The Biodata analytics unit comprises several scientists with complementary skills and provides access to data analysis expertise (bioinformatics, modelling and statistics) that may not be easy to acquire by research groups independently.
Jukka Siren

I have a background in statistics, and my main research interest has been statistical modeling of complex phenomena. I have mostly worked with statistical applications arising from the biological sciences, with the focus shifting from population genetics and evolutionary biology to ecology. During my PhD, I studied the evolution of genetic population structure based on allele frequency change ("population phylogenetics"). After that my main research direction switched to simulation-based inference methods (e.g. approximate Bayesian computation, ABC) with a special emphasis on generating predictions from individual based models in population ecology.  In addition, I have worked on various other applied projects from many fields ranging from criminal psychology to computational linguistics. I have wide experience in different areas of statistics including theory, experimental design, exploratory analyses, computational statistics, inference and software development. I prefer to take a Bayesian approach to statistics, which allows us to coherently take into account all uncertainty and provides the regularization necessary for inference with complex models. Currently, in the Biodata Analytics Unit, I am continuing with ABC-based prediction research, as well as working in several applied projects using more standard statistical methods such as GLMMs.

Fatemeh Seyednasrollah

I am a bioinformatician/computational biologist with broad knowledge in molecular and clinical data analysis. At the molecular level, I have extensive experience in transcriptomics data analysis and methods development. So far, the main focus of my projects has been on developing computational and meta-analysis techniques for detecting and characterizing molecular and clinical markers playing a role in the diagnosis and prognosis of complex diseases such as cancer. In particular, I have developed various types of clinical predictive models (e.g., survival models) using statistical and machine learning approaches. Currently, I am working on wide range of transcriptomics projects in the Biodata Analytics Unit.

Pasi Rastas

I have a computer science background and in-depth expertise in theoretical and practical computation. I have been working on computational problems and software development in biology and bioinformatics since before my MSc (2005) and PhD (2009). After my PhD, I started pursuing linkage mapping and this has since become my main research direction. I have published and developed the popular software suite Lep-MAP (versions 1, 2 and 3) for linkage mapping and Lep-Anchor for linkage map guided genome anchoring. I have been developing new software and working on genome assemblies, genomics and population genetics for many groups, projects and non-model species. I have continued my research in the Biodata Analytics Unit, helping and providing expertise to many research groups at the Viikki campus on genomic and other studies.