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I am currently a Ph.D. candidate at University of California, Los Angeles (UCLA), where I am fortunate to be advised by Prof.Yizhou Sun and Prof.Wei Wang. My research interest lies in graph neural networks and machine learning in general, with a special focus on reasoning over dynamical systems (graphs) and knowledge graph modeling. My research is generously supported by Amazon Ph.D. Fellowship. Before UCLA, I received my bachelor's degree of Information Engineering from Shanghai Jiao Tong University (SJTU) in 2019. I love piano, badminton and traveling. I'm on the job market for year 2023-2024 ! Email / linkedin / Github / Google Scholar |
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Zijie Huang*, Wanjia Zhao*, Jingdong Gao, Ziniu Hu, Xiao Luo, Yadi Cao, Yuanzhou Chen, Yizhou Sun, Wei Wang Preprint, 2023. Best Paper Award at DLDE workshop at Neurips 2023. [Paper] [Code] |
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Zijie Huang*, Jeehyun Hwang*, Junkai Zhang*, Jinwoo Baik, Weitong Zhang, Quanquan Gu, Dominik Wodarz, Yizhou Sun, Wei Wang The Web Conference (WWW), 2024 | The Symbiosis of Deep Learning and Differential Equations (DLDE) workshop at Neurips, 2023. |
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Zijie Huang, Baolin Li, Hafez Asgharzadeh, Anne Cocos, Lingyi Liu, Evan Cox, Colby Wise, Sudarshan Lamkhede Preprint, 2023. |
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Xiao Luo, Haixin Wang, Zijie Huang, Huiyu Jiang, Abhijeet Sadashiv Gangan, Song Jiang, Yizhou Sun The Conference on Neural Information Processing Systems (NeurIPS), 2023 |
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Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan, Zijie Huang, Ziniu Hu, Yewen Wang, Yingyan (Celine) Lin, Jason Cong, Yizhou Sun preprint, 2023 |
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Han Liu, Zijie Huang, Samuel S. Schoenholz, Ekin D. Cubuk, Morten M. Smedskjaer, Yizhou Sun, Wei Wang and Mathieu Bauchy Materials Horizons, 2023 A mahchine learning framework for fast glass dynamics simulation. |
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Zijie Huang, Yizhou Sun and Wei Wang The Conference on Knowledge Discovery and Data Mining (KDD), 2023 We proposed a generalized graphODE framework for predicting multi-agent system dynamics across different envrionments. |
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Song Jiang,Zijie Huang, Xiao Luo and Yizhou Sun The Conference on Knowledge Discovery and Data Mining (KDD), 2023 We proposed a novel graphODE framework for making counterfactual predictions of multi-agent dynamical systems. |
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Zijie Huang, Daheng Wang, Binxuan Huang, Chenwei Zhang, Jingbo Shang, Yan Liang, Zhengyang Wang, Xian Li, Christos Faloutsos, Yizhou Sun and Wei Wang Annual Meeting of the Association for Computational Linguistics (ACL-Findings), 2023 We proposed a novel geometric representation learning framework for learning two-view knowledge graphs and constructed a recipe-related two-view KG dataset. |
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Xiao Luo, Jingyang Yuan, Zijie Huang, Huiyu Jiang, Yifang Qin, Wei Ju, Ming Zhang, Yizhou Sun International Conference on Machine Learning (ICML), 2023 We proposed a higher-order graphODE framework to improve model effectiveness on various dynamical system downstream tasks. |
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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang Annual Meeting of the Association for Computational Linguistics (ACL), 2022 We proposed a multilingual knowledge graph completion model and constructed an e-commercial multilingual KG dataset.
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Zijie Huang, Yizhou Sun, Wei Wang The Conference on Knowledge Discovery and Data Mining (KDD), 2021 We proposed coupled graph ODE (CG-ODE) for modeling the co-evolution of graph nodes and edges in a continuous manner.
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Ruijie Wang, Zijie Huang, Shengzhong Liu, Huajie Shao, Dongxin Liu, Jinyang Li, Tianshi Wang, Dachun Sun,Shuochao Yao, Tarek Abdelzaher The International ACM SIGIR Conference on Research and Developmentin Information Retrieval (SIGIR), 2021 We proposed a generative model that considers user dynamic interest and text contents to predict cascade diffusioin on social network.
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Zijie Huang, Yizhou Sun, Wei Wang The Conference on Neural Information Processing Systems (NeurIPS), 2020 We proposed latent graph ODE (LG-ODE) to learn system dynamics from irregularly-sampled and partial observations
with underlying graph structure.
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Amin Javari, Zhankui He, Zijie Huang, Raj Jeetu, Kevin Chen-Chuan Chang The Web Conference (WWW) , 2020 We built a novel weak supervision model that jointly considers user interactions and hashtag text contents for hashtag recommendation.
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