Tim van Bremen

I am an Assistant Professor in the College of Computing and Data Science at Nanyang Technological University.

I have open positions for PhD students, postdocs, and research assistants to work with me. (more info)

My research interests lie in Artificial Intelligence (Statistical-Symbolic Models, Probabilistic Inference, Probabilistic Programming), Data Management (Probabilistic Databases, Data Provenance), and Knowledge Representation and Reasoning (Knowledge Compilation, Reasoning under Uncertainty), encompassing both theoretical and practical aspects. Specifically, together with my collaborators, I have worked in the following areas:

  • Probabilistic Inference in Statistical-Symbolic Models
    I have worked on developing scalable algorithms [UAI-21, AAAI-21, IJCAI-21, IJCAI-20] for exact and approximate inference in statistical-symbolic models, such as Markov logic networks and probabilistic logic programs in languages like ProbLog. I have also contributed to mapping the boundary between tractable and intractable models for inference [KR-21]. In addition, I have investigated applications of efficient inference algorithms in combinatorics and probabilistic networks [ILP-21, PROBPROG-21], and have further worked on formalizing and studying notions of efficient sampling in statistical-symbolic models [AAAI-22].
  • Query Evaluation on Probabilistic Databases
    More recently, I have been doing research in tuple-independent probabilistic databases, which extend classic relational databases to incorporate uncertainty. I have sought to understand when queries on such databases can and cannot be approximately evaluated (with rigorous (ε,δ)-style guarantees) in polynomial time [ICDT-24, PODS-23]. I have also worked on developing practical approaches for ontology-mediated querying of probabilistic data [CIKM-19].

Before joining NTU in August 2024, I was a postdoc for two years at the National University of Singapore, hosted by Kuldeep Meel. Prior to coming to Singapore, I graduated with a PhD in Computer Science in 2022 at KU Leuven, supervised by Luc De Raedt and Ondřej Kuželka.

News

Publications (see also dblp)

Conferences:

  • Conjunctive Queries on Probabilistic Graphs: The Limits of Approximability (pdf, arXiv, doi, slides)
    Antoine Amarilli*, Timothy van Bremen*, and Kuldeep S. Meel*
    International Conference on Database Theory (ICDT) 2024
    (Invited to a special issue of the journal Logical Methods in Computer Science)
  • Probabilistic Query Evaluation: The Combined FPRAS Landscape (pdf, doi)
    Timothy van Bremen* and Kuldeep S. Meel*
    ACM Symposium on Principles of Database Systems (PODS) 2023
  • Domain-Lifted Sampling for Universal Two-Variable Logic and Extensions (doi)
    Yuanhong Wang, Timothy van Bremen, Yuyi Wang, and Ondřej Kuželka
    AAAI Conference on Artificial Intelligence (AAAI) 2022
  • Automatic Conjecturing of P-Recursions Using Lifted Inference (pdf, doi)
    Jáchym Barvínek, Timothy van Bremen, Yuyi Wang, Filip Železný, and Ondřej Kuželka
    International Conference on Inductive Logic Programming (ILP) 2021
  • Lifted Inference with Tree Axioms (doi)
    Timothy van Bremen and Ondřej Kuželka
    International Conference on Principles of Knowledge Representation and Reasoning (KR) 2021
    (Marco Cadoli Best Student Paper Award Runner-up)
  • Faster Lifting for Two-Variable Logic Using Cell Graphs (url)
    Timothy van Bremen and Ondřej Kuželka
    Conference on Uncertainty in Artificial Intelligence (UAI) 2021
  • Symmetric Component Caching for Model Counting on Combinatorial Instances (doi)
    Timothy van Bremen*, Vincent Derkinderen*, Shubham Sharma*, Subhajit Roy, and Kuldeep S. Meel
    AAAI Conference on Artificial Intelligence (AAAI) 2021
  • Fast Algorithms for Relational Marginal Polytopes (doi)
    Yuanhong Wang, Timothy van Bremen, Yuyi Wang, Juhua Pu, and Ondřej Kuželka
    International Joint Conference on Artificial Intelligence (IJCAI) 2021
  • From Probabilistic NetKAT to ProbLog: New Algorithms for Inference and Learning in Probabilistic Networks (pdf)
    Birthe van den Berg*, Timothy van Bremen*, Vincent Derkinderen*, Angelika Kimmig, Tom Schrijvers, and Luc De Raedt
    International Conference on Probabilistic Programming (PROBPROG) 2021
  • Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry (doi)
    Timothy van Bremen and Ondřej Kuželka
    International Joint Conference on Artificial Intelligence (IJCAI) 2020
  • Ontology-mediated Queries over Probabilistic Data via Probabilistic Logic Programming (pdf, doi)
    Timothy van Bremen*, Anton Dries*, and Jean Christoph Jung*
    ACM International Conference on Information and Knowledge Management (CIKM) 2019

Journals:

  • Lifted Inference with Tree Axioms (pdf, doi)
    Timothy van Bremen and Ondřej Kuželka
    Artificial Intelligence (AIJ) 2023
    (Journal version of the KR 2021 paper)
  • onto2problog: A Probabilistic Ontology-mediated Querying System using Probabilistic Logic Programming (doi)
    Timothy van Bremen*, Anton Dries*, and Jean Christoph Jung*
    KI - Künstliche Intelligenz (German Journal of Artificial Intelligence) 2020
    (This is a "systems description" version of the CIKM 2019 paper)

Peer-reviewed workshops and domestic conferences: (show)

  • Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry (arXiv)
    Timothy van Bremen and Ondřej Kuželka
    International Workshop on Statistical Relational AI (StarAI) at AAAI 2020
    (Preliminary version of a paper later published at IJCAI 2020)
  • Efficient Cardinality Constraints in ProbLog
    Timothy van Bremen, Wannes Meert, and Luc De Raedt
    Benelux Conference on Artificial Intelligence (BNAIC) 2018

(* = alphabetical order or equal contribution)

Where possible, I try to keep the PDF versions of papers linked above up-to-date with corrections to any errors appearing in the published version.

Software

  • FastWFOMC
    A tool for computing the weighted first-order model count of a two-variable sentence in a domain-lifted way.
  • onto2problog
    A tool for ontology-mediated query answering over probabilistic data for ontologies formulated in OWL 2 EL.
  • SymGANAK
    A probabilistic exact model counter with support for symmetric component caching.

Recent Teaching

Please see NTULearn for course material.