Tim van Bremen
Research Fellow
School of Computing
National University of Singapore
Office address:
COM3 02-20
11 Research Link
Singapore 119391
Email: tvanbr [ at ] comp.nus.edu.sg
I am a Research Fellow in the School of Computing at the National University of Singapore, hosted by Kuldeep Meel.
My current research interests lie at the intersection of logic and counting, with applications to data management and reasoning under uncertainty. In particular, I am interested in designing fast and scalable algorithms for inference and learning in structured graphical models and probabilistic databases, as well as theoretical results in these areas.
Before coming to Singapore, I received a PhD in Computer Science in June 2022 at KU Leuven, supervised by Luc De Raedt and Ondřej Kuželka. Prior to that, I received an MS in Computer Science from National Taiwan University in 2017, and even before that I got a BEng in Mathematics and Computer Science from Imperial College London in 2015.
Together with a few colleagues, I help to organise the AlgoTheory seminar at NUS. Let me know if you're interested in giving a talk!
Publications: (see also dblp)
Conferences: (show)
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Probabilistic Query Evaluation: The Combined FPRAS Landscape (pdf)
Timothy van Bremen* and Kuldeep S. Meel*
ACM Symposium on Principles of Database Systems (PODS) 2023
(The PDF preprint linked corrects a couple of minor typos in the published version)
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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
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Automatic Conjecturing of P-Recursions Using Lifted Inference (doi)
Jáchym Barvínek, Timothy van Bremen, Yuyi Wang, Filip Železný, and Ondřej Kuželka
International Conference on Inductive Logic Programming (ILP) 2021
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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)
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Faster Lifting for Two-Variable Logic Using Cell Graphs (url)
Timothy van Bremen and Ondřej Kuželka
Conference on Uncertainty in Artificial Intelligence (UAI) 2021
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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
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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
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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
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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: (show)
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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 conferences without formally published proceedings: (show)
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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
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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)
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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)
Software:
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FastWFOMC
A tool for computing the weighted first-order model count of a two-variable sentence in a domain-lifted way.
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onto2problog
A tool for ontology-mediated query answering over probabilistic data for ontologies formulated in OWL 2 EL.
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SymGANAK
A probabilistic exact model counter with support for symmetric component caching.
