Sloan Nietert

profile picture

email: nietert "at" cs.cornell.edu

office: 336 Gates Hall

about

I am a fourth-year Ph.D. student in Computer Science at Cornell University, where I am fortunate to be advised by Ziv Goldfeld. I received my B.S. in Mathematical Sciences and B.A. in Computer Science from Clemson University. My research interests include learning theory, algorithms, and statistics in high dimensions, with a particular focus on optimal transport. I am supported by an NSF Graduate Research Fellowship.

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.

Outside of my research, I'm an avid climber and am passionate about fair redistricting.

research
journal conference workshop preprint

C5 Statistical, robustness, and computational guarantees for sliced Wasserstein distances. Sloan Nietert, Ritwik Sadhu, Ziv Goldfeld, Kengo Kato. Accepted to Neural Information Processing Systems (NeurIPS) 2022. [arXiv]
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]
P1 Limit distribution theory for smooth p-Wasserstein distances. Ziv Goldfeld, Kengo Kato, Sloan Nietert, and Gabriel Rioux. 2022. [arXiv]
C3 Outlier-robust optimal transport: duality, structure, and statistical analysis. Sloan Nietert, Rachel Cummings, and Ziv Goldfeld. AISTATS 2022. [arXiv, conference version, poster]
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]
C1 Learning with comparison feedback: online estimation of sample statistics. Michela Meister and Sloan Nietert. Algorithmic Learning Theory (ALT) 2021. [arXiv, conference version]
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]
J2 Polarization, sign sequences and isotropic vector systems. Gergely Ambrus and Sloan Nietert. Pacific Journal of Mathematics 2019. [arXiv, journal version]
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]

awards & fellowships

  • 2019-Present: 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

  • 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