about
I am a postdoctoral fellow at EPFL, where I am hosted by Daniel Kuhn. I recently completed my Ph.D. in Computer Science at Cornell University, where I was advised by Ziv Goldfeld and supported by an NSF Graduate Research Fellowship. I received my B.S. in Mathematical Sciences and B.A. in Computer Science from Clemson University.
My research provides statistical and computational guarantees for robust and geometry-aware machine learning, often through the lens of optimal transport. More broadly, I am interested in machine learning theory, online algorithms, and high-dimensional statistics.
Prior to starting my graduate studies, I spent an academic year in Budapest, Hungary, as a researcher at the Alfréd Rényi Institute of Mathematics and as a student and lecturer with Budapest Semesters in Mathematics. My work was supported by the Fulbright U.S. Student Program and was supervised by Gergely Ambrus and Mihály Weiner.
updates
- October 2025: I will be speaking at INFORMS in Atlanta, Georgia.
- September 2025: I am attending the Swiss CLOCK Summit in Engelberg, Switzerland.
- August 2025: I moved to Lausanne, Switzerland, to start my postdoc at EPFL.
- July 2025: I defended my Ph.D. at Cornell!
- February 2025: I received the Sun Prize (top student award) for my talk on robust distribution estimation at the Information Theory and Applications (ITA) Graduation Day!
research
journal
conference
workshop
preprint
| C8 |
Robust alignment via partial Gromov-Wasserstein distances. Xiaoyun Gong, Sloan Nietert, and Ziv Goldfeld. International Symposium on Information Theory (ISIT) 2025.
[arXiv,
]
@inproceedings{gong_2025_robust_alignment,
title={Robust alignment via partial Gromov-Wasserstein distances},
author={Gong, Xiaoyun and Nietert, Sloan and Goldfeld, Ziv},
booktitle={International Symposium on Information Theory (ISIT)},
year={2025}
}
|
| C7 |
Robust distribution learning with local and global adversarial corruptions. Sloan Nietert, Ziv Goldfeld, and Soroosh Shafiee. Conference on Learning Theory (COLT) 2024.
[arXiv,
]
@inproceedings{nietert_2024_distribution_learning,
title={Robust distribution learning with local and global adversarial corruptions},
author={Nietert, Sloan and Goldfeld, Ziv and Shafiee, Soroosh},
booktitle={Conference on Learning Theory (COLT)},
year={2024}
}
Recieved top prize for talk on this work at Information Theory and Applications (ITA) Graduation Day 2025! |
| C6 |
Outlier-robust Wasserstein DRO. Sloan Nietert, Ziv Goldfeld, and Soroosh Shafiee. Neural Information Processing Systems (NeurIPS) 2023.
[arXiv,
]
@inproceedings{nietert_2024_OR-WDRO,
title={Outlier-robust Wasserstein DRO},
author={Nietert, Sloan and Goldfeld, Ziv and Shafiee, Soroosh},
booktitle={Neural Information Processing Systems (NeurIPS)},
year={2023}
}
|
| P1 |
Robust estimation under the Wasserstein distance. Sloan Nietert, Rachel Cummings, and Ziv Goldfeld. 2023. [arXiv,
]
@article{nietert_2023_robust_wasserstein,
title={Robust estimation under the Wasserstein distance},
author={Nietert, Sloan and Cummings, Rachel and Goldfeld, Ziv},
journal={arXiv preprint arXiv:2501.01234},
year={2023}
}
|
| C5 |
Statistical, robustness, and computational guarantees for sliced Wasserstein distances. Sloan Nietert, Ritwik Sadhu, Ziv Goldfeld, and Kengo Kato. Neural Information Processing Systems (NeurIPS) 2022. [arXiv,
]
@inproceedings{nietert_2022_sliced_ot,
title={Statistical, robustness, and computational guarantees for sliced Wasserstein distances},
author={Nietert, Sloan and Sadhu, Ritwik and Goldfeld, Ziv and Kato, Kengo},
booktitle={Neural Information Processing Systems (NeurIPS)},
year={2022}
}
|
| C4 |
Learning in Stackelberg games with non-myopic agents. Nika Haghtalab, Thodoris Lykouris, Sloan Nietert, and Alexander Wei. ACM Conference on Economics and Computation (EC) 2022. [arXiv,
]
@inproceedings{nietert_2022_stackelberg,
title={Learning in Stackelberg games with non-myopic agents},
author={Haghtalab, Nika and Lykouris, Thodoris and Nietert, Sloan and Wei, Alexander},
booktitle={ACM Conference on Economics and Computation (EC)},
year={2022}
}
|
| J4 |
Limit distribution theory for smooth p-Wasserstein distances. Ziv Goldfeld, Kengo Kato, Sloan Nietert, and Gabriel Rioux. Annals of Applied Probability 2024. [arXiv, journal version,
]
@inproceedings{goldfeld_2024_smooth_Wp,
title={Limit distribution theory for smooth p-Wasserstein distances},
author={Goldfeld, Ziv and Kato, Kengo and Nietert, Sloan and Rioux, Gabriel},
booktitle={Annals of Applied Probability},
year={2024}
}
|
| C3 |
Outlier-robust optimal transport: duality, structure, and statistical analysis. Sloan Nietert, Rachel Cummings, and Ziv Goldfeld. International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. [arXiv, conference version, poster,
]
@inproceedings{nietert_2022_robust_OT,
title={Outlier-robust optimal transport: duality, structure, and statistical analysis},
author={Nietert, Sloan and Cummings, Rachel and Goldfeld, Ziv},
booktitle={International Conference on Artificial Intelligence and Statistics (AISTATS)},
year={2022}
}
|
| W1 | Outlier-robust optimal transport with applications to generative modeling and data privacy. Sloan Nietert, Rachel Cummings, and Ziv Goldfeld. Presented at Theory and Practice of Differential Privacy (TPDP) Workshop at ICML 2021. [poster] |
| C2 |
Smooth p-Wasserstein distance: structure, empirical approximation, and statistical applications. Sloan Nietert, Ziv Goldfeld, and Kengo Kato. International Conference on Machine Learning (ICML) 2021. [arXiv, conference version, poster,
]
@inproceedings{nietert_2021_smooth_Wp,
title={Smooth p-Wasserstein distance: structure, empirical approximation, and statistical applications},
author={Nietert, Sloan and Goldfeld, Ziv and Kengo, Kato},
booktitle={International Conference on Machine Learning (ICML)},
year={2021}
}
|
| C1 |
Learning with comparison feedback: online estimation of sample statistics. Michela Meister and Sloan Nietert. Algorithmic Learning Theory (ALT) 2021. [arXiv, conference version,
]
@inproceedings{meister_2021_comparison,
title={Learning with comparison feedback: online estimation of sample statistics},
author={Meister, Michela and Nietert, Sloan},
booktitle={Algorithmic Learning Theory (ALT)},
year={2021}
}
|
| J3 |
Rigidity and a common framework for mutually unbiased bases and k-nets. Sloan Nietert, Zsombor Szilágyi, and Mihály Weiner. Journal of Combinatorial Designs 2020. [arXiv, journal version, poster,
]
@article{nietert_2020_MUB_rigidity,
title={Rigidity and a common framework for mutually unbiased bases and k-nets},
author={Nietert, Sloan and Szilágyi, Zsombor and Weiner, Mihály},
journal={Journal of Combinatorial Designs},
year={2020}
}
|
| J2 |
Polarization, sign sequences and isotropic vector systems. Gergely Ambrus and Sloan Nietert. Pacific Journal of Mathematics 2019. [arXiv, journal version,
]
@article{ambrus_2019_polarization,
title={Polarization, sign sequences and isotropic vector systems},
author={Ambrus, Gergely and Nietert, Sloan},
booktitle={Pacific Journal of Mathematics},
year={2019}
}
|
| J1 |
Assessing the correlation between physical activity and quality of life in advanced lung cancer. Brett Bade, Mary Brooks, Sloan Nietert, Ansley Ulmer, David Thomas, Paul Nietert, JoAnn Scott, and Gerard Silvestri. Integrative Cancer Therapies 2018. [pubmed, journal version,
]
@article{bade_2018_lung_cancer,
title={Assessing the correlation between physical activity and quality of life in advanced lung cancer},
author={Bade, Brett and Brooks, Mary and Nietert, Sloan and Ulmer, Ansley and Thomas, David and Nietert, Paul and Scott, JoAnn and Silvestri, Gerard},
booktitle={Integrative Cancer Therapies},
year={2018}
}
|
awards & fellowships
- 2019-2025: NSF Graduate Research Fellow
- 2018-2019: Fulbright U.S. Student Grantee
- Summer 2018: MIT/Tufts Voter Rights Data Institute Fellow
- Spring 2018: Clemson Outstanding Senior in Science
- Fall 2017: HackMIT 2nd Place Overall
- Summer 2014: NSLI-Y Scholar in Seoul, South Korea
teaching
- Spring 2025: ECE 3200/5200 - Fundamentals of Machine Learning; Teaching Assistant, Cornell University
- Spring 2024: ECE 4200/5200 - Fundamentals of Machine Learning; Teaching Assistant, Cornell University
- Fall 2022: ECE 5110/4110 - Random Signals in Communications and Signal Processing; Teaching Assistant, Cornell University
- Spring 2021: ECE 6970 - Statistical Distances for Modern Machine Learning; Teaching Assistant, Cornell University
- Fall 2018, Spring 2019: GRE Math Subject Test Preparation Seminar; Lecturer, Budapest Semesters in Mathematics