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Daniel Rice

  • Associate Professor of Practice
Academic Division: Operations and Information Management
I am a business innovator and experienced educator with decades of hands-on business leadership, academic, and program director experience. As faculty at Loyola University and full-time lecturer at other universities, I have taught undergraduate, masters, and professional program students developing their knowledge, insight, and ability to impact business in finance, operations, and information technologies. As the director of Graduate Studies at the University of North Carolina, Greensborough, I led the restructuring and development of a new Mater of Science curriculum and professional programs in operations research, information technology, and cyber security, designing and implementing traditional and online courses and successfully presenting the curriculum for approval by graduate school council leading to full implementation in the business school curriculum. In my other professional careers, I have led cross-functional professional teams on research, engineering, information systems, software development and quantitative analysis; having successfully delivered results on projects totally over $28 million. I have significant experience capturing and delivering on government grants and contracts, have led projects as program manager (PM), principal investigator (PI), and integrated product team (IPT) lead on multiple programs including multiple DoD SBIR programs totally over $6 million in awards and $1.3 million non-SBIR funding as PI.

Academic Degrees

  • Ph D, University of Connecticut School of Business
  • MBA, University of Connecticut - School of Finance

Publications

Journal Articles

  • Rice, D.O., Korna, M., Biagiatti, E., Barker, D. (2019). Predicting Respirator Size and Fit from 2D Images. International Journal of Human Factors Modeling and Simulation. Inderscience.

Conference Proceedings

  • Rice, D.O. (2024). Recent advances on generative models for semantic segmentation: a survey: Recent advances on generative models for semantic segmentation: a survey. SPIE Digital Library, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI.
  • Rice, D.O. (2023). Toward Scene Understanding with Depth and Object-Aware Clustering in Contested Environment: Toward Scene Understanding with Depth and Object-Aware Clustering in Contested Environment. Vol: 2023 International Conference on Machine Learning and Applications (ICMLA). IEEE Xplore.