He is Ruomin Huang (黄若民 in Chinese), a first-year CS Ph.D. student at Duke University, advised by Prof. Rong Ge. Previously he recieved his master’s and bachelor’s degrees at USTC, where he worked with Prof. Hu Ding.

Research Interests:

Previously he worked on the algorithmic aspect of machine learning (ML). Now he is generally interested in ML theory, especially deep learning (DL) theory. Here lists some intriguing DL topics.

• In-context learning
• Feature learning
• Edge of stability

He is pursuing the following:

• Theory foundations of DL;

• Efficient and Robust algorithms in ML.

Publications

* denotes equal contribution.

• Hu Ding, Ruomin Huang, Kai Liu, Haikuo Yu, Zixiu Wang, Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers, submitted for publication.

• Ruomin Huang, Jiawei Huang, Wenjie Liu, Hu Ding, Coresets for Wasserstein Distributionally Robust Optimization Problems, NeurIPS 2022 (spotlight).

• Jiaxiang Chen, Qingyuan Yang, Ruomin Huang, Hu Ding, Coresets for Relational Data and The Applications, NeurIPS 2022 (spotlight).

• Jiawei Huang*, Ruomin Huang*, Wenjie Liu*, Nikolaos M. Freris, Hu Ding, A Novel Sequential Coreset Method for Gradient Descent Algorithms, ICML 2021.

Teaching Assistant

Undergraduate course

• 01118601 Algorithms for Big Data (2021 spring, USTC)

Doctoral course

• COMP7102P01 Advanced Algorithms Design and Analysis (2021 fall, USTC)

Seminar

He is open to discussions.

• Feature Learning Seminar (2022 spring) on papers:
  • the Tensor Program series, by Greg Yang, et al.
• High-Dimensional Statistics Seminar (2021 fall) on books:
  • High-Dimensional Statistics: A Non-Asymptotic Viewpoint, by Martin J. Wainwright.
  • High-Dimensional Probability: An Introduction with Applications in Data Science, by Roman Vershynin.
• Spectral and Algebraic Graph Seminar (2021 fall) on the note:
  • Spectral and Algebraic Graph Theory, by Daniel A. Spielman.