World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
112
Citations
57100
World Ranking
205
National Ranking
116

Research.com Recognitions

  • 1997 - IEEE Fellow For contributions to the theory and practice of neural networks.

Overview

C. Lee Giles is affiliated with Pennsylvania State University in the United States. Their research primarily lies within the field of Computer Science, with a particular focus on Artificial Intelligence. The subfields of study they have been involved in include Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Statistics, Probability and Uncertainty, and Computational Theory and Mathematics.

The scientist's work spans several main topics, notably:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Text Analysis Techniques
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Algorithms
  • Ferroelectric and Negative Capacitance Devices
  • Neural Networks and Applications

Some of the recent papers authored or co-authored by Giles include:

  • "Physics-informed deep learning for prediction of CO2 storage site response" (2021), published in Journal of Contaminant Hydrology
  • "Understanding the onset of hot streaks across artistic, cultural, and scientific careers" (2021), published in arXiv (Cornell University)
  • "Table Header Detection and Classification" (2021), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Deep Learning Can Predict Laboratory Quakes From Active Source Seismic Data" (2021), published in Geophysical Research Letters
  • "Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching" (2021), published in Proceedings of the AAAI Conference on Artificial Intelligence

Giles has frequently published in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Value in Health
  • Journal of Contaminant Hydrology
  • Geophysical Research Letters

The scientist has collaborated extensively with several co-authors, including:

  • Ankur Mali
  • Daniel Kifer
  • Jian Wu
  • Alexander G. Ororbia
  • Sarah Rajtmajer

In recognition of their contributions to the field, C. Lee Giles was named an IEEE Fellow in 1997, specifically noted for contributions to the theory and practice of neural networks.

Best Publications

  • Face recognition: a convolutional neural-network approach

    S. Lawrence;C.L. Giles;Ah Chung Tsoi;A.D. Back

  • Accessibility of information on the Web

    Steve Lawrence;C. Lee Giles

  • Searching the World Wide Web

    Steve Lawrence;C. Lee Giles

  • Neural Information Processing Systems 7

    Kam-Chuen Jim;Bill Horne;C. Lee Giles

  • Self-organization and identification of Web communities

    G.W. Flake;S. Lawrence;C.L. Giles;F.M. Coetzee

  • CiteSeer: an automatic citation indexing system

    C. Lee Giles;Kurt D. Bollacker;Steve Lawrence

  • Efficient identification of Web communities

    Gary William Flake;Steve Lawrence;C. Lee Giles

  • Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping

    Rich Caruana;Steve Lawrence;C. Lee Giles

  • Digital libraries and autonomous citation indexing

    S. Lawrence;C. Lee Giles;K. Bollacker

  • Focused Crawling Using Context Graphs

    Michelangelo Diligenti;Frans Coetzee;Steve Lawrence;C. Lee Giles

  • Learning, invariance, and generalization in high-order neural networks

    C. Lee Giles;Tom Maxwell

  • Learning long-term dependencies in NARX recurrent neural networks

    Tsungnan Lin;B.G. Horne;P. Tino;C.L. Giles

  • Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach

    David M. Pennock;Eric Horvitz;Steve Lawrence;C. Lee Giles

  • Winners don't take all: Characterizing the competition for links on the web

    David M. Pennock;Gary William Flake;Steve Lawrence;Eric J. Glover

  • Computational capabilities of recurrent NARX neural networks

    H.T. Siegelmann;B.G. Horne;C.L. Giles

  • Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference

    C. Lee Giles;Steve Lawrence;Ah Chung Tsoi

  • Two supervised learning approaches for name disambiguation in author citations

    Hui Han;Lee Giles;Hongyuan Zha;Cheng Li

  • Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks

    Unknown

  • Learning on the border: active learning in imbalanced data classification

    Seyda Ertekin;Jian Huang;Leon Bottou;Lee Giles

  • CiteSeer: an autonomous Web agent for automatic retrieval and identification of interesting publications

    Kurt D. Bollacker;Steve Lawrence;C. Lee Giles

  • Know your enemy: learning from in-game opponents

    David Weintrop;Uri Wilensky

Frequent Co-Authors

Prasenjit Mitra
Prasenjit Mitra Pennsylvania State University
Steve Lawrence
Steve Lawrence Google (United States)
Daniel Kifer
Daniel Kifer Pennsylvania State University
Cornelia Caragea
Cornelia Caragea University of Illinois at Chicago
David M. Pennock
David M. Pennock Rutgers, The State University of New Jersey
Xue Liu
Xue Liu McGill University
John Yen
John Yen Pennsylvania State University
Hongyuan Zha
Hongyuan Zha Chinese University of Hong Kong, Shenzhen
Wang-Chien Lee
Wang-Chien Lee Pennsylvania State University
James Z. Wang
James Z. Wang Pennsylvania State University

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