PhD, Postdoctoral Researcher
I'm a postdoctoral researcher in the Probabilistic Machine Learning group at the Department of Computer Science at Aalto University and at Helsinki Institute for Information Technology HIIT. The group is also a part of the Finnish Center for Artificial intelligence (FCAI). I received my PhD in statistics in 2013 from the Department of Mathematics at Åbo Akademi University. Prior to joining my current group, I've been a member of the Bayesian Statistics and Fisheries and Environmental Management groups at the University of Helsinki. In the years 2007-2013, I also worked as a statistician at the National Institute for Health and Welfare (THL).
Much of my current work focuses on developing methods for scalable Bayesian inference, distributed probabilistic modeling and learning from multiple data sources. I have previously done work on predictive modeling, Bayesian clustering and applications of state-space models in population dynamics.
Here is a list of some recent papers. A more extensive list can be found here.
Mesquita, D. Blomstedt, P. and Kaski, S. (2019). Embarrassingly parallel MCMC using deep invertible transformations. Accepted to UAI 2019. [Preprint]
Qin, X., Blomstedt, P. and Kaski, S. (2019). Scalable Bayesian non-linear matrix completion. Accepted to IJCAI 2019.
Blomstedt, P., Mesquita, D., Lintusaari, J., Sivula, T., Corander, J. and Kaski, S. (2019). Meta-analysis of Bayesian analyses. arXiv:1904.0448#[stat.ME]. [Preprint]
Lintusaari, J., Blomstedt, P., Sivula, T., Gutmann, M. U. , Kaski, S. and Corander, J. (2019). Resolving outbreak dynamics using Approximate Bayesian Computation for stochastic birth-death models [version 1; referees: 1 approved with reservations]. Wellcome Open Research, 4(14). [Link] [Code]
Vehtari, A., Gelman, A., Sivula, T., Jylänki, P., Tran, D., Sahai, S., Blomstedt, P., Cunningham, J. P., Schiminovich, D. and Robert, C. (2018). Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data. arXiv:1412.4869#[stat.CO]. [Preprint] [Code]
Dutta, R., Blomstedt, P. and Kaski, S. (2016). Bayesian inference in hierarchical models by combining independent posteriors. Technical report. [Link]
Blomstedt, P., Dutta, R., Seth, S., Brazma, A. and Kaski, S. (2016). Modelling-based experiment retrieval: A case study with gene expression clustering. Bioinformatics, 32(9), 1388–1394. [Link] [Code]
Mäntyniemi, S., Whitlock, R., Perälä, T., Blomstedt, P., Vanhatalo, J., Rincón, M.M., Kuparinen, A. Pulkkinen, H. and Kuikka, S. (2015). General state-space population dynamics model for Bayesian stock assessment. ICES Journal of Marince Science, 72(8), 2209–2222. [Link]
Blomstedt, P., Tang, J., Granlund, C. and Corander, J. (2015). A Bayesian predictive model for clustering data of mixed discrete and continuous type. IEEE Transactions on Pattern Analysis and Machine intelligence, 37(3), 489–498. [Link] [Code]
Blomstedt, P. and Corander, J. (2015). Posterior predictive comparisons for the two-sample problem. Communications in Statistics – Theory and Methods, 44(2), 376–389. [Link]