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. I am also a member of the The Finnish Centre of Excellence in Computational Inference Research (COIN). 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.
Gelman, A., Vehtari, A., Jylänki, P., Sivula, T., Tran, D., Sahai, S., Blomstedt, P., Cunningham, J. P., Schiminovich, D. and Robert, C. (2017). Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data. arXiv:1412.4869#[stat.CO]. [Preprint]
Qin, X., Blomstedt, P., Leppäaho, E., Parviainen, P. and Kaski, S. (2017). Distributed Bayesian Matrix Factorization with Minimal Communication. arXiv:1703.00734#[stat.ML]. [Preprint]
Dutta, R., Blomstedt, P. and Kaski, S. (2016). Bayesian inference in hierarchical models by combining independent posteriors. arXiv:1603.09272#[stat.CO]. [Preprint]
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]
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]
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]
Blomstedt, P., Vanhatalo, J., Ulmestrand, M., Gårdmark, A., and Mäntyniemi, S. (2014). A Bayesian length-based population dynamics model for northern shrimp (Pandalus Borealis). Public Deliverable D4.18, the ECOKNOWS project (FP7/2007-2013 grant no. 244706). arXiv:1509.08774#[stat.AP]. [Preprint]
Blomstedt, P., Gauriot, R., Viitala, N., Reinikainen, T., and Corander, J. (2014). Bayesian predictive modeling and comparison of oil samples. Journal of Chemometrics, 28(1), 51–59. [Link]
Prokopenko, I., Poon, W., Mägi, R. et al. (2014). A Central Role for GRB10 in Regulation of Islet Function in Man. PLoS Genetics, 10(4), e1004235. [Link]