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Jaelle Scheuerman

Professional Summary

Computer scientist and educator with 15+ years of experience and a deep passion for making emerging technologies accessible to all. From my M.S. work in developing educational technology for K-5, to my Ph.D. research into heuristics and bias in decision making (Tulane, 2020), I’ve dedicated my career to bridging gaps between complex technology and real-world understanding. I specialize in Human-AI Collaboration, Interactive Machine Learning, Human-Computer Interaction, and helping interdisciplinary teams enable their communities to critically engage with and deploy technical solutions with intention.

Education

Ph.D., Computer Science — Tulane University (2015–2020)
Dissertation: Computational Models of Heuristics and Bias in Human Behavior

M.S., Human Computer Interaction — Iowa State University (2012–2014)
Capstone: Computational Thinking Through Games

B.S., Computer Science — South Dakota School of Mines & Technology (2007–2010)

Professional & Research Experience

Co-Founder — Train with Intent (2025–present)

Computer Scientist — Center for Geospatial Sciences, Naval Research Lab (2015–present)

Manager of Technology Initiatives — Newcomb College Institute, Tulane University (2010–2015)

Undergraduate Research Assistant — South Dakota School of Mines & Technology (2008–2010)

Teaching Experience

Tulane University

Selected Publications

Scheuerman, Jaelle, Shannon McGarry, Ciara Sibley, and Noelle Brown. 2025. “Beyond Explicit Instruction Enhancing Human-AI Collaboration with Implicit User Feedback.” Artificial Intelligence and Social Computing 163. https://doi.org/10.54941/ahfe1006049.
Harman, Jason L., and Jaelle Scheuerman. 2024. Multi-Criteria Comparison as a Method of Advancing Knowledge-Guided Machine Learning. arXiv. https://doi.org/10.48550/arXiv.2403.11840.
Bishof, Zachary, Jaelle Scheuerman, and Chris J. Michael. 2023. “Closed-Loop Uncertainty: The Evaluation and Calibration of Uncertainty for Human–Machine Teams Under Data Drift.” Entropy 25 (10): 1443. https://doi.org/10.3390/e25101443.
Harman, Jason L., and Jaelle Scheuerman. 2023. “Simple Rules Outperform Machine Learning for Personnel Selection: Insights from the 3rd Annual SIOP Machine Learning Competition.” Discover Artificial Intelligence 3 (1): 2. https://doi.org/10.1007/s44163-022-00044-2.
Scheuerman, Jaelle, Chris J. Michael, Brad Landreneau, Dina M. Acklin, and Jason L. Harman. 2021. “Designing Interactive Machine Learning Systems for GIS Applications.” In Engineering Artificially Intelligent Systems: A Systems Engineering Approach to Realizing Synergistic Capabilities, edited by William F. Lawless, James Llinas, Donald A. Sofge, and Ranjeev Mittu. Springer International Publishing. https://doi.org/10.1007/978-3-030-89385-9_9.
Michael, Chris J., Dina Acklin, and Jaelle Scheuerman. 2020. On Interactive Machine Learning and the Potential of Cognitive Feedback. arXiv. https://doi.org/10.48550/arXiv.2003.10365.

Honors & Awards

Service & Community Engagement