World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8506
World Ranking
7940
National Ranking
3422

Overview

Chang-Tien Lu is affiliated with Virginia Tech in the United States and works primarily in the field of Computer Science, with a focus on Artificial Intelligence. Their research spans several subfields, including Computer Vision and Pattern Recognition, Building and Construction, Statistical and Nonlinear Physics, and Transportation.

The scientist's work covers a range of main topics, particularly in areas related to data analysis and machine learning methodologies. Key topics include:

  • Traffic Prediction and Management Techniques
  • Topic Modeling
  • Advanced Graph Neural Networks
  • Complex Network Analysis Techniques
  • Human Mobility and Location-Based Analysis
  • Anomaly Detection Techniques and Applications
  • Domain Adaptation and Few-Shot Learning

Chang-Tien Lu has contributed to numerous publications, including journal articles and conference papers. Notable recent papers include:

  • "TapNet: Multivariate Time Series Classification with Attentional Prototypical Network," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks," 2020, arXiv (Cornell University)
  • "Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks," 2023, ACM Computing Surveys
  • "Probabilistic Topic Modeling for Comparative Analysis of Document Collections," 2020, ACM Transactions on Knowledge Discovery from Data
  • "The Quality of AI-Generated Dental Caries Multiple Choice Questions: A Comparative Analysis of ChatGPT and Google Bard Language Models," 2024, Heliyon

The scientist frequently publishes in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2022 IEEE International Conference on Big Data (Big Data)
  • ACM Transactions on Knowledge Discovery from Data
  • Frontiers in Big Data

Chang-Tien Lu has collaborated extensively with a number of coauthors, including:

  • Fanglan Chen
  • Zhiqian Chen
  • Kaiqun Fu
  • Jianfeng He
  • Shuo Lei

Best Publications

  • Advances in Spatial and Temporal Databases

    Michael Gertz;Matthias Renz;Xiaofang Zhou;Erik Hoel

  • Survey of fraud detection techniques

    Yufeng Kou;Chang-Tien Lu;S. Sirwongwattana;Yo-Ping Huang

  • Spatial databases-accomplishments and research needs

    S. Shekhar;S. Chawla;S. Ravada;A. Fetterer

  • A Unified Approach to Detecting Spatial Outliers

    Shashi Shekhar;Chang-Tien Lu;Pusheng Zhang

  • 'Beating the news' with EMBERS: forecasting civil unrest using open source indicators

    Naren Ramakrishnan;Patrick Butler;Sathappan Muthiah;Nathan Self

  • TapNet: Multivariate Time Series Classification with Attentional Prototypical Network

    Xuchao Zhang;Yifeng Gao;Jessica Lin;Chang-Tien Lu

  • Detecting graph-based spatial outliers: algorithms and applications (a summary of results)

    Shashi Shekhar;Chang-Tien Lu;Pusheng Zhang

  • Algorithms for spatial outlier detection

    C.-T. Lu;D. Chen;Y. Kou

  • Misinformation Propagation in the Age of Twitter

    Fang Jin;Wei Wang;Liang Zhao;Edward Dougherty

  • Spatial Weighted Outlier Detection.

    Yufeng Kou;Chang-Tien Lu;Dechang Chen

  • Multi-Task Learning for Spatio-Temporal Event Forecasting

    Liang Zhao;Qian Sun;Jieping Ye;Feng Chen

  • Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media

    Rupinder Paul Khandpur;Taoran Ji;Steve Jan;Gang Wang

  • CROWDSAFE: crowd sourcing of crime incidents and safe routing on mobile devices

    Sumit Shah;Fenye Bao;Chang-Tien Lu;Ing-Ray Chen

  • Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data

    Sina Dabiri;Chang-Tien Lu;Kevin Heaslip;Chandan K. Reddy

  • On Detecting Spatial Outliers

    Dechang Chen;Chang-Tien Lu;Yufeng Kou;Feng Chen

  • Map cube: A visualization tool for spatial data warehouses

    S Shekhar;C T Lu;X Tan;S Chawla

  • Detecting spatial outliers with multiple attributes

    Chang-Tien Lu;Dechang Chen;Yufeng Kou

  • Activity analysis based on low sample rate smart meters

    Feng Chen;Jing Dai;Bingsheng Wang;Sambit Sahu

  • SOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection

    Jason Wang;Kaiqun Fu;Chang-Tien Lu

  • Detecting graph-based spatial outliers

    Shashi Shekhar;Chang-Tien Lu;Pusheng Zhang

  • Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

    Ouri Wolfson;Divyakant Agrawal;Chang-Tien Lu

  • Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling

    Liang Zhao;Feng Chen;Jing Dai;Ting Hua

  • SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning

    Liang Zhao;Jiangzhuo Chen;Feng Chen;Wei Wang

Frequent Co-Authors

Naren Ramakrishnan
Naren Ramakrishnan Virginia Tech
Shashi Shekhar
Shashi Shekhar University of Minnesota
Sanjay Chawla
Sanjay Chawla Qatar Computing Research Institute
Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Yanjie Fu
Yanjie Fu Arizona State University
Ing-Ray Chen
Ing-Ray Chen Virginia Tech
Aravind Srinivasan
Aravind Srinivasan University of Maryland, College Park
Chandan K. Reddy
Chandan K. Reddy Virginia Tech
Lise Getoor
Lise Getoor University of California, Santa Cruz
Yan Huang
Yan Huang University of North Texas

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