Johan Dahlin Passionate about finding hidden patterns and trends in data.

  • 2016

    PhD Automatic Control

    Linköping University, Sweden.
    Includes two years of coursework in Engineering, Machine Learning and Statistics.

  • 2011

    MSc Engineering Physics

    Umeå University, Sweden.
    Majors in Risk Management and Industrial Statistics.

  • 2011

    B.Sc. Economics

    Umeå University, Sweden.
    Includes coursework in Marketing, Project Management and Law.

Who are Johan Dahlin?

I began life in Enköping, Sweden in 1986. As a young boy, I quickly found an interest in Mathematics and Computer Science, which led me to study Engineering and then to pursue a PhD based on research and coursework. 

I received my PhD in Automatic Control in May 2016 after successfully defending my thesis which contained a total of 17 peer-reviewed papers (13 conference papers and 3 journal papers) published at top conferences and journals in the fields of Computational Statistics and System Identification.

Currently, I am developing automated data-driven algorithms for building dynamical models as a PostDoc at the School of Engineering at the University of Newcastle, Australia.

During my research career, I have worked at a number of different companies and universities. I visited Prof. Robert Kohn at he University of New South Wales,  Australia during the autumn of 2014 as part of his PhD studies. I have also worked as a Research Scientist at Sectra AB and as a PostDoc at the division of Statistics and Machine Learning at Linköping University.

What are my objectives and passions?

The Economist has described data as the new oil which can result in the next industrial revolution. Everyday, more and more information is gathered from sensors and the Internet. As a result, Big Data, Statistics and Machine Learning (in short Data Analytics) have become essential tools to generate insights from this data.

 

Objectives in Data Science

  • To condense data into a model.
  • To gain understanding, to make decisions or to forecast future behaviours using this model.
  • However, many models and the algorithms that fit them to data cannot yet cope with the complex, dirty and large data materials
  • I work on how to build algorithms that better scale with the amount of information and to accelerate current methods for model building. 

My Passion

  • Bayesian data analysis employed to finding hidden patterns and trends in data.
  • I work with numbers, images, text documents and other forms of data.
  • I want to make use of my expertise to improve the world in terms of health-care/medicine, economics and the environment.
  • I believe that learning from data is essential in this quest by enabling improved data-driven decision making and forecasting.

What kind of research interests me?

My interests in research are rather broad and spans employing methods from Statistics, Machine Learning, Artificial Intelligence, Engineering, etc. to solve real-world problems. You can see some examples of practical applications of my research on the next page and here I discuss some more academic interests.

Accelerating Monte Carlo algorithms for Bayesian inference, such as particle filtering and Markov chain Monte Carlo.

Efficient inference in longitudinal data, i.e., short data records for many individuals.

Approximate Bayesian inference by modifying the model to simplify inference, e.g., using Gaussian process optimisation.

Developing methods for applying Machine/Deep Learning for applications in Climate Science, Medicine and Finance.

What can my research be used for?

Would you like to know more?

I love to connect with new people for networking and to share experiences and idea. Therefore, do not hesitate to get in touch with me if you have any questions connected to my research, the source code on GitHub or if you would like to discuss a business idea or hire me for a job.