper

Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters

Priyankari Perali*, Thomas Manzini*, Raisa Karnik, Mihir Godbole, Hasnat Abdullah, and Robin Murphy. Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters. In 2025 IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2025. (*equal contribution)

Paper

Challenges and Research Directions from the Operational Use of a Machine Learning Damage Assessment System via Small Uncrewed Aerial Systems at Hurricanes Debby and Helene

Priyankari Perali*, Thomas Manzini*, Robin Murphy, and David Merrick. Challenges and Research Directions from the Operational Use of a Machine Learning Damage Assessment System via Small Uncrewed Aerial Systems at Hurricanes Debby and Helene. In 2025 IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2025. (*equal contribution)

Paper

Unprompted Touch with Touch Surface Characteristics: A Survey

Priyankari Perali and Robin Murphy. Unprompted Touch with Touch Surface Characteristics: A Survey. ACM Transactions on Human-Robot Interaction, 15(1):1–30, 2025.

Paper

Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response

Thomas Manzini, Priyankari Perali, and Robin Murphy. Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response. In The Thirty-Eighth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), 2026.

Paper

A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagery

Priyankari Perali*, Thomas Manzini*, Raisa Karnik, and Robin Murphy. A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagery. In Proceedings of the AAAI Conference on Artificial Intelligence, 2026. (*equal contribution)

Paper Dataset

Investigating a Real-Time Adaptive Procedure System

Atif Mohammed Ashraf, Priyankari Perali, Hyun-Gee Jei, Joseph W. Hendricks, Thomas Manzini, Vanessa Nasr, S. Camille Peres, Farzan Sasangohar, Maryam Zahabi, and Robin Murphy. Investigating a Real-Time Adaptive Procedure System. Human-Intelligent Systems Integration, 2026.

Looks Can Be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment

Thomas Manzini, Priyankari Perali, Raisa Karnik, Stephen Johnson, and Robin Murphy. Looks Can Be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment. In Proceedings of the 2026 AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2026. (To appear)