Måns Magnusson
Universitetslektor vid Statistiska institutionen
- E-post:
- mans.magnusson@statistik.uu.se
- Besöksadress:
- Ekonomikum (plan 3)
Kyrkogårdsgatan 10 - Postadress:
- Box 513
751 20 UPPSALA
Ladda ned kontaktuppgifter för Måns Magnusson vid Statistiska institutionen
- ORCID:
- 0000-0002-0296-2719
Nyckelord
- digital humanities
- computational social science
- textual analysis
- probabilistic machine learning
- bayesian statistics
- model comparison and evaluation
- text-as-data
- survey sampling
Biografi
I am currently an assistant professor in Statistics at Department of Statistics, Uppsala University. Before Uppsala University, I was a postdoc under Aki Vehtari at the Department of Computer Science at Aalto University, Finland. I hold a PhD from Linköping University, Sweden, supervised by Mattias Villani and Marco Kuhlmann as co-supervisors. During my PhD, I was a guest researcher/PhD at Cornell University under David Mimno.
In 2019, I was awarded the Cramér prize for the best dissertation in Statistics and Mathematical Statistics in Sweden for my thesis Scalable and Efficient Probabilistic Topic Model Inference for Textual Data.
I hold two research grants from the Swedish Research Council (Mining for meaning and Welfare state Analytics) and have been awarded the Academy of Finland postdoctoral researcher grant. I am also affiliated with the Institute for Analytical Sociology at Linköping University.
I have a background as a statistician for the Swedish Agency for Education, the Swedish Agency for Crime Prevention, and the Swedish Agency for Public Health where I was working on education statistics, crime statistics and public health.

Publikationer
Senaste publikationer
Bias in Legal Data for Generative AI
Ingår i Generative AI and Law (GenLaw’24’), 2024
Ingår i The R Journal, s. 4-14, 2024
The Swedish parliament corpus 1867–2022
Ingår i Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), s. 16100-16112, 2024
Ingår i Communications in Statistics - Theory and Methods, s. 5877-5899, 2023
The Cambridge Law Corpus: A Dataset for Legal AI Research
Ingår i Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023
Alla publikationer
Artiklar i tidskrift
Ingår i The R Journal, s. 4-14, 2024
Ingår i Communications in Statistics - Theory and Methods, s. 5877-5899, 2023
From Documents to Data: A Framework for Total Corpus Quality
Ingår i SOCIUS, 2022
- DOI för From Documents to Data: A Framework for Total Corpus Quality
- Ladda ner fulltext (pdf) av From Documents to Data: A Framework for Total Corpus Quality
Konferensbidrag
Bias in Legal Data for Generative AI
Ingår i Generative AI and Law (GenLaw’24’), 2024
The Swedish parliament corpus 1867–2022
Ingår i Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), s. 16100-16112, 2024
The Cambridge Law Corpus: A Dataset for Legal AI Research
Ingår i Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
Ingår i Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), s. 2925-2934, 2020
Robust, Accurate Stochastic Optimization for Variational Inference
Ingår i Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020