Ronilo Ragodos

Assistant Professor
Decision Sciences
Office: Paul College, Durham, NH 03824
Websites:
Curriculum Vitae
Pronouns: He/Him/His
Ronilo Ragodos’ research interests lie in solving problems in business and healthcare through machine learning. His work focuses on designing interpretable models tailored to each application. In addition to working with standard tabular datasets, he especially enjoys tackling problems involving unstructured data, such as images. The models he develops are designed either to extract insights from data or to support data-driven decision-making. Because these models are interpretable—that is, their decision-making processes are simple and understandable—they empower users. By being able to evaluate the models’ reasoning, users can build trust and incorporate their own judgment into analyses.
 
Before earning his Ph.D. in Business Administration (Business Analytics) at the University of Iowa, he received a degree in Mathematics. Having focused on topics in measure theory during his masters, Ronilo enjoys utilizing his theoretical understanding of machine learning and probability in high dimensions in research. He is also interested in bridging the gap between scientific practice and the philosophy of science. 

Education

  • Ph.D., University of Iowa
  • M.Sc., Mathematics, University of Iowa
  • B.Sc., Applied Mathematical Sciences and Computational Sciences, Texas A&M University

Research Interests

  • Artificial Intelligence
  • Big Data
  • Health care
  • Information Science/Systems
  • Philosophy of science

Selected Publications

  • An, B., Zhou, X., Zhou, Z., Ragodos, R., Xu, Z., & Luo, J. (2025). GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models Through Statistically-Guided Geo-Prototyping. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 39 (pp. 11427-11435). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v39i11.33243

  • Ragodos, R., Zhou, X., Wang, T., Pan, Y., & Luo, J. (2025). ConPro-GAIL: Interpretable Policy Learning via Conceptual Prototyping for Human Spatiotemporal Decision Understanding. In ACM SIGSPATIAL Conference on Advances in Geographic Information Systems (SIGSPATIAL’25). Minneapolis, MN, USA.

  • Gurung, R., Ragodos, R. J., Chen, C., Ma, H., & Wang, T. (2025). ProtoPairNet: Interpretable Regression through Prototypical Pair Reasoning. In Advances in Neural Information Processing Systems 39: Annual Conference on Neural Information Processing Systems 2025 (NeurIPS ’25). San Diego, CA, USA.

  • Ragodos, R., & Wang, T. (2022). Disjunctive Rule Lists. INFORMS Journal on Computing, 34(6), 3259-3276. doi:10.1287/ijoc.2022.1242

  • Ragodos, R., & Wang, T. (2022). Code and Data Repository for Disjunctive Rule Lists. INFORMS Journal on Computing. doi:10.1287/ijoc.2022.1242.cd

  • Ragodos, R. J., Wang, T., Lin, Q., & Zhou, X. (2022). ProtoX: Explaining a Reinforcement Learning Agent via Prototyping. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022 (NeurIPS ’22). New Orleans, LA, USA. Retrieved from http://papers.nips.cc/

View All