Aston Zhang



Applied Scientist
Amazon Web Services Deep Learning

I am an Applied Scientist at AWS. My research interests are in deep learning. I earned my Ph.D. in Computer Science from University of Illinois at Urbana-Champaign under the supervision of Prof. Carl A. Gunter and Prof. Jiawei Han.

My past internships were with Google Research, Microsoft Research, Yahoo Labs, UBS, and proprietary trading. I also obtained a second M.S. in Statistics and a B.Eng in Computer Science with a Business Minor (top GPA for all the degrees).

[GitHub] [LinkedIn] [Twitter]

Books

  • A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola.
    Dive into Deep Learning.

  • A. Zhang, M. Li, Z. C. Lipton, and A. J. Smola.
    动手学深度学习 (in Chinese).
      • Lecture 1: Introduction, Data Manipulation, Regression, and Classification. [B] [Y]
      • Lecture 2: Using GPU, Overfitting, Multilayer Perceptron, and Convolutional Neural Networks. [B] [Y]
      • Lecture 3: AlexNet, VGG, Deep Learning Computing, and Choosing GPU. [B] [Y]
      • Lecture 4: Batch Normalization, NetInNet, GoogLeNet, ResNet, DenseNet, and Data Augmentation. [B] [Y]
      • Lecture 5: Computational Performance and Optimization (Gradient Descent and SGD). [B] [Y]
      • Lecture 6: Fine-tuning and Optimization (Momentum, Adagrad, RMSProp, Adadelta, and Adam). [B] [Y]
      • Lecture 7: Object Detection (R-CNN, Fast R-CNN, and Faster R-CNN). [B] [Y]
      • Lecture 8: Object Detection (SSD). [B] [Y]
      • Lecture 9: Object Detection (YOLO and Mask R-CNN). [B] [Y]
      • Lecture 10: Semantic Segmentation. [B] [Y]
      • Lecture 11: Neural Style Transfer. [B] [Y]
      • Lecture 12: Recurrent Neural Networks. [B] [Y]
      • Lecture 13: Forward Passing, Backpropagation, and Backpropagation Through Time. [B] [Y]
      • Lecture 14: Language Model with Recurrent Neural Networks. [B] [Y]
      • Lecture 15: GRU, LSTM, and Multi-layer Recurrent Neural Networks. [B] [Y]
      • Lecture 16: Word Embedding (word2vec). [B] [Y]
      • Lecture 17: Word Embedding (GloVe and fastText) and Applications. [B] [Y]
      • Lecture 18: Sequence-to-Sequence (Decoder-Encoder) and Attention. [B] [Y]
      • Lecture 19: Neural Machine Translation. [B] [Y]

Papers

  • S. Yao, Y. Zhao, A. Zhang, S. Hu, H. Shao, C. Zhang, L. Su, and T. Abdelzaher.
    Deep Learning for the Internet of Things.
    In Computer, 2018. (selected as May 2018 cover feature of the flagship magazine of the IEEE Computer Society)

  • A. Zhang, X. Lu, C. A. Gunter, S. Yao, F. Tao, R. Zhu, H. Gui, D. Fabbri, D. Liebovitz, and B. Malin.
    De Facto Diagnosis Specialties: Recognition and Discovery.
    In Learning Health Systems (LHS), 2018.

  • A. El-Kishky, F. Xu, A. Zhang, S. Macke, and J. Han.
    Entropy-Based Subword with an Application to Word Embeddings.
    In Proceedings of the 2nd Workshop on Subword and Character Level Models in NLP at the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL SCLeM), 2018.

  • S. Yao, Y. Zhao, H. Shao, C. Zhang, A. Zhang, D. Liu, S. Liu, L. Su, and T. Abdelzaher.
    ApDeepSense: Deep Learning Uncertainty Estimation Without the Pain for IoT.
    In Proceedings of the 38th IEEE International Conference on Distributed Computing Systems (ICDCS), 2018.

  • A. Zhang, L. Garcia-Pueyo, J. B. Wendt, M. Najork, and A. Broder.
    Email Category Prediction.
    In Proceedings of the 26th International World Wide Web Conference (WWW), 2017.

  • S. Yao, S. Hu, Y. Zhao, A. Zhang, and T. Abdelzaher.
    DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing.
    In Proceedings of the 26th International World Wide Web Conference (WWW), 2017.

  • S. Yao, Y. Zhao, A. Zhang, S. Lu, and T. Abdelzaher.
    DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems without Loss of Performance.
    In Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems (ACM Sensys, Best Paper Award Nomination), 2017.

  • S. Yao, Y. Zhao, H. Shao, A. Zhang, C. Zhang, S. Li, and T. Abdelzaher.
    RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations.
    In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (ACM Ubicomp), 2017.

  • A. Zhang, A. Goyal, R. Baeza-Yates, Y. Chang, J. Han, C. A. Gunter, and H. Deng.
    Towards Mobile Query Auto-Completion: An Efficient Mobile Application-Aware Approach.
    In Proceedings of the 25th International World Wide Web Conference (WWW), 2016.

  • A. Zhang and Q. Gu.
    Accelerated Stochastic Block Coordinate Descent with Optimal Sampling. [Proof of Lemmas]
    In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016.

  • S. Demetriou, W. Merrill, W. Yang, A. Zhang, and C. A. Gunter.
    Free for All! Assessing User Data Exposure to Advertising Libraries on Android.
    In Proceedings of the Network and Distributed System Security Symposium (NDSS), 2016.

  • R. Zhu, A. Zhang, J. Peng, and C. Zhai.
    Exploiting Temporal Divergence of Topic Distributions for Event Detection.
    In Proceedings of the IEEE International Conference on Big Data (IEEE BigData), 2016.

  • A. Zhang, A. Goyal, W. Kong, H. Deng, A. Dong, Y. Chang, C. A. Gunter, and J. Han.
    adaQAC: Adaptive Query Auto-Completion via Implicit Negative Feedback.
    In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2015.

  • W. Kong, R. Li, J. Luo, A. Zhang, Y. Chang, and J. Allan.
    Predicting Search Intent Based on Pre-Search Context.
    In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2015.

  • H. Fu, A. Zhang, and X. Xie.
    Effective Social Graph De-anonymization based on Graph Structure and Descriptive Information.
    In ACM Transactions on Intelligent Systems and Technology (ACM TIST), Vol. 6, No. 4, 2015.

  • A. Zhang*, X. Lu*, C. A. Gunter, D. Fabbri, D. Liebovitz, and B. Malin (*equal contribution).
    Discovering De Facto Diagnosis Specialties.
    In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), 2015. (selected to receive the NSF travel fellowship)

  • A. Zhang, X. Xie, K. C.-C. Chang, C. A. Gunter, J. Han, and X. Wang.
    Privacy Risk in Anonymized Heterogeneous Information Networks.
    In Proceedings of the 17th International Conference on Extending Database Technology (EDBT), 2014.

  • H. Fu, A. Zhang, and X. Xie.
    De-anonymizing Social Graphs via Node Similarity.
    In Proceedings of the 23rd International World Wide Web Conference (WWW), 2014.

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Services

  • Journal Editorial Board Member
    • Frontiers in Big Data

  • Program Committee Member/Reviewer
    • AAAI Conference on Artificial Intelligence (AAAI), 2019
    • ACM International Conference on Information and Knowledge Management (CIKM), 2016
    • ACM International Conference on Web Search and Data Mining (WSDM), 2018, 2019 (Outstanding PC Award)
    • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018, 2019
    • Conference on Neural Information Processing Systems (NeurIPS), 2019
    • International Conference on Machine Learning (ICML), 2019
    • IEEE Global Communications Conference (GLOBECOM), 2019
    • The International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2018, 2019
    • The Web Conference (former International World Wide Web Conference, WWW), 2018

  • Open Source Committer