World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
75
Citations
20846
World Ranking
1423
National Ranking
740

Research.com Recognitions

  • 2016 - IEEE Fellow For contributions to the analysis and modeling of integrated circuits and systems

Overview

Peng Li is affiliated with the University of California, Santa Barbara in the United States, specializing primarily in the field of Medicine. Their research spans several subfields including Pulmonary and Respiratory Medicine, Surgery, Molecular Biology, Epidemiology, and Gastroenterology. The scientist's work addresses a range of topics related to cancer management, liver diseases, and medical imaging.

Key topics explored in their research include:

  • Gastric Cancer Management and Outcomes
  • Metastasis and carcinoma case studies
  • Gastrointestinal Tumor Research and Treatment
  • Liver Disease Diagnosis and Treatment
  • Esophageal and GI Pathology
  • Radiomics and Machine Learning in Medical Imaging
  • Liver Disease and Transplantation

Frequent co-authors with whom Peng Li has collaborated include:

  • Shutian Zhang
  • Xiujing Sun
  • Haiyun Shi
  • Ming Ji

Peng Li has published extensively, with recent notable papers including:

  • "Acute-on-chronic liver failure: far to go-a review," 2023, Critical Care
  • "Integrating SWATH-MS Proteomics and Transcriptome Analysis Identifies CHI3L1 as a Plasma Biomarker for Early Gastric Cancer," 2020, Molecular Therapy - Oncolytics
  • "Predicting the Onset of Hepatitis B Virus-Related Acute-on-Chronic Liver Failure," 2022, Clinical Gastroenterology and Hepatology
  • "Comparison of Over-the-Scope Clips to Standard Endoscopic Treatment as the Initial Treatment in Patients With Bleeding From a Nonvariceal Upper Gastrointestinal Cause," 2023, Annals of Internal Medicine
  • "Liquid-Liquid Phase Separation in Nucleation Process of Biomineralization," 2022, Frontiers in Chemistry

The scientist has published in several frequent venues, including:

  • SSRN Electronic Journal
  • Frontiers in Oncology
  • Journal of Digestive Diseases
  • Endoscopy
  • DOAJ (DOAJ: Directory of Open Access Journals)

Peng Li was recognized with the IEEE Fellow award in 2016 for contributions to the analysis and modeling of integrated circuits and systems, indicating involvement in interdisciplinary research activities beyond the primary medical focus.

Best Publications

  • A survey on deep learning for big data

    Qingchen Zhang;Qingchen Zhang;Laurence T. Yang;Laurence T. Yang;Zhikui Chen;Peng Li

  • Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View

    Deli Chen;Yankai Lin;Wei Li;Peng Li

  • A Survey on Deep Learning for Multimodal Data Fusion

    Jing Gao;Peng Li;Zhikui Chen;Jianing Zhang

  • DocRED: A Large-Scale Document-Level Relation Extraction Dataset.

    Yuan Yao;Deming Ye;Peng Li;Xu Han

  • Clustering to Find Exemplar Terms for Keyphrase Extraction

    Zhiyuan Liu;Peng Li;Yabin Zheng;Maosong Sun

  • Dynamical Properties and Design Analysis for Nonvolatile Memristor Memories

    Yenpo Ho;Garng M Huang;Peng Li

  • Nonvolatile memristor memory: device characteristics and design implications

    Yenpo Ho;Garng M. Huang;Peng Li

  • Rethinking the performance comparison between SNNS and ANNS.

    Lei Deng;Lei Deng;Yujie Wu;Xing Hu;Ling Liang

  • A deep neural network improves endoscopic detection of early gastric cancer without blind spots

    Lianlian Wu;Wei Zhou;Xinyue Wan;Jun Zhang

  • Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

    Jie Zhou;Ying Cao;Xuguang Wang;Peng Li

  • An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics

    Qingchen Zhang;Laurence T. Yang;Zheng Yan;Zhikui Chen

  • FewRel 2.0: Towards More Challenging Few-Shot Relation Classification

    Tianyu Gao;Xu Han;Hao Zhu;Zhiyuan Liu

  • A Linear-Centric Modeling Approach to Harmonic Balance Analysis

    Peng Li;L. Pileggi

  • Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery.

    Pengbin Zhang;Yinghai Ke;Zhenxin Zhang;Mingli Wang

  • Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things

    Peng Li;Zhikui Chen;Laurence Tianruo Yang;Qingchen Zhang

  • Learning from Context or Names? An Empirical Study on Neural Relation Extraction

    Hao Peng;Tianyu Gao;Xu Han;Yankai Lin

  • A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition

    Yong Zhang;Peng Li;Yingyezhe Jin;Yoonsuck Choe

  • Circuit design and exponential stabilization of memristive neural networks

    Shiping Wen;Tingwen Huang;Zhigang Zeng;Yiran Chen

  • Strain engineering in monolayer WS2, MoS2, and the WS2/MoS2 heterostructure

    Xin He;Hai Li;Hai Li;Zhiyong Zhu;Zhenyu Dai

  • An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things

    Qingchen Zhang;Chunsheng Zhu;Laurence T. Yang;Zhikui Chen

  • MAVEN: A Massive General Domain Event Detection Dataset

    Xiaozhi Wang;Ziqi Wang;Xu Han;Wangyi Jiang

  • ACM Transactions on Design Automation of Electronic Systems (TODAES) special section call for papers: Parallel CAD: Algorithm design and programming

    Kurt Keutzer;Peng Li;Li Shang;Hai Zhou

  • Parallel CAD: Algorithm Design and Programming Special Section Call for Papers TODAES: ACM Transactions on Design Automation of Electronic Systems

    Kurt Keutzer;Peng Li;Li Shang;Hai Zhou

Frequent Co-Authors

Xixiang Zhang
Xixiang Zhang King Abdullah University of Science and Technology
Larry Pileggi
Larry Pileggi Carnegie Mellon University
Jinhua Ye
Jinhua Ye National Institute for Materials Science
Qiang Zhang
Qiang Zhang Peking University
Gary H. Bernstein
Gary H. Bernstein University of Notre Dame
Husam N. Alshareef
Husam N. Alshareef King Abdullah University of Science and Technology
Wolfgang Porod
Wolfgang Porod University of Notre Dame
Jiang Hu
Jiang Hu Texas A&M University
Chuan Xia
Chuan Xia University of Electronic Science and Technology of China
Yuri Suzuki
Yuri Suzuki Stanford University

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