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Computer Science

D-Index
33
Citations
7696
World Ranking
12423
National Ranking
114

Overview

Tijl De Bie is a researcher affiliated with Ghent University in Belgium. Their work primarily focuses on computer science, with extensive contributions to subfields such as artificial intelligence, statistical and nonlinear physics, computer vision and pattern recognition, information systems, and management science and operations research. Their research topics include advanced graph neural networks, complex network analysis techniques, ethics and social impacts of AI, data mining algorithms and applications, natural language processing techniques, recommender systems and techniques, and advanced bandit algorithms research.

De Bie has published multiple papers in various scientific venues. Notable recent papers include:

  • Automating data science, 2022, Communications of the ACM
  • Conditional t-SNE: more informative t-SNE embeddings, 2020, Machine Learning
  • Inherent Limitations of AI Fairness, 2024, Communications of the ACM
  • A Challenge-based Survey of E-recruitment Recommendation Systems, 2024, ACM Computing Surveys
  • DeBayes: a Bayesian Method for Debiasing Network Embeddings, 2020, arXiv (Cornell University)

The researcher has collaborated frequently with others in their field. Some of their frequent coauthors are:

  • Jefrey Lijffijt
  • Bo Kang
  • Maarten Buyl
  • Yvan Saeys
  • Edith Heiter

Tijl De Bie's publications appear in various venues, with multiple papers featured across these outlets:

  • arXiv (Cornell University)
  • Ghent University Academic Bibliography (Ghent University)
  • Data Mining and Knowledge Discovery
  • Applied Sciences
  • IEEE Access

Their contributions span 85 publications within computer science, demonstrating a comprehensive engagement with different areas and subfields. The researcher's work touches on technical topics such as network analysis, AI fairness, and recommender systems, reflecting a broad spectrum of expertise. This profile integrates topics, publications, collaborations, and academic affiliations to outline their scholarly activities.

Best Publications

  • CAFE: a computational tool for the study of gene family evolution

    Tijl De Bie;Nello Cristianini;Jeffery P. Demuth;Matthew W. Hahn

  • A statistical framework for genomic data fusion

    Gert R. G. Lanckriet;Tijl De Bie;Nello Cristianini;Michael I. Jordan

  • FACE: Feasible and Actionable Counterfactual Explanations

    Rafael Poyiadzi;Kacper Sokol;Raul Santos-Rodriguez;Tijl De Bie

  • Flu detector: tracking epidemics on twitter

    Vasileios Lampos;Tijl De Bie;Nello Cristianini

  • Kernel-based data fusion for gene prioritization

    Tijl De Bie;Léon-Charles Tranchevent;Liesbeth M. M. van Oeffelen;Yves Moreau

  • Maximum entropy models and subjective interestingness: an application to tiles in binary databases

    Tijl De Bie

  • Convex Methods for Transduction

    Tijl D. Bie;Nello Cristianini

  • Eigenproblems in Pattern Recognition

    Tijl De Bie;Nello Cristianini;Roman Rosipal

  • Inferring transcriptional modules from ChIP-chip, motif and microarray data

    Karen Lemmens;Thomas Dhollander;Tijl De Bie;Pieter Monsieurs

  • An information theoretic framework for data mining

    Tijl De Bie

  • Automatic Chord Estimation from Audio: A Review of the State of the Art

    Matt McVicar;Raul Santos-Rodriguez;Yizhao Ni;Tijl De Bie

  • Research methods in the age of digital journalism

    Ilias Flaounas;Omar Ali;Thomas Lansdall-Welfare;Tijl De Bie

  • IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2015

    Jefrey Lijffijt;Eirini Spyropoulou;Bo Kang;Tijl De Bie

  • MINI: Mining Informative Non-redundant Itemsets

    Arianna Gallo;Tijl De Bie;Nello Cristianini

  • Semi-supervised learning using semi-definite programming

    Tijl De Bie;Nello Cristianini

  • Subjective interestingness in exploratory data mining

    Tijl De Bie

  • Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems

    Tijl De Bie;Nello Cristianini

  • An Information-Theoretic Approach to Finding Informative Noisy Tiles in Binary Databases.

    Kleanthis-Nikolaos Kontonasios;Tijl De Bie

  • DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli.

    Karen Lemmens;Tijl De Bie;Tijl De Bie;Thomas Dhollander;Sigrid C. J. De Keersmaecker

  • Learning from General Label Constraints

    Tijl De Bie;Johan A. K. Suykens;Bart De Moor

  • NOAM: news outlets analysis and monitoring system

    Ilias Flaounas;Omar Ali;Marco Turchi;Tristan Snowsill

Frequent Co-Authors

Nello Cristianini
Nello Cristianini University of Bath
Marco Turchi
Marco Turchi Fondazione Bruno Kessler
Bart De Moor
Bart De Moor KU Leuven
Yvan Saeys
Yvan Saeys Ghent University
Peter A. Flach
Peter A. Flach University of Bristol
Aristides Gionis
Aristides Gionis Royal Institute of Technology
Kathleen Marchal
Kathleen Marchal Ghent University
Elisa Ricci
Elisa Ricci Fondazione Bruno Kessler
Marc Leman
Marc Leman Ghent University
Matthew W. Hahn
Matthew W. Hahn Indiana University

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