World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
12641
World Ranking
5270
National Ranking
2427

Overview

Ji Liu is affiliated with Facebook in the United States and has a research portfolio focused predominantly in computer science, with a strong emphasis on artificial intelligence and computer vision. Their scholarly activity spans over 130 publications, addressing various subfields and applied topics within these domains.

The main areas of study for Ji Liu include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Physiology
  • Endocrine and Autonomic Systems

Within these subfields, Ji Liu's specific research interests comprise:

  • Advanced Neural Network Applications
  • Stochastic Gradient Optimization Techniques
  • Privacy-Preserving Technologies in Data
  • Machine Learning and Data Classification
  • Generative Adversarial Networks and Image Synthesis
  • Adversarial Robustness in Machine Learning
  • Video Surveillance and Tracking Methods

Ji Liu has published frequently in several venues, reflecting the interdisciplinary nature of their research. The primary publication outlets include:

  • arXiv (Cornell University)
  • Proceedings of the VLDB Endowment
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • American Journal of Physiology-Endocrinology and Metabolism
  • IEEE Internet of Things Journal

Some notable recent papers authored by Ji Liu are:

  • "Data Poisoning Attacks on Federated Machine Learning" (2021), published in IEEE Internet of Things Journal
  • "Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification" (2022), featured at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Activation of arcuate nucleus glucagon-like peptide-1 receptor-expressing neurons suppresses food intake" (2022), published in Cell & Bioscience
  • "Glucose-sensing glucagon-like peptide-1 receptor neurons in the dorsomedial hypothalamus regulate glucose metabolism" (2022), published in Science Advances
  • "Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters" (2022), published in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Ji Liu frequently collaborates with a core group of coauthors, including:

  • Xiangru Lian
  • Binhang Yuan
  • Sen Yang
  • Shaoduo Gan
  • Zhaohuan Huang

Best Publications

  • Tensor completion for estimating missing values in visual data

    Ji Liu;Przemyslaw Musialski;Peter Wonka;Jieping Ye

  • Sparse reconstruction cost for abnormal event detection

    Yang Cong;Junsong Yuan;Ji Liu

  • Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent

    Xiangru Lian;Ce Zhang;Huan Zhang;Cho-Jui Hsieh

  • Asynchronous parallel stochastic gradient for nonconvex optimization

    Xiangru Lian;Yijun Huang;Yuncheng Li;Ji Liu

  • IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval

    Hui Chen;Guiguang Ding;Xudong Liu;Zijia Lin

  • Gradient Sparsification for Communication-Efficient Distributed Optimization

    Jianqiao Wangni;Jialei Wang;Ji Liu;Tong Zhang

  • Abnormal event detection in crowded scenes using sparse representation

    Yang Cong;Junsong Yuan;Ji Liu

  • An asynchronous parallel stochastic coordinate descent algorithm

    Ji Liu;Stephen J. Wright;Christopher Ré;Victor Bittorf

  • Asynchronous Decentralized Parallel Stochastic Gradient Descent

    Xiangru Lian;Wei Zhang;Ce Zhang;Ji Liu

  • Asynchronous Stochastic Coordinate Descent: Parallelism and Convergence Properties

    Ji Liu;Stephen J. Wright

  • $D^2$: Decentralized Training over Decentralized Data

    Hanlin Tang;Xiangru Lian;Ming Yan;Ce Zhang

  • Data Poisoning Attacks on Federated Machine Learning

    Gan Sun;Yang Cong;Jiahua Dong;Qiang Wang

  • Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks

    Jianhui Chen;Ji Liu;Jieping Ye

  • Staleness-aware async-SGD for distributed deep learning

    Wei Zhang;Suyog Gupta;Xiangru Lian;Ji Liu

  • Communication Compression for Decentralized Training

    Hanlin Tang;Shaoduo Gan;Ce Zhang;Tong Zhang

  • ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning

    Hantian Zhang;Jerry Li;Kaan Kara;Dan Alistarh

  • ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting

    Xiaohan Ding;Tianxiang Hao;Jianchao Tan;Ji Liu

  • An accelerated randomized Kaczmarz algorithm

    Ji Liu;Stephen J. Wright

  • Global Sparse Momentum SGD for Pruning Very Deep Neural Networks

    Xiaohan Ding;guiguang ding;Xiangxin Zhou;Yuchen Guo

  • DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression

    Hanlin Tang;Chen Yu;Xiangru Lian;Tong Zhang

  • DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression

    Hanlin Tang;Xiangru Lian;Chen Yu;Tong Zhang

  • LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning

    Yali Du;Lei Han;Meng Fang;Ji Liu

  • Data Poisoning Attacks on Federated Machine Learning

    Gan Sun;Yang Cong;Jiahua Dong;Qiang Wang

Frequent Co-Authors

Ce Zhang
Ce Zhang ETH Zurich
Zhangyang Wang
Zhangyang Wang The University of Texas at Austin
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Sridhar Mahadevan
Sridhar Mahadevan University of Massachusetts Amherst
Guiguang Ding
Guiguang Ding Tsinghua University
Jungong Han
Jungong Han Aberystwyth University
Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Dacheng Tao
Dacheng Tao Nanyang Technological University
Jiebo Luo
Jiebo Luo University of Rochester
Junsong Yuan
Junsong Yuan University at Buffalo, State University of New York

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