I do research in probabilistic programming, an interdisciplinary field with influences from computer science, probability theory, statistics, machine learning, and artificial intelligence. In my research, I focus on developing mathematical foundations and efficient compilers for probabilistic programming languages. I am particularly interested in programming language theory,compilers, and static program analysis.
I carry out research as part of the ASSEMBLE project. See the ASSEMBLE web page for more information.
Distinguished Artifact Award at the European Symposium on Programming, 2022.
Commissions of Trust
Doctoral student representative in the ICT doctoral program council, 2018 – .
Program Committees and Reviewing Assignments
- Teacher in IS1200 Computer Hardware Engineering, August 2017 – .
- Teacher in IS1500 Computer Organization and Components, August 2017 – .
Conference and Journal Articles
- Daniel Lundén, Joey Öhman, Jan Kudlicka, Viktor Senderov, Fredrik Ronquist, David Broman. Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference. ESOP 2022. [ SpringerLink | DiVA | PDF | arXiv (extended) | PDF (extended) ]
- Daniel Lundén, Johannes Borgström, and David Broman. Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages. ESOP 2021. [ SpringerLink | DiVA | PDF | arXiv (extended) | PDF (extended) ]
- Fredrik Ronquist, Jan Kudlicka, Viktor Senderov, Johannes Borgström, Nicolas Lartillot, Daniel Lundén, Lawrence Murray, Thomas B. Schön, and David Broman. Universal probabilistic programming offers a powerful approach to statistical phylogenetics. In Communications Biology 4. 2021. [ Nature | bioRxiv | PDF ]
- Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, and Thomas Schön. Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics. AISTATS 2018. [ AISTATS | PDF ]
- Daniel Lundén, David Broman, Fredrik Ronquist, and Lawrence M. Murray. Automatic Alignment of Sequential Monte Carlo Inference in Higher-Order Probabilistic Programs. 2018. [ arXiv | PDF ]
Workshop Extended Abstracts
- Daniel Lundén, Joey Öhman, David Broman. Compilation of Universal Probabilistic Programs to GPGPUs. PROBPROG 2020. [ PROBPROG | PDF | Poster ]
- Daniel Lundén, David Broman, Fredrik Ronquist, and Lawrence M. Murray. Automatic Discovery of Static Structures in Probabilistic Programs. PROBPROG 2018. [ PROBPROG | PDF | Poster ]
- Daniel Lundén, David Broman, and Lawrence M. Murray. Combining Static and Dynamic Optimizations Using Closed-Form Solutions. PPS 2018. [ PPS | | ]
- Daniel Lundén. Delayed sampling in the probabilistic programming language Anglican. Master's thesis, KTH Royal Institute of Technology. 2017. [ DiVA | PDF ]
- Erik Forsblom, Daniel Lundén.Factoring integers with parallel SAT solvers. Bachelor’s thesis, KTH Royal Institute of Technology. 2015. [ DiVA | PDF ]