World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
8204
World Ranking
10545
National Ranking
4420

Overview

Wei Ding is affiliated with the University of Massachusetts Boston in the United States. Their research primarily spans the fields of computer science and engineering, with a substantial focus on artificial intelligence, computer vision and pattern recognition, signal processing, control and systems engineering, and molecular biology.

The main research topics covered by Wei Ding include anomaly detection techniques and applications, time series analysis and forecasting, music and audio processing, air quality monitoring and forecasting, IoT and edge/fog computing, model reduction, neural networks, machine learning, and data classification.

Wei Ding has published extensively in various venues. The most frequent publication venues include:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • ACM Transactions on Knowledge Discovery from Data
  • IEEE Intelligent Systems
  • IEEE Access

Recent papers authored or coauthored by Wei Ding illustrate a diverse range of interests and venues, including:

  • "CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing," 2021, arXiv (Cornell University)
  • "Causal Feature Selection with Missing Data," 2022, ACM Transactions on Knowledge Discovery from Data
  • "Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics," 2023, Journal of Chemical Information and Modeling
  • "Edge-cloud Collaboration-driven Predictive Planning Based on LSTM-attention for Wastewater Treatment," 2024, Computers & Industrial Engineering
  • "Introducing Time Series Snippets: A New Primitive for Summarizing Long Time Series," 2020, Data Mining and Knowledge Discovery

Frequent coauthors collaborating with Wei Ding include:

  • Ping Chen
  • Scott E. Crouter
  • Matthew Almeida
  • Zihan Li
  • Tianyu Kang

Best Publications

  • Data mining with big data

    Xindong Wu;Xingquan Zhu;Gong-Qing Wu;Wei Ding

  • Online Feature Selection with Streaming Features

    Xindong Wu;Kui Yu;Wei Ding;Hao Wang

  • Crime Forecasting Using Data Mining Techniques

    Chung-Hsien Yu;Max W. Ward;Melissa Morabito;Wei Ding

  • Enhancement of Immunoassay’s Fluorescence and Detection Sensitivity Using Three-Dimensional Plasmonic Nano-Antenna-Dots Array

    Liangcheng Zhou;Fei Ding;Hao Chen;Wei Ding

  • Online Streaming Feature Selection

    Xindong Wu;Xindong Wu;Kui Yu;Hao Wang;Wei Ding

  • Scalable and Accurate Online Feature Selection for Big Data

    Kui Yu;Xindong Wu;Wei Ding;Jian Pei

  • Learning a Proposal Classifier for Multiple Object Tracking

    Peng Dai;Renliang Weng;Wongun Choi;Changshui Zhang

  • Towards Scalable and Accurate Online Feature Selection for Big Data

    Kui Yu;Xindong Wu;Wei Ding;Jian Pei

  • Crime hotspot mapping using the crime related factors--a spatial data mining approach

    Dawei Wang;Wei Ding;Henry Lo;Tomasz Stepinski

  • Multi-Source Causal Feature Selection

    Kui Yu;Lin Liu;Jiuyong Li;Wei Ding

  • Detection of Sub-Kilometer Craters in High Resolution Planetary Images Using Shape and Texture Features

    Louren » co Bandeira;Wei Ding;Tomasz F. Stepinski

  • Subkilometer crater discovery with boosting and transfer learning

    Wei Ding;Tomasz F. Stepinski;Yang Mu;Lourenco Bandeira

  • Finding regional co-location patterns for sets of continuous variables in spatial datasets

    Christoph F. Eick;Rachana Parmar;Wei Ding;Tomasz F. Stepinski

  • Authorship identification from unstructured texts

    Chunxia Zhang;Xindong Wu;Zhendong Niu;Wei Ding

  • Designing efficient accelerator of depthwise separable convolutional neural network on FPGA

    Unknown

  • Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns

    Dawei Wang;Wei Ding;Henry Z. Lo;Melissa Morabito

  • Mining sequential patterns with periodic wildcard gaps

    Youxi Wu;Lingling Wang;Jiadong Ren;Wei Ding

  • Using a model checker to test safety properties

    P. Ammann;Wei Ding;Daling Xu

  • Local discriminative distance metrics ensemble learning

    Yang Mu;Wei Ding;Dacheng Tao

  • A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge

    Ping Chen;Wei Ding;Chris Bowes;David Brown

  • Online Learning from Trapezoidal Data Streams

    Qin Zhang;Peng Zhang;Guodong Long;Wei Ding

  • Model Checkers in Software Testing

    Paul E Black;Paul Ammann;Wei Ding

  • Evaluation of three specification-based testing criteria

    A. Abdurazik;P. Ammann;Wei Ding;J. Offutt

  • Cancer subtype identification using somatic mutation data.

    Marieke Lydia Kuijjer;Joseph Nathaniel Paulson;Joseph Nathaniel Paulson;Peter Salzman;Wei Ding

Frequent Co-Authors

Stephen Y. Chou
Stephen Y. Chou Princeton University
Xindong Wu
Xindong Wu Hefei University of Technology
Ru Huang
Ru Huang Peking University
John Quackenbush
John Quackenbush Harvard University
Jian Pei
Jian Pei Duke University
Dacheng Tao
Dacheng Tao Nanyang Technological University
Shafiqul Islam
Shafiqul Islam Tufts University
Yangyuan Wang
Yangyuan Wang Peking University
Xingquan Zhu
Xingquan Zhu Florida Atlantic University
Eamonn Keogh
Eamonn Keogh University of California, Riverside

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

Exploring Computer Science doesn’t only mean attending classes on campus. Many students are opting for flexible learning options, including online programs that fit their schedules and budgets. Prospective learners can fast-track their education with a fastest online master's degree for quicker entry into the workforce. These accelerated pathways help working professionals advance their careers without a lengthy time commitment.

Choosing the right credential also matters. Pursuing one of the most useful masters degrees ensures that your qualification is relevant, valued, and in demand in today’s job market. If you’re just starting out or seeking a cost-effective way to enter the tech field, an associates degree online offers foundational knowledge and essential skills.

Balancing education costs is a key concern for many students. Those looking to minimize debt can consider the cheapest online degrees, which still deliver quality instruction but at a fraction of the expense. With these diverse options, learners can map their own journey toward a successful career in Computer Science and related fields.

Best Scientists Citing Wei Ding

Trending Scientists