Yijun Dong (董一珺)

About Me

I am a Courant Instructor/Assistant Professor (postdoc) at the Courant Institute of New York University. I completed my PhD at the Oden Institute of UT Austin, advised by Prof. Per-Gunnar Martinsson and Prof. Rachel Ward.

My research sits at the intersection of numerical linear algebra, high-dimensional probability, and statistical learning theory. I am interested in computationally and data-efficient algorithms for high-dimensional problems in machine learning and scientific computing. Algorithmically, I design fast and reliable randomized algorithms for dimension reduction, data selection, and model pruning. Theoretically, I work on the mathematical foundation of learning paradigms like data augmentation, knowledge distillation, and post-training alignment.

I will be on the 2025-2026 job market, looking for full-time academia and industry positions globally. Please feel free to reach out!

Selected Publications

• Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension.
  Yijun Dong, Yicheng Li, Yunai Li, Jason D. Lee, Qi Lei.
  ICML 2025.
  

• Robust Blockwise Random Pivoting: Fast and Accurate Adaptive Interpolative Decomposition.
  Yijun Dong, Chao Chen, Per-Gunnar Martinsson, Katherine Pearce.
  SIAM Journal on Matrix Analysis and Applications 2025. [GitHub]
  

• Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning.
  Yijun Dong*, Hoang Phan*, Xiang Pan*, Qi Lei.
  NeurIPS 2024. [GitHub]
  

• Efficient Bounds and Estimates for Canonical Angles in Randomized Subspace Approximations.
  Yijun Dong, Per-Gunnar Martinsson, Yuji Nakatsukasa.
  SIAM Journal on Matrix Analysis and Applications 2024. [GitHub]
  

• Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering.
  Yijun Dong*, Kevin Miller*, Qi Lei, Rachel Ward.
  NeurIPS 2023. [GitHub]
  

• Sample Efficiency of Data Augmentation Consistency Regularization.
  Shuo Yang*, Yijun Dong*, Rachel Ward, Inderjit Dhillon, Sujay Sanghavi, Qi Lei.
  AISTATS 2023. [pmlr]
  

• Simpler is better: A comparative study of randomized algorithms for computing the CUR decomposition.
  Yijun Dong, Per-Gunnar Martinsson.
  Advances in Computational Mathematics 2023. [GitHub]
  

(* denotes equal contribution or alphabetical order)

Education

Ph.D. in Computational Science, Engineering, and Mathematics, 2018 - 2023
Oden Institute for Computational Engineering and Sciences, UT Austin, Austin, Texas, US
Thesis: Randomized Dimension Reduction with Statistical Guarantees

B.S. in Applied Mathematics & Engineering Science, 2014 - 2018
Emory University, Atlanta, Georgia, US
Thesis: Crystals and Liquids in Gravitationally Confined Quasi-2-Dimensional Colloidal Systems