Papers

  • Relation-guided acoustic scene classification aided with event embeddings
    Yuanbo Hou, Bo Kang, Wout Van Hauwermeiren, Dick Botteldooren
    International Joint Conference on Neural Networks (IJCNN), 2022.
    preprint
  • CT-SAT: Contextual Transformer for Sequential Audio Tagging
    Yuanbo Hou, Zhaoyi Liu, Bo Kang, Yun Wang, Dick Botteldooren
    Annual Conference of the International Speech Communication Association (INTERSPEECH), 2022.
    preprint
  • Evaluating Representation Learning and Graph Layout Methods for Visualization
    Edith Heiter, Bo Kang, Tijl De Bie, Jefrey Lijffijt
    IEEE Computer Graphics and Applications, 2022.
    paper
  • Graph-Survival: A Survival Analysis Framework for Machine Learning on Temporal Network
    Raphaël Romero, Bo Kang, Tijl De Bie
    arXiv, 2022.
    preprint
  • Topologically Regularized Data Embeddings
    Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, and Yvan Saeys
    In The 10th International Conference on Learning Representations (ICLR), 2022.
    paper preprint
  • The Curse Revisited: a Newly Quantified Concept of Meaningful Distances for Learning from High-Dimensional Noisy Data
    Robin Vandaele, Bo Kang, Tijl De Bie, and Yvan Saeys
    In The 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
    paper preprint
  • ExClus: Explainable Clustering on Low-dimensional Data Representations
    Xander Vankwikelberge, Bo Kang, Edith Heiter, and Jefrey Lijffijt
    Joint AI & ML conference for Belgium, Netherlands & Luxemburg (BNAIC/BeneLearn), 2021.
    paper   project
  • FONDUE : a framework for node disambiguation and deduplication using network embeddings
    Ahmad Mel, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    Applied Sciences, 2021.
    paper
  • Quantifying and reducing imbalance in networks
    Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In The ACM RecSys Workshop on Recommender Systems for Human Resources (RecSys in HR), 2021.
    paper
  • Adversarial robustness of probabilistic network embedding for link prediction
    Xi Chen, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In Proceedings of the 3rd ECML-PKDD Workshop on Machine Learning for Cybersecurity (MLCS), 2021.
    preprint
  • Explanations for Network Embedding-based Link Predictions
    Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In Proceedings of the 3rd ECML-PKDD Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD), 2021.
    preprint   video
  • Quantifying and reducing imbalance in networks
    Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In The 1st ECML-PKDD Workshop on Fair, Effective And Sustainable Talent management using data science (FEAST), 2021.
    paper
  • ALPINE: Active link prediction using network embedding
    Xi Chen, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In Applied Science, 2021.
    paper preprint
  • Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
    Bo Kang, Darío García García, Jefrey Lijffijt, Raúl Santos-Rodríguez, and Tijl De Bie
    In Machine Learning(MLJ), 2021
    paper preprint   video
  • Network embedding method
    Tijl De Bie, Bo Kang, and Jefrey Lijffijt
    US Patent, 2020
    Google patents
  • Mining explainable local and global subgraph patterns with surprising densities
    Junning Deng, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In Data Mining and Knowledge Discovery(DAMI), 2020.
    paper preprint
  • FONDUE: A framework for node disambiguation using network embeddings
    Ahmad Mel, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In International Conference on Data Science and Advanced Analytics (DSAA), 2020.
    paper preprint
  • Explainable subgraphs with surprising densities: a subgroup discovery approach
    Junning Deng, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In SIAM International Conference on Data Mining (SDM), 2020.
    preprint
  • Learning subjectively interesting data representations
    Bo Kang
    Faculty of Engineering and Architecture, Ghent University, 2019.
    thesis
  • SMIT: subjectively interesting motifs in time series
    Junning Deng, Jefrey Lijffijt, Bo Kang, and Tijl De Bie
    In Entropy, 2019.
    paper
  • Explainable subgraphs with surprising densities: a subgroup discovery approach
    Junning Deng, Jefrey Lijffijt, Bo Kang, and Tijl De Bie
    In ACM SIGKDD Workshop on Mining and Learning with Graphs (MLG), 2019.
    paper
  • Interactive visual data exploration with subjective feedback: an information-theoretic approach
    Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In Data Mining and Knowledge Discovery (DAMI), 2019.
    paper
  • A constrained randomization approach to interactive visual data exploration with subjective feedback
    Bo Kang, Kai Puolamäki, Jefrey Lijffijt, and Tijl De Bie
    In IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.
    paper
  • Conditional Network Embeddings
    Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In International Conference on Learning Representations (ICLR), 2019.
    paper
  • Subjectively Interesting Motifs in Time Series
    Junning Deng, Jefrey Lijffijt, Bo Kang and Tijl De Bie
    In 3rd ECML-PKDD Workshop on Advanced Analytics and Learning on Temporal Data (ECML-PKDD), 2018.
    paper
  • SICA: Subjectively Interesting Component Analysis
    Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodríguez, and Tijl De Bie
    In Data Mining and Knowledge Discovery (DAMI), 2018.
    paper
  • Subjectively interesting subgroup discovery on real-valued targets
    Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, and Tijl De Bie
    In IEEE International Conference on Data Engineering (ICDE), 2018.
    preprint   extended version at arXiv
  • Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach
    Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In IEEE International Conference on Data Engineering (ICDE), 2018.
    preprint   extended version at arXiv
  • Clipped Projections for More Informative Visualizations [A Work-in-Progress Report]
    Bo Kang, Junning Deng, Jefrey Lijffijt, and Tijl De Bie
    In ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA), 2017.
    paper   poster
  • Interactive Visual Data Exploration with Subjective Feedback
    Kai Puolamäki, Bo Kang, Jefrey Lijffijt, and Tijl De Bie
    In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), 2016.
    paper
  • A Tool for Subjective and Interactive Visual Data Exploration
    Bo Kang, Kai Puolamäki, Jefrey Lijffijt, and Tijl De Bie
    In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), 2016.
    paper   demo
  • Subjectively Interesting Component Analysis: Data Projections that Contrast with Prior Expectations
    Bo Kang, Jefrey Lijffijt, Raúl Santos-Rodríguez, and Tijl De Bie
    In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016.
    paper   poster   video
  • SIDE: A Web App for Interactive Visual Data Exploration with Subjective Feedback
    Jefrey Lijffijt, Bo Kang, Kai Puolamäki, and Tijl De Bie
    In ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA), 2016.
    paper
  • Informative Data Projections: A Framework and Two Examples
    Tijl De Bie, Jefrey Lijffijt, Raúl Santos-Rodríguez, and Bo Kang
    In European Symposium on Artificial Neural Networks (ESANN), 2016.
    preprint
  • P-N-RMiner: A Generic Framework for Mining Interesting Structured Relational Patterns
    Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, and Tijl De Bie
    In International Journal of Data Science and Analytics, 2016.
    paper
  • P-N-RMiner: A Generic Framework for Mining Interesting Structured Relational Patterns
    Jefrey Lijffijt, Eirini Spyropoulou, Bo Kang, and Tijl De Bie
    In IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015.
    paper
  • Creedo―Scalable and Repeatable Extrinsic Evaluation for Pattern Discovery Systems by Online User Studies
    Mario Boley, Maike Krause-Traudes, Bo Kang, and Björn Jacobs
    In ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA), 2015.
    paper   project
  • A Framework of Quantifying Subjective Unexpectedness of Pattern Measurements
    Bo Kang
    Institute of Computer Science, University of Bonn, 2015.
    thesis
  • One Click Mining―Interactive Local Pattern Discovery through Implicit Preference and Performance Learning
    Mario Boley, Michael Mampaey, Bo Kang, Pavel Tokmakov, and Stefan Wrobel
    In ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA), 2013.
    paper   project

Software

  • Explanations for Network Embedding-based Link Prediction   project
  • Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information   project
  • DataUntangler: Interactive Data Exploration with Embeddings and Probing   project
  • CNE: Conditional Network Embeddings   project
  • Subjectively Interesting Subgroup Discovery on Real-valued Targets   project
  • SIDE: A Tool for Subjective and Interactive Visual Data Exploration   demo
  • SICA: Subjectively Interesting Component Analysis   code

Working Experience

  • Research Staff, Ghent University, 2015 - now.
  • Research Intern, Facebook AI, Jun.2018 - Oct.2018.
  • Research Staff, University of Bristol, Summer 2015.
  • Research Assistant, Fraunhofer IAIS, 2012 - 2015.
  • Research Assistant, University of Bonn, Winter 2012.

Teaching

Awards

  • IBM Innovation Award, 2020. tweet
  • International Conference on Learning Representations (ICLR) Travel Award, 2019.

Talks and Posters

  • A recommender system deployed on VDAB data
    UGent @Work Symposium, Ghent University, 2022.
    slides tba
  • Subjectively Interesting Data Representations
    Research Seminar of the TUW RU Machine Learning, TU Wien, 2021.
    slides tba
  • A Recommender Platform Deployed on VDAB Data
    FLAIR WP8-T8.3 Workshop, Ghent, Belgium, 2021.
    slides tba
  • Conditional Network Embeddings
    BNAIC19 & Benelearn19, Brussels, Belgium, 2019.
    slides tba
  • Conditional t-SNE
    Tufts University, United States, 2018.
    slides
  • CLIPPR: Maximally Informative CLIPped PRojections with Bounding Regions
    with Dylan Cashman, Remco Chang, Jefrey Lijffijt and Tijl De Bie
    In IEEE Visual Analytics in Science and Technology (VAST), 2018.
    poster   abstract
  • A graph based approach for formalizing subjective interestingness of data projections
    with Jefrey Lijffijt, Raúl Santos-Rodríguez and Tijl De Bie
    In International Symposium on Intelligent Data Analysis (IDA), 2015.
    poster
  • One Click Mining: Interactive Local Pattern Discovery through Implicit Preference and Performance Learning
    Advanced Database Research and Modeling Group, University of Antwerp, Belgium, 2013.
    talk slides

Community Services

Organisation of conferences, workshops

  • Co-chair. ECML-PKDD Workshop on Fair, Effective and Sustainable Talent Management Using Data Science (FEAST 2022), Grenoble, France. Website
  • Co-chair. ECML-PKDD Workshop on Fair, Effective and Sustainable Talent Management Using Data Science (FEAST 2021), Virtual, Spain. Website
  • Co-chair. ECML-PKDD Workshop on Graph Embedding and Mining (GEM 2021), Virtual, Spain. Website
  • Web chair, Virtual conference chair. European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD 2020), Ghent, Belgium. Website
  • Co-chair. ECML-PKDD Workshop on Graph Embedding and Mining (GEM 2020), Ghent, Belgium. Website
  • Co-chair. ECML-PKDD Workshop on Graph Embedding and Mining (GEM 2019), Würzburg, Germany. Website

Reviewer for journals

  • Machine Learning Journal (MLJ)
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)
  • IEEE Transactions on Big Data (BigData)

Program committee member for conferences and workshops

  • European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML-PKDD), 2016, 2017, 2018, 2019, 2020, 2021, 2022.
  • Neural Information Processing Systems (NeurIPS), 2022.
  • International Conference on Machine Learning (ICML), 2022.
  • ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), 2022.
  • ACM International Conference on Web Search and Data Mining (WSDM), 2022.
  • Learning on Graphs Conference (LoG), 2022.
  • IEEE Visualization Conference (VIS), 2022.
  • International Symposium on Intelligent Data Analysis, 2022.
  • ACM RecSys Workshop on Recommender Systems for Human Resources (RecSys in HR), 2021.
  • The Web Conference, 2021, 2022.
  • SIAM International Conference on Data Mining (SDM), 2021, 2022.
  • International Joint Conference on Artificial Intelligence (IJCAI), 2020.
  • European Conference on Artificial Intelligence (ECAI), 2020.
  • ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA), 2017, 2018.
  • International Conference on Discovery Science (DS), 2018.
  • Computer Science Conference for University of Bonn Students (CSCUBS), 2014, 2015.

Examination committee member

  • Xander Vankwikelberge, ExClus: Explainable Clustering on Low-dimensional Data Representations, Master of Computer Science Engineering, Ghent University, 2021.
  • Robin Vandaele, Topological Inference in Graphs and Images, Doctor of Computer Science Engineering, Ghent University, 2020.

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