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Jingrui He

Jingrui He

· Professor, School of Information Sciences

University of Illinois Urbana-Champaign · Computer Science

Active 2004–2026

h-index31
Citations3.8k
Papers267136 last 5y
Funding$1.7M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Jingrui He is a professor at the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. His research areas include Data and Information Systems. He has taught courses such as Introduction to Data Science and Data Mining. Jingrui He has been recognized for his significant contributions to the field, including being named a 2023 ACM Distinguished Member. His work and leadership contribute to advancing knowledge in data science and information systems, and he is actively involved in the academic community through teaching, research, and service.

Research topics

  • Computer Science
  • Artificial Intelligence
  • Data Mining
  • Machine Learning
  • Theoretical computer science
  • World Wide Web
  • Data science
  • Economics
  • Finance

Selected publications

  • InFoRM: Individual Fairness on Graph Mining

    2020 · 99 citations

    Algorithmic bias and fairness in the context of graph mining have largely remained nascent. The sparse literature on fair graph mining has almost exclusively focused on group-based fairness notation. However, the notion of individual fairness, which promises the fairness notion at a much finer granularity, has not been well studied. This paper presents the first principled study of Individual Fairness on gRaph Mining (InFoRM). First, we present a generic definition of individual fairness for gra…

    DOI

  • A Data-Driven Graph Generative Model for Temporal Interaction Networks

    2020 · 97 citations

    Senior authorCorresponding

    Deep graph generative models have recently received a surge of attention due to its superiority of modeling realistic graphs in a variety of domains, including biology, chemistry, and social science. Despite the initial success, most, if not all, of the existing works are designed for static networks. Nonetheless, many realistic networks are intrinsically dynamic and presented as a collection of system logs (i.e., timestamped interactions/edges between entities), which pose a new research direct…

    DOI

  • Local Motif Clustering on Time-Evolving Graphs

    2020 · 40 citations

    Senior authorCorresponding

    Graph motifs are subgraph patterns that occur in complex networks, which are of key importance for gaining deep insights into the structure and functionality of the graph. Motif clustering aims at finding clusters consisting of dense motif patterns. It is commonly used in various application domains, ranging from social networks to collaboration networks, from market-basket analysis to neuroscience applications. More recently, local clustering techniques have been proposed for motif-aware cluste…

    DOI

  • Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting

    2020 · 40 citations

    Senior authorCorresponding

    Financial time series analysis plays a central role in optimizing investment decision and hedging market risks. This is a challenging task as the problems are always accompanied by dual-level (i.e, data-level and task-level) heterogeneity. For instance, in stock price forecasting, a successful portfolio with bounded risks usually consists of a large number of stocks from diverse domains (e.g, utility, information technology, healthcare, etc.), and forecasting stocks in each domain can be treated…

    DOI

  • Deep Co-Attention Network for Multi-View Subspace Learning

    2021 · 22 citations

    Senior authorCorresponding

    Many real-world applications involve data from multiple modalities and thus exhibit the view heterogeneity. For example, user modeling on social media might leverage both the topology of the underlying social network and the content of the users’ posts; in the medical domain, multiple views could be X-ray images taken at different poses. To date, various techniques have been proposed to achieve promising results, such as canonical correlation analysis based methods, etc. In the meanwhile, it is…

    DOI

Recent grants

Frequent coauthors

Labs

  • Jingrui HePI

Education

  • PhD, School of Computer Science

    Carnegie Mellon University

    2010

Awards & honors

  • 2023 ACM Distinguished Member

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