He is Ruomin Huang (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, especially the following topics. Recently, he is interested in the Edge of Stability (EoS) phenomenon in deep learning.
 Statistical learning theory
 Generative models
 Optimization
 Optimal Transport
 Computation
 Application in machine learning
He is pursuing the following.

Theory foundation of deep learning;

Efficient algorithms in machine learning;

Robust algorithms in machine learning.
Publications
* denotes equal contribution.
 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 enjoys seminars and is open to discussions.
 Learning Theory Seminar (2022 fall) on the book:
 Feature Learning Seminar (2022 spring) on papers:
 the Tensor Program series, by Greg Yang, et al.
 HighDimensional Statistics Seminar (2021 fall) on books:
 Spectral and Algebraic Graph Seminar (2021 fall) on the note: