World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
15655
World Ranking
2612
National Ranking
1298

Electronics and Electrical Engineering

D-Index
59
Citations
13324
World Ranking
1750
National Ranking
700

Overview

Yanzhi Wang is a researcher affiliated with Northeastern University in the United States, with a substantial body of work primarily centered in the fields of Computer Science and Engineering. Their publications reflect a focus on multiple subfields, including Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Materials Chemistry, and Renewable Energy, Sustainability and the Environment.

Their research topics cover diverse areas such as Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning, Advanced Memory and Neural Computing, Advanced Image and Video Retrieval Techniques, CCD and CMOS Imaging Sensors, Adversarial Robustness in Machine Learning, and Electrocatalysts for Energy Conversion.

Among their recent publications are:

  • EfficientFormer: Vision Transformers at MobileNet Speed, 2022, published in arXiv (Cornell University)
  • AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • A Survey of Stochastic Computing Neural Networks for Machine Learning Applications, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Controlling Oxygen Reduction Selectivity through Steric Effects: Electrocatalytic Two-Electron and Four-Electron Oxygen Reduction with Cobalt Porphyrin Atropisomers, 2021, Angewandte Chemie International Edition

Yanzhi Wang collaborates frequently with a group of co-authors, including Wei Niu, Geng Yuan, Xue Lin, Yanyu Li, and Bin Ren. These collaborations contribute to a significant volume of work published in prominent venues.

Their frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • Angewandte Chemie
  • IEEE Transactions on Neural Networks and Learning Systems

Yanzhi Wang's body of work, spanning advanced neural network techniques, machine learning applications, and electrocatalysis, reflects an interdisciplinary approach bridging computer science and engineering disciplines. This multidisciplinary focus is evident across their publication record and their research topics addressing both theoretical and applied aspects of technology and materials science.

Best Publications

  • A Systematic DNN Weight Pruning Framework Using Alternating Direction Method of Multipliers

    Tianyun Zhang;Shaokai Ye;Kaiqi Zhang;Jian Tang

  • Experience-driven Networking: A Deep Reinforcement Learning based Approach

    Zhiyuan Xu;Jian Tang;Jingsong Meng;Weiyi Zhang

  • Deep Reinforcement Learning for Building HVAC Control

    Tianshu Wei;Yanzhi Wang;Qi Zhu

  • Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach

    Jing Wang;Jian Tang;Zhiyuan Xu;Yanzhi Wang

  • PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

    Wei Niu;Xiaolong Ma;Sheng Lin;Shihao Wang

  • Adversarial T-shirt! Evading Person Detectors in A Physical World

    Kaidi Xu;Gaoyuan Zhang;Sijia Liu;Quanfu Fan

  • A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

    Ning Liu;Zhe Li;Jielong Xu;Zhiyuan Xu

  • Task Scheduling with Dynamic Voltage and Frequency Scaling for Energy Minimization in the Mobile Cloud Computing Environment

    Xue Lin;Yanzhi Wang;Qing Xie;Massoud Pedram

  • A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs

    Zhiyuan Xu;Yanzhi Wang;Jian Tang;Jing Wang

  • Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

    Zihao Liu;Qi Liu;Tao Liu;Nuo Xu

  • CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices

    Caiwen Ding;Siyu Liao;Yanzhi Wang;Zhe Li

  • Multi-Channel Attention Selection GAN With Cascaded Semantic Guidance for Cross-View Image Translation

    Hao Tang;Dan Xu;Nicu Sebe;Yanzhi Wang

  • C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

    Shuo Wang;Zhe Li;Caiwen Ding;Bo Yuan

  • AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates.

    Ning Liu;Xiaolong Ma;Zhiyuan Xu;Yanzhi Wang

  • PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices.

    Xiaolong Ma;Fu-Ming Guo;Wei Niu;Xue Lin

  • CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

    Caiwen Ding;Siyu Liao;Yanzhi Wang;Zhe Li

  • A Survey of Stochastic Computing Neural Networks for Machine Learning Applications

    Yidong Liu;Siting Liu;Yanzhi Wang;Fabrizio Lombardi

  • SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning

    Unknown

  • Adaptive Control for Energy Storage Systems in Households With Photovoltaic Modules

    Yanzhi Wang;Xue Lin;Massoud Pedram

  • SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

    Ao Ren;Zhe Li;Caiwen Ding;Qinru Qiu

  • ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Methods of Multipliers

    Ao Ren;Tianyun Zhang;Shaokai Ye;Jiayu Li

  • Experience-Driven Congestion Control: When Multi-Path TCP Meets Deep Reinforcement Learning

    Zhiyuan Xu;Jian Tang;Chengxiang Yin;Yanzhi Wang

  • Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

    Zihao Liu;Qi Liu;Tao Liu;Yanzhi Wang

  • SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

    Ao Ren;Ji Li;Zhe Li;Caiwen Ding

Frequent Co-Authors

Massoud Pedram
Massoud Pedram University of Southern California
Naehyuck Chang
Naehyuck Chang Korea Advanced Institute of Science and Technology
Qinru Qiu
Qinru Qiu Syracuse University
Jian Tang
Jian Tang Syracuse University
Bo Yuan
Bo Yuan Rutgers, The State University of New Jersey
Sijia Liu
Sijia Liu Michigan State University
Bin Ren
Bin Ren Xiamen University
Bruce Allen
Bruce Allen Max Planck Society
N. A. Robertson
N. A. Robertson California Institute of Technology
Alessandra Buonanno
Alessandra Buonanno Max Planck Institute for Gravitational Physics

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