Danielle Maddix Robinson

Danielle Maddix Robinson

Senior Applied Scientist, AWS AI Labs, PhD in Computational and Mathematical Engineering, Stanford University

Publications

For a full list of my publications please see: my Google Scholar profile.

Selected Publications

Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility

Published in The Fourteenth International Conference on Learning Representations (ICLR), 2026

Our TSFM Compression code is on the amazon-science github.

Recommended citation: Yu, A., Maddix, D.C., Han, B., Zhang, X., Ansari, A.F., Shchur, O., Faloutsos, C., Wilson, A.G., Mahoney, M.W., Wang, Y., (2026). "Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility. " The Fourteenth International Conference on Learning Representations (ICLR). https://openreview.net/pdf?id=axR2KZwaD3

Understanding the Implicit Biases of Design Choices for Time Series Foundation Models

Published in The Fourteenth International Conference on Learning Representations (ICLR), 2026

Our TSFM Biases code is on the amazon-science github.

Recommended citation: Yu, A., Maddix, D.C., Han, B., Zhang, X., Ansari, A.F., Shchur, O., Faloutsos, C., Wilson, A.G., Mahoney, M.W., Wang, Y., (2026). "Understanding the Implicit Biases of Design Choices for Time Series Foundation Models. " The Fourteenth International Conference on Learning Representations (ICLR). https://openreview.net/pdf?id=5jkzTzV5Ao

Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics

Published in Data Science in Science, 2026

Our DLWP benchmarking code is on the amazon-science github.

Recommended citation: Karlbauer, M., Maddix, D.C., Ansari, A.F., Han, B., Gupta, G., Wang, Y., Stuart, A., Mahoney, M.W., (2026). "Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics." Data Science in Science. https://arxiv.org/abs/2407.14129

MITRA: Mixed Synthetic Priors for Enhancing Tabular Foundation Models

Published in Proceedings of the 39th Conference of Neural Information Processing Systems (NeurIPS), 2025

Our Mitra regressor checkpoint and classifier checkpoint are on HuggingFace!

Recommended citation: Zhang, X., Maddix, D.C., Yin, J., Erickson, N., Ansari, A.F., Han, B., Zhang, S., Akoglu, L., Faloutsos, C., Mahoney, M.W., Wang, Y., Hu, C., Rangwala, H., Karypis, G., Wang, B. (2025). "MITRA: Mixed Synthetic Priors for Enhancing Tabular Foundation Models. " Proceedings of the 39th Conference of Neural Information Processing Systems (NeurIPS). https://arxiv.org/abs/2510.21204

Early Warning of Complex Climate Risk with Integrated Artificial Intelligence

Published in Nature Communications, 2025

Recommended citation: Reichstein, M., Benson, V., Blunk, J., Camps-Valls, G., Creuzig, F., Fearnley, C.J., Han, B., Kornhuber, K., , Rahaman, N., Schölkopf, B., Tárraga, J.M., Vinuesa, R., Dall, K., Denzler, J., Frank, D., Martini, G., Nganga, N., Maddix, D.C., Weldemariam, K., (2025). "Early Warning of Complex Climate Risk with Integrated Artificial Intelligence." Nature Communications 16, 2564. https://rdcu.be/eefiY

DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics

Published in Technical Report, Preprint arXiv:2408.11969, 2024

Our DrivAerML dataset is on HuggingFace.

Recommended citation: Ashton, N., Mockett, C., Fuchs, M., Fliessbach, L., Hetmann, H., Knacke, T., Schonwald, N., Skaperdas, V., Fotiadis, G., Walle, A., Hupertz, B., Maddix, D.C., (2024). "DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics." Technical Report, Preprint arXiv:2408.11969. https://arxiv.org/abs/2408.11969

WindsorML: High-Fidelity Computational Fluid Dynamics Dataset for Automotive Aerodynamics

Published in Proceedings of the 38th Conference of Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2024

Our WindsorML dataset is on HuggingFace.

Recommended citation: Ashton, N., Angel, J.B., Ghate, A.S., Kenway, G.K.W., Wong, M.L., Kiris, C., Walle, A., Maddix, D.C., Page, G., (2024). "WindsorML: High-Fidelity Computational Fluid Dynamics Dataset for Automotive Aerodynamics." Proceedings of the 38th Conference of Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 37:27823-37835. https://proceedings.neurips.cc/paper_files/paper/2024/file/42a59a5f35b1b3c3fd648397c88a7164-Paper-Datasets_and_Benchmarks_Track.pdf

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs

Published in Proceedings of the 41st International Conference on Machine Learning (ICML), 2024

Our Operator-ProbConserv code is on the amazon-science github.

Recommended citation: Mouli, S.C., Maddix, D.C., Alizadeh, S., Gupta, G., Wang, Y., Stuart, A., Mahoney, M.W. (2024). "Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs." Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR. 235:36372-36418. https://proceedings.mlr.press/v235/mouli24a.html

Chronos: Learning the Language of Time Series

Published in Transactions on Machine Learning Research (TMLR), 2024

Our Chronos code is on the amazon-science github.

Recommended citation: Ansari, F.A., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., Shchur, O., Rangapuram, S.S., Arango, S.A., Kapoor, S., Zschiegner, J., Maddix, D.C., et al. (2024). "Chronos: Learning the Language of Time Series." Transactions on Machine Learning Research (10/2024). https://www.stat.berkeley.edu/~mmahoney/pubs/2619_Chronos_Learning_the_Lang.pdf

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Published in Proceedings of the 37th Conference of Neural Information Processing Systems (NeurIPS), 2023

Our PreDiff code is available on github.

Recommended citation: Gao, Z., Shi, X., Han, B., Wang, H., Jin, X., Maddix, D.C., Zhu, Y., Li, M., Wang, Y. (2023). "PreDiff: Precipitation Nowcasting with Latent Diffusion Models." Proceedings of the 37th Conference of Neural Information Processing Systems (NeurIPS). https://proceedings.neurips.cc/paper_files/paper/2023/file/f82ba6a6b981fbbecf5f2ee5de7db39c-Paper-Conference.pdf

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting

Published in Proceedings of the 40th International Conference on Machine Learning (ICML), 2023

Recommended citation: Hasson, H., Maddix, D.C., Wang, Y., Park, Y., Gupta, G., (2023). "Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting." Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR. 202:12616-12632. https://proceedings.mlr.press/v202/hasson23a/hasson23a.pdf

Guiding Continuous Operator Learning through Physics-based boundary constraints

Published in Proceedings of the International Conference on Learning Representations (ICLR), 2023

Our Boundary enforcing Operator Network (BOON) code is on the amazon-science github.

Recommended citation: Saad, N.*, Gupta, G.*, Alizadeh, S., Maddix, D.C. (2023). "Guiding Continuous Operator Learning through Physics-based boundary constraints." Proceedings of the International Conference on Learning Representations (ICLR). https://www.amazon.science/publications/guiding-continuous-operator-learning-through-physics-based-boundary-constraints

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting

Published in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Our Incremental Quantile Function (IQF) code is incorporated into the MQ-CNN Estimator in GluonTS.

Recommended citation: Park, Y., Maddix, D.C., Aubet, FX., Kan, K., Gasthaus, J., Wang, Y. (2022). "Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting." Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR. 151:8127-8150. https://proceedings.mlr.press/v151/park22a.html

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems

Published in Proceedings of the 3rd Conference on Learning for Dynamics and Control (L4DC), 2021

Our AutoODE code is on github.

Recommended citation: Wang, R., Maddix, D.C., Faloutsos, C., Wang, Y., Yu, R. (2021). "Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems." Proceedings of the 3rd Conference on Learning for Dynamics and Control (L4DC), PMLR. 144:385-398. http://proceedings.mlr.press/v144/wang21a/wang21a.pdf

GluonTS: Probabilistic and Neural Time Series Modeling in Python

Published in Proceedings of the 39th International Conference on Machine Learning (ICML), 2020

GluonTS Code

Recommended citation: Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D.C., Rangapuram, S., Salinas, D., Schulz, J., Stella, L., Türkmen, A.C., Wang, Y. (2020). "GluonTS: Probabilistic and Neural Time Series Modeling in Python." Journal of Machine Learning Research (JMLR). 21(116):1-6. https://www.jmlr.org/papers/v21/19-820.html

Advanced Fluid Reduced Order Models for Compressible Flow

Published in Sandia National Laboratories Report, Sand No. 2017-10335, 2017

Chapter 5: Structure preservation in finite volume ROMs via physics-based constraints

Recommended citation: Tezaur, I.K., Fike, J., Carlberg, K., Barone, M., Maddix, D.C., Mussoni, E., Balajewicz, M. (2017). "Advanced Fluid Reduced Order Models for Compressible Flow." Sandia National Laboratories Report, Sand No. 2017-10335. https://www.osti.gov/servlets/purl/1395816