At a general level, my research interests are focused around developing and applying statistical and machine learning methods for real-world applications. Some key topics are:
Statistical machine learning
Probabilistic graphical models
Network structure learning
Computational causal inference
I completed my PhD in statistics in 2016 at Åbo Akademi University (Finland), in which I developed a new class of probabilistic graphical models, along with algorithms for learning the structure of the models from data. During 2016-2020, I worked as a postdoc at University of Helsinki (Finland), developing and applying data analysis tools, primarily for applications in bacterial statistical genomics. Since February 2020, I have been working at UiO as an Associate Professor in Statistics and Data Science.
- STK4011 - Statistical Inference Theory (Autumn 2020).
- STK4290 - Probabilistic Graphical Models (Spring 2021).
Supervision (PhD students)
- Juri Kuronen (Department of Biostatistics, 2018-): High-dimensional structure learning of Markov networks with applications in bacterial statistical genomics.
- Ghadi Al Hajj (Department of Informatics, 2020-): Improving generalization of machine learning models in medical image and immune receptor sequence analysis through the incorporation of domain priors and constraints.
Finnish Statistical Society - Doctoral Thesis Award (2013-2016).
For a complete list, see my profile at Google Scholar.
- Tadei, Alessandro; Haajanen, Juulia; Pensar, Johan; Santtila, Pekka & Antfolk, Jan (2020). Counteracting deceptive responding in the Finnish Investigative Instrument of Child Sexual Abuse (FICSA). Journal of Sexual Aggression. ISSN 1355-2600. . doi: 10.1080/13552600.2020.1846802 Full text in Research Archive.
- Top, Janetta; Arredondo-Alonso, Sergio; Schürch, Anita C.; Puranen, Santeri; Pesonen, Maiju; Pensar, Johan; Willems, Rob J.L. & Corander, Jukka (2020). Genomic rearrangements uncovered by genome-wide co-evolution analysis of a major nosocomial pathogen, Enterococcus faecium. Microbial Genomics. ISSN 2057-5858. 6(12), s 1- 8 . doi: 10.1099/mgen.0.000488 Full text in Research Archive.
- Viinikka, Jussi; Hyttinen, Antti; Pensar, Johan & Koivisto, Mikko (2020). Towards Scalable Bayesian Learning of Causal DAGs. Advances in Neural Information Processing Systems. ISSN 1049-5258.