The research group BIAS (Bioinformatics and Applied Statistics) conducts research in the exciting and rapidly evolving scientific borderland between bioinformatics and applied statistics.
At BIAS, we can offer an inclusive and enthusiastic work environment. Although the BIAS members have diverse scientific interests, we engage in discussions about each other’s projects. As a master student in our group, you will take part in ongoing activities as a regular BIAS member.
In the following overview, you can find a short introduction to the scientific interests of each of the BIAS members. The master projects we offer reflect our research interests.
Thore Egeland: Biostatistics – genetics (BI+AS)
My main focus is on forensic genetics, i.e., applications of genetics in legal settings. Projects typically relate to the freely available software Familias: A program for probability calculations for relationship inference based on DNA data. However, some projects are also relevant for genetics more generally like estimation of mutation rates. Read more here.
Kathrine Frey Frøslie: Applied statistics (AS)
How do people move to music, and how do we digest our food? Statistics can be used to answer both questions, as well as questions in the research fields of teaching, flu epidemics and official statistics. Available master projects include collaborations with The National institute for Public Health (flu projects) and Statistics Norway. Read more here.
Lars Snipen: Microbial genomics (BI+AS)
In microbial genomics we study the DNA of the microbes (bacteria). Many applications make use of this in one way or another, e.g. the studies of human gut microbiomes and its influence on our health or understanding the mechanisms behind antibiotic resistance. Such studies usually mean sequence analysis and High Performance Computing. Most projects are in collaboration with microbiologists. Read more here.
Ove Øyås: Genome-scale metabolic models (BI+AS)
How does a salmon and the microbes in its gut convert nutrients from the feed into the fillet that we eat? To answer such questions, we use mathematical models to study the metabolic networks of enzymatic reactions that are encoded in genomes. Master projects are available within three research areas: feed prediction for salmon, integration of models and omics data, and development of scalable modeling tools. All projects aim to answer interesting biological questions by combining models and data and require basic knowledge of biochemistry, programming, and linear algebra. Read more here.
Torgeir R. Hvidsten: Bioinformatics – gene regulation (BI)
What defines a species? What makes a tree a tree, and not an annual flower? Two decades of genomics have taught us that the answer is not primarily that different species have different genes, but that they regulate the same genes differently. We utilize large genomics datasets to model how genes interact in regulatory networks and ask how traits characteristic to individuals and species emerge through perturbations or evolution of such networks. We collaborate with biologists on projects studying wood formation in trees, regulatory evolution after whole genome duplication in salmonids and host-microbiota systems in salmon and cow. Read more here.
Jon Olav Vik: Biostatistics and systems biology (BI+AS)
I am passionate about using mathematics to integrate biological knowledge and data, identifying patterns and contrasts, making comparisons and making real-world interpretations of the results. The production biology of salmon is my current focus, though I also co-supervise on medical genetics, studying the clinical importance of regulatory variation in DNA and micro-RNA. I believe in combining approaches ranging from broad statistical descriptions to more mechanistic mathematical descriptions of physiology. The value of modeling is purposeful simplification of reality, generalizing insights from the model to a broader class of systems. Read more here.
Hilde Vinje: Biostatistics (BI+AS)
My focus is on applied statistics in Life Sciences. One of the main interests is how we can teach statistics to Life Sciences students so that they get the most out of the courses. Due to collaboration with both Animalia and Nortura, there have been, and are, opportunities for master's projects within livestock data, but with a large emphasis on statistical methodology. Collaboration with other disciplines are possible and the master projects may therefore vary depending on the student's interest. Read more here.
Lars Erik Gangsei: Applied statistics in agriculture (AS)
In my work I use traditional, i.e. not machine learning, methods within livestock production. My main employer is the Norwegian Meat and Poultry Research Centre (Animalia). I apply statistical methods to different fields like image analysis of CT-scanned pig boars and objective carcass grading within the meat industry. I have also worked with population dynamics for moose and deer in Norway. Read more here.
Aliaksandr Hubin: Bayesian statistics and machine learning (AS)
My research interests are focused on Bayesian model selection, averaging, and automatic model configuration in complex regression contexts ranging from simple linear models to highly nonlinear Bayesian regressions and Neural Networks. In general, Bayesian statistics are both applied (survey data, epigenetic data, QTL studies) and methodological (MCMC, Variational inference, EM-like algorithms) I am also interested in Weak Supervision in Machine Learining through mixture modeling. Read more here.