Welcome!

This is the website of Pierre Talbot. I'm a research scientist at the University of Luxembourg. I'm working on new theories of constraint reasoning and parallel programming. I'm also the co-director of the Master in High Performance Computing. I serve in the program committees of AAAI (since 2024) and ICTAI (since 2025).

• 🎉22 September 2026: I obtained the ADR and the right to officially supervise PhD students!
• 15 September 2026: Welcome to Isaac Palma, starting his research on GPU-based cost network functions.
• 9 July 2026: We organized the first NEURESA workshop.
• 22-23 June 2026: I gave two online lectures for the blended intensive program A Journey into Knowledge and Reasoning [lecture].
• 15 June 2026: Welcome to Wenbo Han and Ching-Yao Tang doing a summer internship on GPU profiling and vectorizing Turbo on CPU..
• 🎉12 June 2026: Manuel Combarro successfully defended his PhD on Exact Multi-objective Algorithms in Constraint Programming.
• 🎉29 May 2026: Yi-Nung Tsao got his paper Towards Continuous Constraint Programming for Sound Neural Network Verification accepted at LogicNN 2026 [more info].
• 18 May 2026: We went to the international day at the French conference JFPC 2026 and we gave two talks.
• 🎉1 May 2026: Manuel Combarro got his paper Combining an ϵ-Constraint Method with the Pareto Global Constraint accepted at CP 2026 [more info].
• 🎉28 April 2026: Tobias Fischbach successfully defended his PhD on End-to-End Quantum Circuit Optimization using ZX-Calculus.
• 🎉17 March 2026: Hedieh Haddad successfully defended her PhD on Hyperparameter Optimization of Constraint Programming Solvers.
• 15 March 2026: Welcome to Clément Poncelet, new postdoc on the project NEURESA.
• 🎉12 March 2026: Hakan Hasan got his first paper Towards Distributed Constraint Solving with CRDT accepted at the workshop PaPoC 2026 [more info].
• 1 March 2026: Welcome to Wei Huang doing his Master thesis with us on Improving the Fixpoint Engine in Turbo.
• 15 February 2026: Welcome to Anisa Meta doing her Master thesis with us on GPU-based Inprocessing in Turbo.
• 21 January 2026: An afternoon discussing about lattices, fixpoint and FPOP with Prof. Michael Adams and his students from National University of Singapore [slides].
• 🎉20 January 2026: Our AI-HPC BRIDGE FNR project Neuro-Symbolic Reasoning for Embedded System Architectures Design (NEURESA) with Cognifyer has been accepted.
• 22-25 November 2025: We had a booth at the Science Festival 2025 to explain parallel computing to kids, teens and adults. [more info]
• 🎉8 November 2025: Really happy my paper A GPU-based Constraint Programming Solver has been accepted to AAAI 2026 with oral presentation [more info].

All news Can abstract interpretation be the backbone theory to unify constraint reasoning approaches?

• Goal I: Combine constraint solvers (by reduced products) to more efficiently solve combinatorial problems.
• Goal II: Generalize reasoning procedures (e.g., propagation, multiobjective algorithms, clause learning) to monotone functions working over any abstract domains.

We are working on a new theory of combinatorial optimization called abstract satisfaction, first introduced by D'Silva, Haller and Kroening. It relies on lattice theory and abstract interpretation to unify the subfields of combinatorial optimization.

Highlights:

• FNR AI-HPC BRIDGES 2026—NEURESA 2026-2029 to explore neuro-symbolic reasoning for embedded system architectures design (PI: P. Talbot, 1.135.000€).
  NEURESA explores a solution to Goal I: Can LLM be useful to build constraint solvers dynamically? Project in cooperation with Cognifyer.
• The lattice land project is a collection of C++ libraries implementing abstract domains such as intervals, octagons and new ones such as propagator completion.
• Our TPLP journal paper (2020) introduces the propagator completion and new a product of abstract domains.

Can lattice theory be the backbone of a safe model of parallel programming?

• Goal I: Make parallel programs correct-by-construction.
• Goal II: Take advantage of specialized hardware (e.g., GPUs, FPGAs, quantum architectures)

The foundation of this language is the same than for abstract constraint reasoning: lattice theory. In short: data are lattices, programs are monotone functions and the execution is the computation of a fixpoint. Our primary focus and application is to accelerate the algorithms developed for abstract constraint reasoning on parallel architectures.

Highlights:

• Turbo is an abstract constraint solver fully executing on the GPU.
• cuda-battery provides C++ data structures working on both the CPU and GPU (CUDA).
• Our AAAI2022 paper describes the foundation of this parallel model of computation.
• Our AAAI2026 paper describes the implementation of a GPU-based constraint solver within this model.
• FNR CORE Grant COMOC 2022-2025 to explore this strand of research (PI: P. Talbot, 384k€).

I have the pleasure to supervise and collaborate with several Master students, Ph.D. candidates and postdocs.

• Clément Poncelet, postdoctoral researcher on the project NEURESA, March 2026-.
• Hasan Hakan, Ph.D. candidate, Conflict-free Replicated Data Types for Distributed Constraint Programming, 2025-2028.
• Yi-Nung Tsao, Ph.D. candidate, Verification of Neural Networks by Abstract Interpretation, 2023-2027.
• Wenbo Han, Master student, In-depth GPU Profiling and MicroBenchmarks, June—December 2026.
• Isaac Palma, Master student, Cost Network Functions on GPU, September—December 2026.

Here the researchers with whom I have active (international) collaborations. Also the people passing by my lab :)

• Dr. Francesca Guffanti, mathematician, postdoctoral researcher at Université Savoie Mont Blanc, France.