He is Ruomin Huang (黄若民 in Chinese and he usually goes by Focke).

Educational Experience

• He’s currently a graduate student in the School of Data Science at USTC, DIAG Group, supervised by Prof. Hu Ding. (2020 ~ Present)

• He received his bachelor’s degree in Information and Computational Science from the School of Mathematical Science, USTC. (2016 ~ 2020)

Research Interests:

He is interested in Machine Learning (ML), especially the following topics. Recently, he is interested in the Edge of Stability (EoS) phenomenon in deep learning (DL).

• Statistical learning theory
  • Kernel methods
• Generative models
  • Diffusion models
• Optimization
  • Stochastic optimization
• Optimal transport
  • Computation
  • Applications in ML
• Algorithmic problems in ML
  • Coresets

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.

• Learning Theory Seminar (2022 fall) on the book:
  • Learning Theory from First Principles, by Francis Bach.
• 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.