Runzhong Wang - 汪润中
/ˌru:ndʒɔ:ŋ ˈwɑːŋ/ Any other approximations are acceptable.
runzhong AT mit DOT edu

I am a Postdoc working with Prof. Connor Coley at MIT. Prior to that, I graduate as a PhD from Department of CSE, Shanghai Jiao Tong University (SJTU), supervised by Prof. Xiaokang Yang and Prof. Junchi Yan.
I do research at the intersection of machine learning, optimization, and computational metabolomics. More specifically, I develop computational methods on solving combinatorial optimization problems, and their applications in computational metabolomics, and other general science topics.
I serve as the reviewer for conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, AAAI, IJCAI, MM) and journals (IJCV, TPAMI, TNNLS, PR, TMM, TCSVT).

What's New

  • One paper on learning combinatorial optimization is accepted by NeurIPS2023! Congrats to Yang Li!
  • I am joining MIT as a Postdoc!
  • One paper on learning virtual network embedding is accepted by KDD2023! Congrats to Haoyu!
  • I successfully passed my thesis defense!
  • One paper (first author) on satisfiability and combinatorial optimization is accepted by ICML2023!
  • One paper (first author) on unsupervised learning of graph matching is accepted by TPAMI!
  • One paper (co-first author) on learning graph matching is accepted by CVPR2023! Congrats to Ziao!
  • One paper accepted by ICASSP 2023! Congrats to Wujiang!
  • One paper (first author) on learning combinatorial optimizaiton is accepted by ICLR2023!
  • Pygmtools now supports numpy/pytorch/jittor/paddle. Install it by pip install pygmtools!

Code and Software

pygmtools

  • A graph matching tool with python API.
  • GPU compatible and deep learning ready.
  • Supported backends: numpy, pytorch, jittor, and paddle.
  • Install it by pip install pygmtools.

ThinkMatch

  • A collection of state-of-the-art deep graph matching models.
  • Official implementation of our models: NGM(v2), GANN-MGM, PCA-GM, CIE.
  • Implementation of other state-of-the-art models: GMN, BBGM.

Awesome ML4CO

  • A collection of awesome machine learning for combinatorial optimization papers.
  • Pull Requests are welcomed to add more papers!

PPO-BiHyb

  • A bi-level framework for learning combinatorial optimization.
  • Upper-level: A reinforcement learning model (PPO) to optmize the graph structure.
  • Lower-level: A fast heuristic algorithm running on the optimized graph.
  • Official implementation on 3 problems: DAG scheduling, graph edit distance and Hamiltonian cycle problem.

Thesis

排列型组合优化问题的机器学习求解方法研究.
上海交通大学学位论文.
Machine Learning Solvers for Permutation-based Combinatorial Optimization Solvers.
A Dissertation Submitted to Shanghai Jiao Tong University for Doctoral Degree.
Runzhong Wang, Advisors: Xiaokang Yang, Junchi Yan.
[thesis (in Chinese)]

Publications

From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization.
Yang Li, Jinpei Guo, Runzhong Wang, Junchi Yan.
NeurIPS 2023.
[paper] [code]

GAL-VNE: Solving the VNE Problem with Global Reinforcement Learning and Local One-Shot Neural Prediction.
Haoyu Geng, Runzhong Wang, Fei Wu, Junchi Yan.
KDD 2023.
[paper] [code]

InstaBoost++: Visual Coherence Principles for Unified 2D/3D Instance Level Data Augmentation.
Jianhua Sun, Hao-Shu Fang, Yuxuan Li, Runzhong Wang, Minghao Gou, Cewu Lu.
IJCV 2023 (vol. 131, pp. 2665-2681).
[paper] [code]

LinSATNet: The Positive Linear Satisfiability Neural Networks.
Runzhong Wang, Yunhao Zhang, Ziao Guo, Tianyi Chen, Xiaokang Yang, Junchi Yan.
ICML 2023.
[paper] [code]

Unsupervised Learning of Graph Matching with Mixture of Modes via Discrepancy Minimization.
Runzhong Wang, Junchi Yan, Xiaokang Yang.
TPAMI 2023 (vol. 45, no. 8, pp. 10500-10518).
[paper] [project page] [code]

Deep Learning of Partial Graph Matching via Differentiable Top-K.
*Runzhong Wang, *Ziao Guo, Shaofei Jiang, Xiaokang Yang, Junchi Yan. (* equal contribution)
CVPR 2023.
[paper] [code]

MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization.
Wujiang Xu, Runzhong Wang, Xiaobo Guo, Shaoshuai Li, Qiongxu Ma, Yunan Zhao, Sheng Guo, Zhenfeng Zhu, Junchi Yan.
ICASSP 2023.
[paper]

Towards One-Shot Neural Combinatorial Optimization Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case.
Runzhong Wang, Li Shen, Yiting Chen, Xiaokang Yang, Dacheng Tao, Junchi Yan.
ICLR 2023.
[paper] [code]

Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching.
Chang Liu, Zetian Jiang, Runzhong Wang, Lingxiao Huang, Pinyan Lu, Junchi Yan.
ICLR 2023.
[paper] [code]

ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs.
Han Lu, Zenan Li, Runzhong Wang, Qibing Ren, Xijun Li, Mingxuan Yuan, Jia Zeng, Xiaokang Yang, Junchi Yan.
ICLR 2023.
[paper] [code]

Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning.
Chang Liu, Chenfei Lou, Runzhong Wang, Alan Yuhan Xi, Li Shen, Junchi Yan.
ICML 2022.
[paper] [code]

Appearance and structure aware robust deep visual graph matching: Attack, defense and beyond.
Qibing Ren, Qingquan Bao, Runzhong Wang, Junchi Yan.
CVPR 2022.
[paper] [code]

A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs.
Runzhong Wang, Zhigang Hua, Gan Liu, Jiayi Zhang, Junchi Yan, Feng Qi, Shuang Yang, Jun Zhou, Xiaokang Yang.
NeurIPS 2021.
[paper] [code]

Deep Latent Graph Matching.
Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li.
ICML 2021.
[paper]

Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching.
Runzhong Wang, Junchi Yan, Xiaokang Yang.
TPAMI 2022 (vol. 44, no. 9, pp. 5261-5279).
Best poster award at Internation Conference on Data Science 2019.
[paper] [project page] [code]

Combinatorial Learning of Graph Edit Distance via Dynamic Embedding.
Runzhong Wang, Tianqi Zhang, Tianshu Yu, Junchi Yan, Xiaokang Yang.
CVPR 2021.
[paper] [code]

Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning.
Runzhong Wang, Junchi Yan, Xiaokang Yang.
NeurIPS 2020.
[paper] [project page] [code]

Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach.
Runzhong Wang, Junchi Yan, Xiaokang Yang.
TPAMI 2023 (vol. 45, no. 6, pp. 6984 - 7000).
[paper] [project page] [code]

Learning deep graph matching with channel-independent embedding and Hungarian attention.
Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li.
ICLR 2020.
[paper] [code]

Learning Combinatorial Embedding Networks for Deep Graph Matching.
Runzhong Wang, Junchi Yan, Xiaokang Yang.
ICCV 2019 (oral).
[paper] [project page] [code] [slides]

InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting.
*Runzhong Wang, *Hao-shu Fang, *Jianhua Sun, Minghao Gou, Yong-Lu Li, Cewu Lu. (* equal contribution)
ICCV 2019.
[paper] [code]

Work

Sep 2023 to now
Postdoc Associate
Department of Chemical Engineering, MIT

Education

Sep 2019 to Jun 2023
PhD of Comupter Science and Technology (with Honor)
Wen-Tsün Wu Honorable Class, AI Institute
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University (SJTU)

Sep 2015 to Jun 2019
Bachelor of Engineering in Information Engineering
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University (SJTU)

Awards

Excellent Graduate Student of Shanghai Jiao Tong University in 2023 (上海交通大学优秀毕业生)

National Scholarship in 2022 (国家奖学金)

Outstanding Reviewer of ICML 2022

CCF-CV Outstanding Young Researcher Award in 2021 (3 winners in China, CCF-CV学术新锐奖)

Computer Science Fellowship at Shanghai Jiao Tong University in 2021 (3 winners SJTU, 上海交大85届计算机系奖学金)

Nomination Award of the 2021 MSRA Fellowship (17 nominations in Asia)

Jiachi Yang honorary scholarship in 2020 (杨嘉墀奖学金)

Best poster award at International Conference on Data Science 2019 (1 winner)

Xu Zhang honorary scholarship in 2019 (张煦奖学金)