XIE, Han

Department of Computer Science, Emory University.

prof_pic.jpg

N410, 400 Dowman Drive

Atlanta, Georgia 30322

Hi there! This is Han Xie. I am currently a final-year Ph.D. student in Computer Science at Emory University, where I am fortunate to be advised by Prof. Carl Yang and Prof. Li Xiong. I have also been working closely with Prof. Salman Avestimehr’s group on federated learning with graph data. My research interests include trustworthy machine learning, graph machine learning, large language models, and AI for healthcare.

Prior to joining Emory, I obtained my master’s and bachelor’s degrees at Carnegie Mellon University and Huazhong University of Science and Technology, respectively. I also spent a great semester in my senior year at University of California, Berkeley.

News

Jul 16, 2024 Our work entitled “Federated Node Classification over Distributed Ego-Networks with Secure Contrastive Embedding Sharing” was accepted by CIKM 2024!
Apr 20, 2024 Honored to receive The 2024 SDM Best Doctoral Forum Poster Runner-Up Award (first place)!
Apr 17, 2024 Defended my dissertation! Now Dr. Xie!
Mar 24, 2024 Honored to receive the SIAM International Conference on Data Mining (SDM) 2024 Doctoral Forum Travel Award (Funded by NSF)!
Oct 30, 2023 Our collaborative work FedBrain was accepted to SPIE Medical Imaging 2024 for oral presentation!

Selected publications

2024

  1. CIKM
    Federated Node Classification over Distributed Ego-Networks with Secure Contrastive Embedding Sharing
    Han Xie, Li Xiong, and Carl Yang
    In Proceedings of the ACM International Conference on Information and Knowledge Management, 2024
  2. SPIE
    Federated learning for cross-institution brain network analysis (Oral, *Co-first Authors)
    Han Xie*, Yi Yang*, Hejie Cui, and Carl Yang
    In Medical Imaging 2024: Computer-Aided Diagnosis. SPIE, 2024
    PDF

2023

  1. ICDM-FedGraph
    Personalized Federated Learning for Graph Classification via Client Graph Learning
    Jiachen Zhou, Han Xie, and Carl Yang
    Workshop on Federated Learning with Graph Data: The IEEE International Conference on Data Mining, 2023
  2. KDD
    Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications (Oral)
    Han Xie, Da Zheng, Jun Ma, Houyu Zhang, Vassilis N. Ioannidis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, Belinda Zeng, and Trishul Chilimbi
    In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
    PDF
  3. WWW
    Federated Node Classification over Graphs with Latent Link-type Heterogeneity (Oral)
    Han Xie, Li Xiong, and Carl Yang
    In Proceedings of the ACM Web Conference, 2023
    PDF

2022

  1. CIKM-FedGraph
    Subgraph Federated Learning over Heterogeneous Graphs
    Ke Zhang, Han Xie, Zishan Gu, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu, Yuan Yao, and Carl Yang
    Workshop on Federated Learning with Graph Data: The ACM International Conference on Information and Knowledge Management, 2022

2021

  1. NeurIPS
    Federated Graph Classification over Non-iid Graphs
    Han Xie, Jing Ma, Li Xiong, and Carl Yang
    In Proceedings of the Conference on Neural Information Processing Systems, 2021
    PDF
  2. ICLRW/MLSysW
    Fedgraphnn: A Federated Learning System and Benchmark for Graph Neural Networks
    Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S Yu, Yu Rong, and  others
    Workshop on Distributed and Private Machine Learning: The International Conference on Learning Representations (ICLR-DPML). Workshop on Graph Neural Networks and Systems: The Conference on Machine Learning and Systems (MLSys-GNNSys), 2021
    arXiv