World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
96
Citations
45231
World Ranking
430
National Ranking
238

Research.com Recognitions

  • 2017 - ACM Fellow For contributions to information retrieval and text data mining
  • 2009 - ACM Distinguished Member
  • 2008 - Fellow of Alfred P. Sloan Foundation

Overview

ChengXiang Zhai is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research primarily spans the field of Computer Science, with a significant focus on Artificial Intelligence, supported by contributions in Information Systems, Molecular Biology, Computer Vision and Pattern Recognition, as well as Sociology and Political Science.

The scientist's work covers a range of topics, notably:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Biomedical Text Mining and Ontologies
  • Misinformation and Its Impacts
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications
  • Recommender Systems and Techniques

Zhai has published numerous papers in a variety of venues, reflecting interdisciplinary interests. Frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Knowledge and Data Engineering
  • 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
  • IET conference proceedings.

Recent notable papers authored or co-authored by them are:

  • "AutoML to Date and Beyond: Challenges and Opportunities" (2021), published in ACM Computing Surveys
  • "Biosystems Design by Machine Learning" (2020), published in ACS Synthetic Biology
  • "Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries" (2021), published in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "KEBLM: Knowledge-Enhanced Biomedical Language Models" (2023), published in Journal of Biomedical Informatics
  • "Transductive Ensemble Learning for Neural Machine Translation" (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence

They have collaborated frequently with several other researchers, including:

  • Heng Ji
  • Ismini Lourentzou
  • Alex Morales
  • Krisztian Balog
  • Daniel Campos

Among the awards received, ChengXiang Zhai was recognized as an ACM Fellow in 2017 for contributions to information retrieval and text data mining. Other distinctions include being named an ACM Distinguished Member in 2009 and a Fellow of the Alfred P. Sloan Foundation in 2008.

Best Publications

  • A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval

    Chengxiang Zhai;John Lafferty

  • Mining Text Data

    Charu C. Aggarwal;Cheng Xiang Zhai

  • Big data: Astronomical or genomical?

    Zachary D. Stephens;Skylar Y. Lee;Faraz Faghri;Roy H. Campbell

  • A study of smoothing methods for language models applied to information retrieval

    Chengxiang Zhai;John Lafferty

  • A study of smoothing methods for language models applied to Ad Hoc information retrieval

    Unknown

  • A survey of text classification algorithms

    Charu C. Aggarwal;Cheng Xiang Zhai

  • Document Language Models, Query Models, and Risk Minimization for Information Retrieval

    John Lafferty;Chengxiang Zhai

  • Topic sentiment mixture: modeling facets and opinions in weblogs

    Qiaozhu Mei;Xu Ling;Matthew Wondra;Hang Su

  • Instance Weighting for Domain Adaptation in NLP

    Jing Jiang;ChengXiang Zhai

  • Model-based feedback in the language modeling approach to information retrieval

    Chengxiang Zhai;John Lafferty

  • A Survey of Text Clustering Algorithms

    Charu C. Aggarwal;Cheng Xiang Zhai

  • Latent aspect rating analysis on review text data: a rating regression approach

    Hongning Wang;Yue Lu;Chengxiang Zhai

  • Discovering evolutionary theme patterns from text: an exploration of temporal text mining

    Qiaozhu Mei;ChengXiang Zhai

  • Context-sensitive information retrieval using implicit feedback

    Xuehua Shen;Bin Tan;ChengXiang Zhai

  • Implicit user modeling for personalized search

    Xuehua Shen;Bin Tan;ChengXiang Zhai

  • Statistical Language Models for Information Retrieval

    ChengXiang Zhai

  • Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions

    Kavita Ganesan;ChengXiang Zhai;Jiawei Han

  • Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval

    Cheng Xiang Zhai;William W. Cohen;John Lafferty

  • Automatic labeling of multinomial topic models

    Qiaozhu Mei;Xuehua Shen;ChengXiang Zhai

  • Topic modeling with network regularization

    Qiaozhu Mei;Deng Cai;Duo Zhang;ChengXiang Zhai

  • Rated aspect summarization of short comments

    Yue Lu;ChengXiang Zhai;Neel Sundaresan

  • Document language models, query models, and risk minimization for information retrieval

    Unknown

Frequent Co-Authors

Jiawei Han
Jiawei Han University of Illinois at Urbana-Champaign
Qiaozhu Mei
Qiaozhu Mei University of Michigan–Ann Arbor
Jing Jiang
Jing Jiang Singapore Management University
Xuanhui Wang
Xuanhui Wang Google (United States)
John Lafferty
John Lafferty Yale University
Tie-Yan Liu
Tie-Yan Liu Microsoft (United States)
Dan Roth
Dan Roth University of Pennsylvania
Yi Chang
Yi Chang Jilin University
James Allan
James Allan University of Massachusetts Amherst
Dolores Albarracín
Dolores Albarracín University of Pennsylvania

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