About Me

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 , Vassilis N. Ioannidis and Christos Faloutsos in summer 2022. In 2023, I went back to LLNL to work on the exciting knowledge-infused gene prediction projects with Jay Thiagarajan , Mark Heimann and Christine Klymko. During my undergrad, I worked on multimodal learning with Andrew Owens, which inspired me a lot on my PhD research.

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). My goal is to build multimodal, structure-aware recommendation systems.


Book Chapter


Selected Awards


Academic Service