I am Jing Zhu, a second year PhD student at the University of Michigan. My advisor is Danai Koutra. I have also interned at LLNL with Mark Heimann in summer 2021 and AWS DGL Team with Xiang Song in summer 2022.

My past research includes unsupervised graph learning (SDM 2021, CIKM 2022), and multimodal learning (EMNLP 2021, NeurIPS 2022). Currently I work more on link prediction using a variety of methods (GNNs, KGEs, LMs).

• CV

News

• [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!
• [2021/11] I will join the DGL group as an applied scientist intern in the upcoming summer!
• [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!

Publications

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

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

Neural Information Processing Systems (Neurips 22 Dataset and Benchmark)

We propose Touch and Go: a multimodal visuo-tactile learning dataset which tries to associate sight with touch to understand material properties

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

Jing Zhu, Danai Koutra, Mark Heimann

International Conference on Information and Knowledge Management (CIKM 22)

We propose CAPER, a multilevel alignment framework that improves upon existing alignment algorithms by enforcing alignment consistency across multiple graph resolutions.

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

Tara Safavi, Jing Zhu, Danai Koutra

The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 21, acceptance rate 25.6%)

We propose NegatER, the first Unsupervised framework that ranks potential negatives in commonsense KBs using a contextual language model (LM).

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

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

SIAM International Conference on Data Mining (SDM 21, acceptance rate 21%)

We present the first unified framework of node embedding (PhUSION) that includes both proximity-preserving and structural embeddings.

Selected Awards

• 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.

Misc

• I would love to help minority students in CS research. Feel free to reach out if you need anything.