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.
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.
My current research lies in the area of statistical-relational learning. In particular, I am interested in designing fast and scalable algorithms for inference and learning in structured graphical models, probabilistic databases, and probabilistic programs.
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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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.
