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.
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.
He is pursuing the following:
Theory foundations of DL;
Efficient and Robust algorithms in ML.
* 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.
He is open to discussions.