Business-minded PhD passionate about data, algorithms and improving the world.

  • 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

    BSc Economics

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

Who am I?

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 degree 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 field.

I have recently returned back to Sweden after a PostDoc in Australia and am looking for new challenges in industry (employment or consulting projects).

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 as well as at the School of Engineering at the University of Newcastle, Australia.

Short CV summary

I am passionate about finding hidden patterns and trends which can be used in a meaningful way. For the last seven years, I learned about and developed new algorithms for analysing data and for making predictions. I now want to employ these skills in industry to improve companies and to create customer value.
My colleagues know me as a resourceful, outgoing and communicative co-worker who delivers good solutions on time. I believe that great things are done together. Therefore, I love working in cross-functional teams to solve challenging problems that makes a difference. I bring positive energy and momentum into any project and workplace. I have been described as ambitious, organised, a quick learner and a highly valued colleague with very good social skills.

Summary of my research

My research is situated somewhere in the intersection of Computational statistics and Machine learning. Three common themes are Bayesian inference (using prior information and quantifying uncertainty), dynamical systems (the behaviour changes over time) and the acceleration of methods (decreasing the computational time to get the answer). I usually work with methods such as Monte Carlo methods (particle filtering, sequential Monte Carlo and Markov chain Monte Carlo), Gaussian/Dirichlet processes and mixture models.

A quite general introduction to these fields and problems with some common applications are found in the introductory chapter of my PhD thesis. More information about my research is found on the pages liked below.

Some of my skills

Data science & Machine learning

Extensive experience in exploratory data analysis and modelling many different types of data. Experience in applying, developing and implementing algorithms from Machine learning including deep learning and reinforcement learning.


Full-stack software development using Python/C /R for computational back-end in the cloud and React for front-end on web or mobile devices.

Problem solving

A PhD education teaches you to quickly learn new things and solving problems such as tailoring/improving algorithms for a new application.

Collaboration and communication

Extensive experience at leading and working in teams in developing technical solutions as well as in sports and other organizations. Extensive experience in teaching, presentation and communications.

Some of my past projects


Teaching computers to detect cancer

Breast cancer is one of the most common forms of cancers in women with about two million new cases every year. Early detection is the best approach to improve survival. The successful application of deep learning for processing mammograms would enable more extensive screenings of the population. Such algorithms would help by sifting through the gigabytes of data collected to flag abnormalities that require further analysis by an expert.

Photo: Nevit Dilmen.


Finding survivors in disaster areas

It is often difficult to coordinate and plan the rescue work after a disaster such as an earthquake. Statistical models can be a help in these situations by combining information from UAVs, maps, cell towers, etc. to estimate the distribution of survivors in an area. This model can then be used together with methods from reinforcement learning to coordinate and lead the work of the the rescuers to find as many survivors as possible after a disaster.

Photo: Mark Dixon.


Finding alternative medical diagnoses

Electronic health records of patients are usually stored on a central server at hospitals. These are a possible gold-mine of information that is currently large underutilised. However, Machine learning and Natural language processing methods can be used to extract useful information from these records. This can help medical doctors to make better diagnoses by presenting lists of possible diagnoses given certain test results and symptoms.

Photo: NEC Corporation of America.


Would you like help with exploring and analyzing a data set? Would you like to have tailored efficient algorithms for your problem? Would you like to have help with training and education? I can help!

I have many years of practical experience in developing and applying methods from Bayesian statistics, Machine learning and Artificial intelligence both in academia and industry. My PhD education has provided me with a deep understanding of these methods, which is essential when tailoring them to specific problem or training others. I have experience in working with numerical, text, image and network data. Get in touch for more information.

Some current hobby projects

I like to keep busy during my spare time with being outdoors, reading books, running, swimming, weight-lifting, yoga, cooking and socializing. Each year, I decide on some special focus areas and the current are:

Swedish circuit

Preparing for 90 km cross-country skiing, 300 km biking on country roads, 30 km running in hilly terrain and 3 km open-water swimming.


Focusing on meditation, Stoicism and Toastmasters International.

Playing the guitar

Following the awesome free courses offered by Justin Guitar to learn to play some Foo Fighters, Coldplay and Death Cab for Cutie.


Focusing on landscapes and streets using my Nikon D7200. Have a look.

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.