Howdy!

I'm an Applied Scientist at Amazon Search. I received my Ph.D. in Computer Science from Texas A&M University advised by Prof. Shuiwang Ji. Prior to that, I obtained my B.S. in Statistics from the School of the Gifted Young at University of Science and Technology of China. My research work aims to improve the applicability and reliability of machine learning and deep learning approaches to drive broader industrial applications and scientific discovery (AI4Science). I currently serve as an Area Chair at Language and Molecule @ ACL and in the program committees of NeurIPS, ICML, and ICLR.

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Research Interests

  • Large Language Models, Retrieval Augemented Generation, Multi-Modality
  • Self-Supervised Learning and Foundation Models
  • Model Explanability
  • Graph Neural Networks
  • AI4Science: Geometric Deep Learning, Bioinfomatics, Biomedical Imaging

    • Selected Publications

    * Equally contributed.

        SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations.

    Xuan Zhang, Jacob Helwig, Yuchao Lin, Yaochen Xie, Cong Fu, Stephan Wojtowytsch, Shuiwang Ji

    International Conference on Learning Representations (ICLR), 2024

      Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies.

    Yaochen Xie, Ziqian Xie, Sheikh Muhammad Saiful Islam, Degui Zhi, Shuiwang Ji

    Preprint, 2023

        Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

    Xuan Zhang*, Limei Wang*, Jacob Helwig*, Youzhi Luo*, Cong Fu*, Yaochen Xie*, ... (47 more), Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji

    Preprint, 2023

        Task-Agnostic Graph Explanations.

    Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji

    The 36th Annual Conference on Neural Information Processing Systems (NeurIPS), 2022

        Self-Supervised Representation Learning via Latent Graph Prediction.

    Yaochen Xie*, Zhao Xu*, and Shuiwang Ji

    International Conference on Machine Learning (ICML), 24460-24477, 2022.

      Group Contrastive Self-Supervised Learning on Graphs.

    Xinyi Xu, Cheng Deng, Yaochen Xie, Shuiwang Ji

    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

      Self-supervised Learning of Graph Neural Networks: A Unified Review.

    Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, and Shuiwang Ji

    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

        Augmented Equivariant Attention Networks for Microscopy Image Transformation.

    Yaochen Xie, Yu Ding, Shuiwang Ji

    IEEE Transactions on Medical Imaging (TMI), 2022.

        Advanced graph and sequence neural networks for molecular property prediction
    and drug discovery.

    Zhengyang Wang*, Meng Liu*, Youzhi Luo*, Zhao Xu*, Yaochen Xie*, Limei Wang*, Lei Cai*, Qi Qi, Zhuoning Yuan, Tianbao Yang, Shuiwang Ji.

    Bioinformatics, 38(9): 2579-2586, 2022.

        DIG: A Turnkey Library for Diving into Graph Deep Learning Research.

    Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui*, Haiyang Yu*, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji.

    Journal of Machine Learning Research (JMLR), 22 (240): 1-9, 2021.

          Global Voxel Transformer Networks for Augmented Microscopy.

    Zhengyang Wang*, Yaochen Xie*, and Shuiwang Ji.

    Nature Machine Intelligence, 3: 161-171, 2021.

        Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising.

    Yaochen Xie, Zhengyang Wang, and Shuiwang Ji,

    The 34th Neural Information Processing Systems (NeurIPS), 20320-20330, 2020.

      Finding the Stars in the Fireworks: Deep Understanding of Motion Sensor Fingerprint.

    Xiang-Yang Li, Huiqi Liu, Lan Zhang, Zhenan Wu, Yaochen Xie, Ge Chen, Chunxiao Wan, and Zhongwei Liang,

    IEEE/ACM Transactions on Networking, 5 (27): 1945-1958, 2019.

    Tutorial

        Frontiers of Graph Neural Networks with DIG.

    Shuiwang Ji*, Meng Liu*, Yi Liu, Youzhi Luo, Limei Wang*, Yaochen Xie*, Zhao Xu*, Haiyang Yu*

    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 4796–4797, 2022.

    Services

    Area Chair

    Language and Molecules @ Annual Meeting of the Association for Computational Linguistics (ACL) 2024

    Program Committee Member

    Annual Conference on Neural Information Processing System (NeurIPS) 2021-2023

    International Conference on Machine Learning (ICML)2022-2024

    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)2021-2022

    International Conference on Learning Representation (ICLR)2022-2023

    Journal Reviewer

    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

    Transactions on Machine Learning Research (TMLR)

    Nature Communications

    IEEE Transactions on Image Processing (TIP)

    IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

    Data Mining and Knowledge Discovery (DAMI)

    Miscellaneous

    + Meet our new family member Sesame, a bernedoodle born on 7/10/2023. Pup training overfits easily!

    + I love perfomance driving, and am interested in auto engineering&tuning. My favorite drive is the Local 2.0, WA.