Dr. Neil Ranly, Assistant Professor of Data Science
Dr. Ranly is a data scientist specializing in software engineering, information management operations, and portfolio decision analysis methods. He has over 20 years of experience in developing software solutions with internet and database technology. He has approximately 15 years of experience in researching and developing solutions for information management problems.
Education
Doctorate, Air Force Institute of Technology, 2018
Major: Operations Research
Master of Science, University of Dayton, 2009
Major: Management Science
Bachelor of Science, University of Dayton, 2004
Major: Computer Science
Magna Cum Laude Graduation Honor
Addison-Wesley Senior Book Award for Excellence in Computer Science
Publications
Archival Journal Articles
* Indicates student
R. Everman*, T. Wagner, N. Ranly, and B. Cox, “Aircraft detection from satellite imagery using synthetic data,” Journal of Defense Modeling & Simulation, p. 15485129241309657, Jan. 2025, doi: 10.1177/15485129241309657.
J. H. Yae*, N. C. Skelly*, N. C. Ranly, and P. M. LaCasse, “Leveraging large language models for word sense disambiguation,” Neural Computing & Applications, Dec. 2024, doi: 10.1007/s00521-024-10747-5.
Pre-prints
N. Ranly and T. Wagner, “Rapid Experimentation with Python Considering Optional and Hierarchical Inputs,” 2025, arXiv. doi: 10.48550/ARXIV.2501.03398.
Refereed Conference Proceedings
N. Ranly, K. Binsted, W. Wu, and T. Wagner, “Introduction to the Minitrack on Natural Language Processing and Large Language Models Supporting Data Analytics for System Sciences,” 2025.
N. Ranly, J. Weir, H. Tucholski, and J. Colombi, “Information production planning with multiple information users and aging information products,” in IIE Annual Conference. Proceedings, Institute of Industrial and Systems Engineers (IISE), 2018, pp. 408–413.
Monographs
Dissertation, Methods to Support the Project Selection Problem with Non-Linear Portfolio Objectives, Time Sensitive Objectives, Time Sensitive Resource Constraints, and Modeling Inadequacies