Jing Zhu

About Me

I am Jing Zhu. I joined Microsoft CoreAI as an applied scientist. I got my PhD at the University of Michigan, with Danai Koutra.

Before that, I obtained my B.S.E. from both the University of Michigan and Shanghai Jiao Tong University, and I worked with Andrew Owens.

News

[2025/08] DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data is accepted at EMNLP'25!
[2025/08] LinkGPT: Leveraging Large Language Models for Enhanced Link Prediction in Text-Attributed Graphs is accepted at CIKM'25!
[2025/02] Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning is accepted at CVPR'25!
[2024/09] On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks is accepted at NeurIPS'24!
[2024/09] Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation is accepted at EMNLP'24!
[2024/04] TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning is accepted at SIGIR'24!
[2023/10] Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices is accepted at WSDM'24!
[2022/09] Touch and Go: Learning from Human-Collected Vision and Touch is accepted at NeurIPS'22!
[2022/08] CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment is accepted at CIKM'22!
[2021/08] NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases is accepted at EMNLP'21!
[2020/12] Node Proximity Is All You Need: A Unified Framework for Proximity-Preserving and Structural Node and Graph Embedding is accepted at SDM'21!

Book Chapter

Fact Summarization for Personalized Knowledge Graphs

Danai Koutra, Davide Mottin, Jing Zhu

Personal Knowledge Graphs (PKGS): Methodology, Tools and Applications

Publications

[12] DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data. [link] [code]

Yuhang Zhou*, Jing Zhu*, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang

EMNLP 2025 Findings

[11] Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning. [link] [code]

Jing Zhu, Yuhang Zhou, Shengyi Qian, Zhongmou He, Tong Zhao, Neil Shah, Danai Koutra

CVPR 2025

[10] Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semantics. [link] [code]

Jing Zhu, Mingxuan Ju, Yozen Liu, Danai Koutra, Neil Shah, Tong Zhao.

Arxiv 2025

[9] LinkGPT: Teaching Large Language Models To Predict Missing Links. [link] [code]

Zhongmou He, Jing Zhu, Shengyi Qian, Joyce Y. Chai, Danai Koutra

CIKM 2025

[8] On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks. [link] [code]

Jiong Zhu*, Gaotang Li, Yao-An Yang, Jing Zhu, Xuehao Cui, Danai Koutra

NeurIPS 2024

[7] Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation. [link] [code]

Yuhang Zhou*, Jing Zhu*, Paiheng Xu, Xiaoyu Liu, Xiyao Wang, Danai Koutra, Wei Ai, Furong Huang

EMNLP 2024 Findings

[6] TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning. [link] [code]

Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos

SIGIR 2024

[5] Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices.[link] [code]

Jing Zhu*, Yuhang Zhou*, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra

WSDM 2024

Best paper award at MLoG [Slides]

[4] Touch and Go: Learning from Human-Collected Vision and Touch. [link] [code]

Fengyu Yang, Chenyang Ma, Jiacheng Zhang, Jing Zhu, Wenzhen Yuan, Andrew Owens

NeurIPS 22 Dataset and Benchmark

[3] CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. [link] [code]

Jing Zhu, Danai Koutra, Mark Heimann

CIKM 2022

[2] NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases. [link] [code]

Tara Safavi, Jing Zhu, Danai Koutra

EMNLP 2021

[1] Node Proximity Is All You Need: A Unified Framework for Proximity-Preserving and Structural Node and Graph Embedding. [link] [code]

Jing Zhu*, Xingyu Lu*, Mark Heimann, Danai Koutra

SDM 2021

Selected Awards

DEI Award, CVPR 2024
Best paper Award, MLoG 2024
Two Sigma Fellowship Finalist
CIKM Travel Award, SIGIR. Oct. 2022
KDD Travel Award, SIGKDD. Aug. 2022
CSE fellowship, University of Michigan. 2021-2022
Rackham Travel Grant, University of Michigan. Nov. 2021.
SDM Travel Award, SIAM. Apr. 2021.
James B. Angell Scholar, University of Michigan. Mar. 2021.
Jackson and Muriel Lum Scholarship, University of Michigan. 2019-2021
Excellent Undergraduate Scholarship, Shanghai Jiao Tong University. Nov. 2018, Nov. 2019.

Teaching

EECS476 Data Mining, University of Michigan. 2021 Winter.
EECS496 Major Design Experience-Professionalism, University of Michigan. 2020 Fall.
VV285 Honors Mathematics III, Shanghai Jiao Tong University. 2019 Summer.
VV186 Honors Mathematics II, Shanghai Jiao Tong University. 2018 Fall.

Academic Service

Review: NeurIPS, ICLR, CVPR, ICML, NLP series ARR, KDD, ECML-PKDD, LoG, AAAI, SDM