Modelling Auditory Spatial Attention with Constraints. - Edward J. Golob; Kristen Brent Venable; Maxwell T. Anderson; Jesse A. Benzell; Jaelle Scheuerman (AIC, 2016)

Computational modeling of auditory spatial attention. - Edward J. Golob; Kristen Brent Venable; Jaelle Scheuerman; Maxwell T. Anderson (CogSci, 2017)

Modeling Bias Reduction Strategies in a Biased Agent. - Jaelle Scheuerman; Dina M. Acklin (IJCAI, 2017)

Modeling Spatial Auditory Attention: Handling Equiprobable Attended Locations. - Jaelle Scheuerman; Kristen Brent Venable; Maxwell T. Anderson; Edward J. Golob (CAID@IJCAI, 2017)

Modeling spatial auditory attention in ACT-R: a constraint-based approach. - Jaelle Scheuerman; Kristen Brent Venable; Maxwell T. Anderson; Edward J. Golob (BICA, 2018)

Computational Model of Spatial Auditory Attention in ACT-R. - Jaelle Scheuerman; Maxwell T. Anderson; Kristen Brent Venable; Edward J. Golob (CogSci, 2018)

Heuristics in Multi-Winner Approval Voting. - Jaelle Scheuerman; Jason L. Harman; Nicholas Mattei; Kristen Brent Venable (CoRR, 2019)

Computational Methods for Simulating Biased Agents. - Jaelle Scheuerman (AAMAS, 2020)

Heuristic Strategies in Uncertain Approval Voting Environments. - Jaelle Scheuerman; Jason L. Harman; Nicholas Mattei; Kristen Brent Venable (AAMAS, 2020)

On Interactive Machine Learning and the Potential of Cognitive Feedback. - Chris J. Michael; Dina M. Acklin; Jaelle Scheuerman (CoRR, 2020)

Attending Doctoral Events - Experiences and Lessons. - Viktor Matkovic; Jaelle Scheuerman; Madeleine Steeds; Sarah Turner; Sarah Clinch (IEEE Pervasive Comput., 2020)

Modeling Voters in Multi-Winner Approval Voting. - Jaelle Scheuerman; Jason L. Harman; Nicholas Mattei; Kristen Brent Venable (AAAI, 2021)

Designing Interactive Machine Learning Systems for GIS Applications. - Jaelle Scheuerman; Chris J. Michael 001; Brad Landreneau; Dina M. Acklin; Jason L. Harman (Engineering Artificially Intelligent Systems, 2021)

Simple rules outperform machine learning for personnel selection: insights from the 3rd annual SIOP machine learning competition. - Jason L. Harman; Jaelle Scheuerman (Discov. Artif. Intell., 2023)

Closed-Loop Uncertainty: The Evaluation and Calibration of Uncertainty for Human-Machine Teams under Data Drift. - Zachary Bishof; Jaelle Scheuerman; Chris J. Michael 001 (Entropy, 2023)

A Framework for Enhancing Behavioral Science Research with Human-Guided Language Models. - Jaelle Scheuerman; Dina M. Acklin (AAAI Spring Symposia, 2024)

Multi-Criteria Comparison as a Method of Advancing Knowledge-Guided Machine Learning. - Jason L. Harman; Jaelle Scheuerman (CoRR, 2024)

Adapting Language Model Responses with Implicit Feedback for Effective Human-AI Collaboration. - Jaelle Scheuerman; Shannon McGarry; Ciara Sibley; Noelle Brown (AAAI Spring Symposia, 2025)

Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind. - Mouad Abrini; Omri Abend; Dina M. Acklin; Henny Admoni; Gregor Aichinger; Nitay Alon; Zahra Ashktorab; Ashish Atreja; Moises Auron; Alexander Aufreiter; Raghav Awasthi; Soumya Banerjee; et al. (CoRR, 2025)