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, Nat Commun, TNNLS, PR, TMM, TCSVT).

What's New

  • [05/2025] Two papers on mass spec are accepted by ICML 2025! Congrats to Monte and Rui-Xi!
  • [05/2025] One paper on BO is accepted by JCIM! Congrats to Jenna!
  • [01/2025] Two papers on CO are accepted by WWW2025 and ICLR2025! Congrats to Wenxuan and Yang!
  • [09/2024] Three papers (fast T2T, predictive CO, synthesis planning) are accepted by NeurIPS 2024! Congrats to Yang, Haoyu, Kevin!
  • [07/2024] One paper on LLM+CO for archaeology (Dunhuang manuscript) is accepted by ECCV2024! Congrats to Yuqing!
  • [06/2024] One paper (first author) on combinatorial optimization is accepted by SCIENTIA SINICA Informationis!
  • [01/2024] Our Pygmtools (first author) is accepted by JMLR! It supports numpy/pytorch/jittor/paddle/tf/ms. Install it by pip install pygmtools!
  • [01/2024] One paper accepted by WWW2024! Congrats to Wujiang!
  • [01/2024] One paper on learning combinatorial optimization is accepted by ICLR2024! Congrats to Xinyan and Yang!

Code and Software

ms-pred

  • A collection of state-of-the-art deep learning mass spectrum predictors.
  • Official implementation of our models: ICEBERG, MARASON, SCARF.
  • Reimplementation of other state-of-the-arts: NEIMS, MassFormer, 3DMolMS, GrAFF-MS.

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, AFAT, LinSAT.
  • Reimplementation of other state-of-the-arts: GMN, BBGM, COMMON.

Awesome ML4CO

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

Publications

Neural Graph Matching Improves Retrieval Augmented Generation in Molecular Machine Learning.
Runzhong Wang, Rui-Xi Wang, Mrunali Manjrekar, Connor W. Coley.
ICML 2025.
[preprint] [code coming soon]

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra.
Montgomery Bohde, Mrunali Manjrekar, Runzhong Wang, Shuiwang Ji, Connor W. Coley.
ICML 2025.
[preprint] [code]

Batched Bayesian optimization with correlated candidate uncertainties.
Jenna Fromer, Runzhong Wang, Mrunali Manjrekar, Austin Tripp, José Miguel Hernández-Lobato, Connor W. Coley.
JCIM.
[paper] [preprint]

ML4TSPBench: Streamlining the Design Space of ML4TSP Suggests Principles for Learning and Search.
Yang Li, Jiale Ma, Wenzheng Pan, Runzhong Wang, Haoyu Geng, Nianzu Yang, Junchi Yan.
ICLR 2025.
[paper] [code]

Unified and Generalizable Reinforcement Learning for Facility Location Problems on Graphs.
Wenxuan Guo, Runzhong Wang, Yanyan Xu, Yaohui Jin.
WWW 2025.
[paper] [code]

Schedule Optimization for Chemical Library Synthesis.
Qianxiang Ai, Fanwang Meng, Runzhong Wang, J. Cullen Klein, Alexander G. Godfrey, Connor W. Coley.
Digital Discovery 2024.
[paper]

Fast T2T: Optimization Consistency Speeds Up Diffusion-Based Training-to-Testing Solving for Combinatorial Optimization.
Yang Li, Jinpei Guo, Runzhong Wang, Hongyuan Zha, Junchi Yan.
NeurIPS 2024.
[paper]

Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search.
Kevin Yu, Jihye Roh, Ziang Li, Wenhao Gao, Runzhong Wang, Connor W. Coley.
NeurIPS 2024.
[paper] [code]

Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime.
Haoyu Geng, Hang Ruan, Runzhong Wang, Yang Li, Yang Wang, Lei Chen, Junchi Yan.
NeurIPS 2024 Datasets and Benchmarks Track.
[paper] [code]

LLMCO4MS: LLMs-aided Neural Combinatorial Solver for Ancient Manuscript Restoration from Fragments with Case Studies on Dunhuang.
Yuqing Zhang, Hangqi Li, Shengyu Zhang, Runzhong Wang, Baoyi He, Huaiyong Dou, Junchi Yan, Yongquan Zhang, Fei Wu.
ECCV 2024.
[paper]

正线性约束组合优化问题的非自回归学习求解 | Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networks.
汪润中, 郦洋, 严骏驰, 杨小康 | Runzhong Wang, Yang Li, Junchi Yan, Xiaokang Yang.
中国科学：信息科学 | SCIENTIA SINICA Informationis 2024.
[中文论文 | English version] [code]

Pygmtools: A Python Graph Matching Toolkit.
Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, Junchi Yan.
JMLR 2024 (vol. 25, no. 33, pp. 1-7).
[paper] [code] [documentation]

Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions.
Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, Junchi Yan.
WWW 2024.
[paper]

From Matching to Mixing: Learning Graph Interpolation for SAT Instance Generation.
Xinyan Chen, Yang Li, Runzhong Wang, Junchi Yan.
ICLR 2024.
[paper]

GMTR: Graph Matching Transformers.
Jinpei Guo, Shaofeng Zhang, Runzhong Wang, Chang Liu, Junchi Yan.
ICASSP 2024.
[paper]

T2T: 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]
Note: the Arxiv version fixed a minor issue in Eq (11) with the original ICML'23 publication

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]

Doctoral Dissertation

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)]

Work

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

Education

Sep 2019 to Sep 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)