Yuanming received her PhD from the department of Business and Management Science in NHH in 2019. She joined SNF as a postdoctoral researcher in 2020 and worked under the project Production in the Barents Sea Fisheries (Research Council of Norway, project no. 302197). Since summer in 2023, she started working as a researcher in SNF focusing on fisheries economics, ocean resource management and machine learning.
Research Interests
- Fisheries economics
- Bioeconomic modeling
- Dynamic programming and optimization
- Machine learning and neural networks
Publications
- Ni, Y., B.Bogstad, G.Ottersen, A. B.Sandø, M.Årthun, and S.Kvamsdal. 2026. Predicting Barents Sea Cod Stock Dynamics Using Oceanographic Data and Neural Network Analysis. Fisheries Oceanography, 1–11. https://doi.org/10.1111/fog.70044.
- Ni, Y., A.O. Hopland, and S. F. Kvamsdal. 2025. Seasonality and Growth in the Generalized Bioeconomic Model. American Journal of Agricultural Economics, 1–19. https://doi.org/10.1111/ajae.70023
- Ni, Y., Sandal, L.K., Kvamsdal, S.F., & Hansen C. (2023). Using feedforward neural networks to represent ecosystem dynamics for bioeconomic analysis. Marine Ecology Progress Series, 716: 1–15. https://doi.org/10.3354/meps14360
- Ni, Y., Sandal, L. K., & Kvamsdal, S. F. (2023). Greed is good: Heuristic adaptations for resilience in renewable resource management. Natural Resource Modeling, 36(2), e12367. https://doi.org/10.1111/nrm.12367
- Ni, Y., Steinshamn, S. I., & Kvamsdal, S. F. (2022). Negative shocks in an age-structured bioeconomic model and how to deal with them. Economic Analysis and Policy, 76, 15–30. https://doi.org/10.1016/j.eap.2022.07.009
- Ni, Y., & Sandal, L. K. (2019). Seasonality matters: A multi-season, multi-state dynamic optimization in fisheries. European Journal of Operational Research, 275(2), 648–658. https://doi.org/10.1016/j.ejor.2018.11.041
- Ni, Y., Eskeland, G. S., Giske, J., & Hansen, J.-P. (2016). The global potential for carbon capture and storage from forestry. Carbon Balance and Management, 11(1), 3. https://doi.org/10.1186/s13021-016-0044-y