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Associate Professor

Bogdan Cautis

PhD in Computer Science, University of Paris-Sud & Inria, France
Cluster:
Infocomm Technology
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Biography

Academic Career
  • 2025 - Ongoing: Associate Professor, Infocomm Technology Cluster, Singapore Institute of Technology (SIT)
  • 2013 - 2025: Professor, University of Paris-Saclay (formerly University of Paris-Sud), France
  • 2021 - 2024: Researcher (lead PI) on secondment at CNRS@CREATE Singapore and Adjunct Professor, NUS School of Computing
  • 2007 - 2013: Assistant / Associate Professor, Telecom ParisTech, France

 

Bogdan Cautis obtained his PhD in Computer Science from the University of Paris-Sud (now University of Paris-Saclay) and Inria France, in 2007 under the supervision of Serge Abiteboul (Inria) and Tova Milo (Tel Aviv University); he obtained a MSc degree (D.E.A. Algorithmique) in Computer Science from Ecole polytechnique, France, in 2004. He received his Habilitation to Supervise Research (HdR) in 2012, from Université Pierre et Marie Curie, France. Since 2013 he is a Professor of Computer Science at University Paris-Saclay (now on extended leave of absence). Between 2007 and 2013 he was an Associate Professor at Telecom ParisTech (formerly known as Ecole Nationale Supérieure des Télécommunications – ENST). 

His research is in the areas of data mining and data management, with a specific focus on graphs. He publishes regularly in top-tier conferences (SIGMOD, KDD, VLDB, IJCAI, ECML-PKDD, AAAI, ICDM, etc) and journals (TODS, TKDD, TKDE, Springer DAMI, etc) on AI and databases. He was lead Principal Investigator in various French (ANR) and EU (FP7) projects; since November 2021 he is a lead Principal Investigator in DesCartes, a CNRS-managed Singapore-France joint research program (funded by the NRF-CREATE) on intelligent modelling for decision-making in critical urban systems.

He has a 20+ years teaching experience in databases, data mining, information retrieval and the Web, or programming languages, and has taught in France, Hong Kong, and Singapore, at both undergraduate and graduate levels.

Since 2007, he has supervised thirteen PhD students and several postdoctoral researchers.

SIT Appointments

  • – Present

Education

  • Habilitation to supervise research (HDR)
    University Pierre et Marie Curie - Paris 6 , France
  • PhD in Computer Science
    University of Paris-Sud & Inria , France
  • MSc in Computer Science
    Ecole polytechnique , France

Corporate Experience

  • AAAI Senior PC
    – Present
  • PVLDB Associate Editor
    Present
  • ACM SIGKDD Area Chair
    Present
  • Senior Researcher, Huawei Noah's Ark Lab, Hong Kong
    –
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Research Interests

  • Data Science, Big Data, Databases

    Theory and systems

  • Graphs and graph mining

    Information diffusion, social media analytics, GNNs

Current Projects

  • Multimodal Graph Retrieval Augmented Generation for Creative Assets (Ignition Grant)
    – Present

    The proposed research develops and evaluates a prototype multimodal Graph Retrieval
    Augmented Generation (mmGraphRAG) system capable of retrieving and reasoning over
    heterogeneous data modalities. The work focuses on three complementary research components,
    addressing knowledge representation, multimodal representation learning, and graph-based
    retrieval. In collaboration with Riot Games.  

  • DESCARTES: A CREATE Program on AI-based Decision making in Critical Urban Systems (NRF)
    – Present

    Lead Principal Investigator for one Work Package on Learning from Smart and Complex Data for Hybrid AI

Past Projects

  • ALICIA: Adaptive learning for intelligent crowdsourcing and information access (ANR France)
    –

    Scientific lead & coordinator

  • ARCOMEM: Leveraging the wisdom of the crowds and the social Web for intelligent preservation (EU FP7)
    –

    Institutional lead Principal Investigator for one Work Package

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Journal Papers

  • Lightweight Influence-Aware News Recommendation in Social Media. Yuting Feng, Bogdan Cautis. IEEE Data Engineering Bulletin, vol. 49, no 2, 2025.

  • Topic-aware influence maximization with deep reinforcement learning and graph attention networks. Taha Halal, Bogdan Cautis, Benoit Groz, Ruize Gao. Springer Data Mining and Knowledge Discovery - ECML-PKDD 2025 Journal Track, vol. 30, no. 71, 2025. https://tinyurl.com/bdf5f2wx

    • Volume 39, article number 71, (2025)Volume 39, article number 71, (2025)

Conferences

  • Hyperparametric Influence Minimization: Feature-Driven Intervention Beyond Blocking. Bin Xiang, Bogdan Cautis, Xiaokui Xiao, Laks V. S. Lakshmanan. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2025.

  • Hyperparametric Robust and Dynamic Influence Maximization. Arkaprava Saha, Bogdan Cautis, Xiaokui Xiao, and Laks V. S. Lakshmanan. Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI), 2025.

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