Research

My research interests are probabilistic programming languages, their applications (especially in evolutionary biology and biodiversity monitoring), and probabilistic inference (especially Monte Carlo methods). I collaborate with KTH Royal Institute of Technology and the Swedish Museum of Natural History.

Publications

Peer-reviewed publications and preprints

Lundén, D., Hummelgren, L., Kudlicka, J., Eriksson, O., & Broman, D. (2024). Suspension analysis and selective continuation-passing style for universal probabilistic programming languages. In European Symposium on Programming (ESOP), Programming Languages and Systems. Springer.
CORE ranking of the conference: A

Senderov, V.^†, Kudlicka, J.^†, Lundén, D., Palmkvist, V., Braga, M. P., Granqvist, E., Broman, D., & Ronquist, F. (2023). TreePPL: A universal probabilistic programming language for phylogenetics. bioRxiv preprint; revised manuscript in preparation following peer review.
^† Joint first authors.

Iwaszkiewicz-Eggebrecht, E., Granqvist, E., Buczek, M., Prus, M., Kudlicka, J., Roslin, T., Tack, A. J., Andersson, A. F., Miraldo, A., Ronquist, F., & Łukasik, P. (2023). Optimizing insect metabarcoding using replicated mock communities. Methods in Ecology and Evolution, 14(4), 1130–1146.
Journal impact factor (2022): 6.6

Lundén, D., Öhman, J., Kudlicka, J., Senderov, V., Ronquist, F., & Broman, D. (2022). Compiling universal probabilistic programming languages with efficient parallel sequential Monte Carlo inference. In European Symposium on Programming (ESOP), Programming Languages and Systems, Lecture Notes in Computer Science, vol. 13240 (pp. 29–56). Springer.
Awarded ESOP’22 Distinguished Artifact Award.
CORE2021 ranking of the conference: A

Ronquist, F.^†, Kudlicka, J.^†, Senderov, V.^†, Borgström, J., Lartillot, N., Lundén, D., Murray, L. M., Schön, T. B., & Broman, D. (2021). Universal probabilistic programming offers a powerful approach to statistical phylogenetics. Communications Biology, 4, 244.
^† Joint first authors.
Journal impact factor (2020): 6.268

Kudlicka, J., Murray, L. M., Schön, T. B., & Lindsten, F. (2020). Particle filter with rejection control and unbiased estimator of the marginal likelihood. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5860–5864). IEEE.
CORE2018 ranking of the conference: B

Kudlicka, J., Murray, L. M., Ronquist, F., & Schön, T. B. (2019). Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling. In Uncertainty in Artificial Intelligence (UAI).
CORE2018 ranking of the conference: A*

Murray, L. M., Lundén, D., Kudlicka, J., Broman, D., & Schön, T. B. (2018). Delayed sampling and automatic Rao-Blackwellization of probabilistic programs. In International Conference on Artificial Intelligence and Statistics (AISTATS) (pp. 1037–1046). PMLR.
CORE2018 ranking of the conference: A

Doctoral thesis

Kudlicka, J. (2021). Probabilistic Programming for Birth-Death Models of Evolution. Doctoral dissertation, Uppsala University. Acta Universitatis Upsaliensis.