Aston Zhang is a scientist at Amazon Web Services AI Research. He studies language models and multimodality for AI. He received the ICLR Outstanding Paper Award (2021), the ACM Ubicomp Distinguished Paper Award (2018), and the ACM SenSys Best Paper Award Nomination (2017). His open-source textbook, Dive into Deep Learning, has been adopted worldwide. He obtained his Ph.D. in Computer Science from University of Illinois at Urbana-Champaign.

Hi! I’m happy to connect and discuss. Our office is at Santa Clara in the San Francisco Bay Area. Just email me.


  • A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola
    Dive into Deep Learning
    Cambridge University Press, 2023
    • Adopted at 400 universities from 60 countries
    • Featured in the AWS re:Invent keynote by Swami, Head of AWS AI, Database, and Analytics
  • A. Zhang, M. Li, Z. C. Lipton, and A. J. Smola
    人民邮电出版社, 2nd ed., 2023, 1st ed., 2019

Papers (All)


  • with A. J. Smola
    Attention in Deep Learning [Keynote] [PDF] [Video]
    In The 36th International Conference on Machine Learning (ICML), 2019

  • with H. Lin, X. Shi, L. Lausen, H. He, S. Zha, and A. J. Smola
    Dive into Deep Learning for Natural Language Processing
    In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019

  • with H. Lin, L. Lausen, S. Zha, A. J. Smola, C. Wang, and M. Li
    From Shallow to Deep Language Representations: Pre-training, Fine-tuning, and Beyond [Website]
    In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019

  • with H. Zhang, T. He, Z. Zhang, Z. Zhang, H. Lin, and M. Li
    Everything You Need to Know to Reproduce SOTA Deep Learning Models from Hands-on Tutorial
    In International Conference on Computer Vision (ICCV), 2019


  • Area Chair
    • Annual Meeting of the Association for Computational Linguistics (ACL)
    • Conference on Empirical Methods in Natural Language Processing (EMNLP)
  • Senior Program Committee
    • AAAI Conference on Artificial Intelligence (AAAI)
  • Journal Editorial Board
    • Frontiers in Big Data