Published in arxiv, 2025
Recommended citation: Guan, V., Janssen, J., Lanzetti, N., Terpin, A., Schiebinger, G., & Robeva, E. (2025). Langevin SDEs have unique transient dynamics. arXiv preprint arXiv:2505.21770. https://arxiv.org/abs/2505.21770
Published in SSRN, 2025
Recommended citation: Janssen, J., Guan, V., & Ameli, A. A. A Causal Inference Framework for Catchment Boundary Delineation. Available at SSRN 5389768. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5389768
Published in Advances in Water Resources, 2025
Recommended citation: Janssen, J., Tootchi, A., & Ameli, A. A. (2025). Tackling water table depth modeling via machine learning: From proxy observations to verifiability. Advances in Water Resources, 201, 104955. https://www.sciencedirect.com/science/article/pii/S0309170825000697
Published in Environmetrics, 2025
Recommended citation: Janssen, J., Meng, S., Haris, A., Schrunner, S., Cao, J., Welch, W. J., ... & Ameli, A. A. (2025). Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science. Environmetrics, 36(4), e70018. https://onlinelibrary.wiley.com/doi/full/10.1002/env.70018
Published in Journal of the Royal Statistical Society Series C: Applied Statistics, 2025
Recommended citation: Schrunner, S., Pishrobat, P., Janssen, J., Jenul, A., Cao, J., Ameli, A. A., & Welch, W. J. (2025). A Gaussian sliding windows regression model for hydrological inference. Journal of the Royal Statistical Society Series C: Applied Statistics, qlaf009. https://academic.oup.com/jrsssc/article/74/4/946/8015432
Published in arxiv, 2024
Recommended citation: Guan, V., Janssen, J., Rahmani, H., Warren, A., Zhang, S., Robeva, E., & Schiebinger, G. (2024). Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots. arXiv preprint arXiv:2410.22729. https://arxiv.org/abs/2410.22729
Published in Frontiers in Environmental Science, 2023
Recommended citation: Le, E., Janssen, J., Hammond, J., & Ameli, A. A. (2023). The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis. Frontiers in Environmental Science, 11, 1207508. https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1207508/full
Published in arxiv, 2023
Recommended citation: Bilolikar, D. K., More, A., Gong, A., & Janssen, J. (2023). How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology. arXiv preprint arXiv:2305.07136. https://arxiv.org/abs/2305.07136
Published in AISTATS, 2023
Recommended citation: Janssen, J., Guan, V., & Robeva, E. (2023, April). Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees. In International Conference on Artificial Intelligence and Statistics (pp. 10782-10814). PMLR. https://proceedings.mlr.press/v206/janssen23a.html
Published in Frontiers in Earth Science, 2021
Recommended citation: Janssen, J., Radić, V., & Ameli, A. (2021). Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America. Frontiers in Earth Science, 9, 730631. https://www.frontiersin.org/articles/10.3389/feart.2021.730631/
Published in Water Resources Research, 2021
Recommended citation: Janssen, J., & Ameli, A. A. (2021). A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions. Water Resources Research, 57(9), e2021WR030263. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021WR030263