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publications
A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions
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
Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America
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/
Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees
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
How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology
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
The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis
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
Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots
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
A Gaussian sliding windows regression model for hydrological inference
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
Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science
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
Tackling water table depth modeling via machine learning: From proxy observations to verifiability
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
A Causal Inference Framework for Catchment Boundary Delineation
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
Gradient-flow SDEs have unique transient population dynamics
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
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
EOSC 533: Advanced Groundwater Hydrology
Graduate course, University of British Columbia, Department of Earth, Ocean and Atmospheric Sciences, 2021
Helped students with MODFLOW labs, finite element problems, and basic groundwater problems
EOSC 118: Earth’s Treasures: Gold and Gems
TA, University of British Columbia, Department of Earth, Ocean and Atmospheric Sciences, 2022
Graded assigments and provided office hours for students who needed help with course material and assignments