World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
3993
World Ranking
11837
National Ranking
4829

Overview

Bo Yuan is affiliated with Rutgers, The State University of New Jersey in the United States. Their research spans multiple fields with a primary focus on computer science and engineering.

The main areas of study include:

  • Computer Science
  • Engineering

Within these broad fields, their subfields of expertise are:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Aerospace Engineering
  • Computer Networks and Communications

Bo Yuan's primary research topics cover advanced and specialized applications such as:

  • Advanced Neural Network Applications
  • Adversarial Robustness in Machine Learning
  • Anomaly Detection Techniques and Applications
  • Advanced Malware Detection Techniques
  • Advanced Wireless Communication Techniques
  • Advanced Image and Video Retrieval Techniques
  • Advanced Memory and Neural Computing

They have published recent papers in a variety of scientific venues, including:

  • "Defective UiO-66-NH2 monoliths for optimizing CO2 capture performance" (2023) in Chemical Engineering Journal
  • "Ultrafast Degradation and High Adsorption Capability of a Sulfur Mustard Simulant under Ambient Conditions Using Granular UiO-66-NH2 Metal-Organic Gels" (2022) in ACS Applied Materials & Interfaces
  • "Audio-domain position-independent backdoor attack via unnoticeable triggers" (2022) in Proceedings of the 28th Annual International Conference on Mobile Computing And Networking
  • "Metasurface on integrated photonic platform: from mode converters to machine learning" (2022) in Nanophotonics
  • "An efficient utility-list based high-utility itemset mining algorithm" (2022) in Applied Intelligence

The most frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • SSRN Electronic Journal
  • Chemical Engineering Journal
  • IEEE Transactions on Computers

Bo Yuan frequently collaborates with several researchers, notably:

  • Yingying Chen
  • Siyu Liao
  • Cong Shi
  • Huy Phan
  • Lingyi Huang

In addition to journal articles and conference papers, they have contributed to book publications, including:

  • Human Exposure to Chlorinated Paraffins in Scandinavia (2023) published by TemaNord

Best Publications

  • Detecting functional modules in the yeast protein--protein interaction network

    Jingchun Chen;Bo Yuan

  • NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization

    Naiyang Guan;Dacheng Tao;Zhigang Luo;Bo Yuan

  • Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent

    Naiyang Guan;Dacheng Tao;Zhigang Luo;Bo Yuan

  • Online Nonnegative Matrix Factorization With Robust Stochastic Approximation

    Naiyang Guan;Dacheng Tao;Zhigang Luo;Bo Yuan

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

    Caiwen Ding;Siyu Liao;Yanzhi Wang;Zhe Li

  • Early Stopping Criteria for Energy-Efficient Low-Latency Belief-Propagation Polar Code Decoders

    Bo Yuan;Keshab K. Parhi

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

    Shuo Wang;Zhe Li;Caiwen Ding;Bo Yuan

  • Non-Negative Patch Alignment Framework

    Naiyang Guan;Dacheng Tao;Zhigang Luo;Bo Yuan

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

    Caiwen Ding;Siyu Liao;Yanzhi Wang;Zhe Li

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

    Ao Ren;Zhe Li;Caiwen Ding;Qinru Qiu

  • Low-Latency Successive-Cancellation Polar Decoder Architectures Using 2-Bit Decoding

    Bo Yuan;Keshab K. Parhi

  • A draft annotation and overview of the human genome

    Fred A Wright;William J Lemon;Wei D Zhao;Russell Sears

  • Low-Latency Successive-Cancellation List Decoders for Polar Codes With Multibit Decision

    Bo Yuan;Keshab K. Parhi

  • Reduced-latency SC polar decoder architectures

    Chuan Zhang;Bo Yuan;Keshab K. Parhi

  • Architecture optimizations for BP polar decoders

    Bo Yuan;Keshab K. Parhi

  • Energy-efficient scheduling on heterogeneous multi-core architectures

    Jason Cong;Bo Yuan

  • AdvPulse: Universal, Synchronization-free, and Targeted Audio Adversarial Attacks via Subsecond Perturbations

    Zhuohang Li;Yi Wu;Jian Liu;Yingying Chen

  • Efficient hardware architecture of softmax layer in deep neural network

    Bo Yuan

  • Real-Time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems

    Yi Xie;Cong Shi;Zhuohang Li;Jian Liu

  • Towards acceleration of deep convolutional neural networks using stochastic computing

    Ji Li;Ao Ren;Zhe Li;Caiwen Ding

  • PermDNN: efficient compressed DNN architecture with permuted diagonal matrices

    Chunhua Deng;Siyu Liao;Yi Xie;Keshab K. Parhi

  • Assembly, Annotation, and Integration of UNIGENE Clusters into the Human Genome Draft

    Degen Zhuo;Wei D. Zhao;Fred A. Wright;Hee Yung Yang

  • Practical Adversarial Attacks Against Speaker Recognition Systems

    Zhuohang Li;Cong Shi;Yi Xie;Jian Liu

  • HEIF: Highly Efficient Stochastic Computing-Based Inference Framework for Deep Neural Networks

    Zhe Li;Ji Li;Ao Ren;Ruizhe Cai

  • Automated malware detection using artifacts in forensic memory images

    Unknown

  • GoSPA: an energy-efficient high-performance globally optimized sparse convolutional neural network accelerator

    Chunhua Deng;Yang Sui;Siyu Liao;Xuehai Qian

  • DSCNN: Hardware-oriented optimization for Stochastic Computing based Deep Convolutional Neural Networks

    Zhe Li;Ao Ren;Ji Li;Qinru Qiu

  • SC-DCNN

    Unknown

  • Enabling Fast and Universal Audio Adversarial Attack Using Generative Model.

    Yi Xie;Zhuohang Li;Cong Shi;Jian Liu

  • TIE: energy-efficient tensor train-based inference engine for deep neural network

    Chunhua Deng;Fangxuan Sun;Xuehai Qian;Jun Lin

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

    Ao Ren;Ji Li;Zhe Li;Caiwen Ding

Frequent Co-Authors

Yanzhi Wang
Yanzhi Wang Northeastern University
Keshab K. Parhi
Keshab K. Parhi University of Minnesota
Qinru Qiu
Qinru Qiu Syracuse University
Zhongfeng Wang
Zhongfeng Wang Nanjing University
Yingying Chen
Yingying Chen Rutgers, The State University of New Jersey
Jian Tang
Jian Tang Syracuse University
Fred A. Wright
Fred A. Wright North Carolina State University
Ralf Krahe
Ralf Krahe The University of Texas MD Anderson Cancer Center
David J. Lilja
David J. Lilja University of Minnesota
Albert de la Chapelle
Albert de la Chapelle The Ohio State University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

As you explore studying Computer Science in the USA, it's important to consider flexible learning options that suit your needs and future plans. Many students begin with affordable online courses to save on tuition while building foundational skills. These options provide flexibility without sacrificing educational quality.

If you are worried about your academic record, some will grad schools accept low gpa concerns can be addressed by finding the right universities that prioritize potential and motivation alongside grades. Several reputable online colleges offer pathways for students with varied academic backgrounds.

A computer science degree opens doors to a wide range of careers, but it can also enhance professions in other fields, such as those explored in jobs with elementary education and environmental science degree. The interdisciplinary nature of technology means your CS skills can apply across many industries.

Looking to fast-track your studies? Consider enrolling in one of the online computer science degree programs that offer accelerated paths. This allows you to gain qualifications quickly and start building your career sooner.

Best Scientists Citing Bo Yuan

Trending Scientists