Rebecka Jörnsten             

Professor of Biostatistics and Applied Statistics


Division of Applied Mathematics and Statistics

Mathematical Sciences
University of Gothenburg/ Chalmers

Vice-Dean for Research and Research Infrastructures
Faculty of Science, University of Gothenburg



Main menu: Overview | | List of Current Projects | | Publications | | Teaching | | People

What's new?

TBA: PhD position, start date around February 2020.

Overview

My research centers on the development of new statistical methodology for network modeling, clustering and model selection, with applications to high-dimensional biological data.

My group consists of 4 PhD students who will soon be joined by 1 or 2 more. We work on projects ranging from multi-group and multi-view data integration, multilayer network modeling, interpretable deep neural networks and analysis challenges in systems biology. For more details, please check the List of Current Projects below - we are always looking for master students who want to get involved in research projects.

Data integration and joint modeling are a rich source for research problems. These types of problems are central components in several joint projects now underway in collaboration with Sven Nelander's group. Our group aims to formulate integrated models for mRNA, microRNA, DNA copy number, methylation and mutation in human cancer. These projects are supported by two grants from the Swedish Foundation for Strategic Research

I also collaborate with Claes Strannegård and Johan Jonasson on robust and explainable neural networks. Claes and I have an Chalmers ICT seedgrant to support this research. Johan and I develop new regularization techniques for deep neural networks to better handle the presence of mislabeled data. This project is supported by the Wallenberg foundation

I have started a new project with Balazs Kulcsar at Electrical Engineering, building on a recent master thesis project, merging control theory and neural networks. The company, Centiro, where the master thesis projects where conducted, will fund two industry PhD positions to continue this line of research.

We have also recently begun collaborating with Jens Nielsen's group at the Systems Biology department at Chalmers, trying to better quantify biological and technical variation in scRNA data.

Since 2018, I am the Vice-Dean for Research and Research Infrastructures at Faculty of Science , University of Gothenburg. I chair the Faculty Board's advisory committee, where we discuss interdepartmental activities, review sabbatical applications and Faculty prizes and awards. We also organize faculty events. Together with CHAIR - Chalmers AI Research Center we recently organized a 3 day event on AI4Health and Healthy AI, aimed at increased coordination of AI-related activities at GU, Chalmers and Sahlgrenska University hospital.

List of Current Projects

We are always looking for master students who want to be a part of our research team. Please contact me if any of the projects sound interesting to you. You are also welcome to contact me with your own project idea. I usually supervise 5-7 master students each year.

AI, Deep Learning

  • Neural Networks and Control Theory: Last year, Viktor Andersson and Andreas Syren wrote a master thesis project on this topic. The project was such a success that Centiro , the company where Viktor and Andreas worked, will fund two industry PhD positions building on this. We are also looking to expand this group with several master thesis students. Please contact me or Professor Balazs Kulcsar at Electrical Engineering if you are interested.
  • AI-Integromics: Oskar Liew and Per Ed will work with us on developing data integration methods for systems biology using deep neural networks paired with structured regularization. We have several related master thesis projects on AI for systems biology.
  • Explainable Neural Networks: This is a project in collaboration with Claes Strannegård and Johan Jonassaon. We are trying to open the black-box of deep learning by investigating different ways of building the network architecture or utilizing different loss functions.
  • Machine Learning, Systems Biology

  • Multi-response Random Forests: Last year Mikael Böörs wrote an excellent thesis on this topic that we are now working on turning into a paper. We want to extend this method to much more complicated data distributions and also look into scalability. There are many applications in systems biology for this problem.
  • Flexible Joint Matrix Factorization: Jacob Lindbäck worked with us on this problem last year. My PhD student Felix Held is working on extensions that we are turning into a paper. There are many open problems one can pursue here; scalability, applications, generalizations.
  • Multi-level network models: My PhD student Jonatan Kallus is working on network estimation methods that can handle complex data structures. We want to extend these to single-cell data where we have a lot of missing values.
  • Teaching

    Teaching Philosophy

    My classes are usually made up of a mix of students; undergraduates, master students and PhD students and all from different fields of study. I enjoy this kind of dynamic classroom.
    I tend to mix black-board lectures with computer demonstrations for all my classes. My goal in teaching is that the students leave my class recognizing that statistical modeling is not a "push-the-button" type exercise, and every data set requires unique consideration.



    Courses & Workshops

    In past years I have cycled teaching applied statistics couses (Linear models, Applied multivariate analysis) and method courses (Statistical inference principles and Survival analysis).
    My main focus for teaching now is Statistical Learning for Big Data and master thesis projects.
    Felix Held and I are also preparing a Chalmers Professional course "Big Data - Small Data" scheduled for summer 2019. Here are some links to recent courses:


    People

      Alums
    • Jose Sanchez, 2009-2014 ​Network models with applications to genomic data: generalization, validation and uncertainty assessment Currently working as an analyst at Astra Zeneca
    • Alexandra Jauhiainen 2009-2010 Statistics in Gene Expression, Metabolomics, and Comparative Genomics in Evolution (co-supervisor). Currently working as principal statistician at Astra Zeneca
      Current Master students
    • Markus Kjällman, Jerry Liu, Martin Eriksson, Navid Haddad, Lukas Nyström, Oskar Liew, Per Ed.
      Alums
    • 2019: Jacob Söderström, Isak Hjortgren, Sandra Larsson, Jacob Lindbäck, Mikael Böörs, Elijah Ferreira, Arash Shahsavari, Sandra Vikander, Linn Engström, Andreas Syren, Viktor Andersson, Natalja Vessilinova, Elijah Ferrera, Wade Rosco
    • 2018: Olof Ekborg, Philip Arndt, Fredrik Beiron, Johan Björk, Adam Brinkman, Fredrik Kjernald, Karl Svensson.
    • 2017: Andrew Nisbet, Leif Schelin, Alex Bergkvist, Sebastian Franzen, Filip Birve
    • 2016: Björn Hedner, Nils Wireklint, Emilio Jorge, Pasha Hashemi, Ludvig Vikström, Oskar Lilja, Maja Fahlen
    • 2015: Sofia Hjalmarsson, Sebastian Anerud, Susanne Pettersson, Linus Lundin
    • 2002-2014: Johanna Svensson, Patrik Johansson, Viktor Skokic, Johanna Sigmundsdottir, Tobias Abenius, Eric Burlow, Owen Martin, Diane Richardson