Tim van Bremen

I am a Research Fellow in the School of Computing at the National University of Singapore, hosted by Kuldeep S. Meel.

I am on the academic job market for positions starting in 2024.

CV | Research Statement | Teaching Statement

Please feel free to get in touch with me to discuss potential opportunities.

My current research interests lie in statistical-symbolic artificial intelligence and data management under uncertainty. More specifically, together with a number of 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 coming to Singapore, I graduated with 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 helped to organise the AlgoTheory seminar (2022/23) at NUS.

News

March 2024 Attending and speaking at EDBT/ICDT 2024 in Paestum, Italy.
March 2024 Visiting the School of Computer Science and Engineering at Nanyang Technological University in Singapore.
November 2023 One joint paper with Antoine Amarilli and Kuldeep Meel accepted at ICDT 2024.
October 2023 Attending and speaking at the Probabilistic Circuits and Logic workshop at the Simons Institute at UC Berkeley
October 2023 Visiting Moshe Vardi's group at Rice University in Houston, Texas.
August 2023 Journal version of our KR 2021 paper is accepted to the journal Artificial Intelligence
June 2023 Visiting the StarAI lab at UCLA in Los Angeles, California.
June 2023 Attending and speaking at SIGMOD/PODS 2023 in Seattle, Washington.
April 2023 Speaking at the NUS AlgoTheory seminar.
March 2023 One joint paper with Kuldeep Meel accepted at PODS 2023. My first database theory paper!
June 2022 Graduated with my PhD from KU Leuven, and joining Kuldeep Meel's group at NUS in sunny Singapore.

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
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.