Welcome

 

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

Welcome

 

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.

Research

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. During and after my PhD studies, I have spent time as a visiting academic at the University of New South Wales,  Australia and have been a PostDoc at the division of Signals and Systems at Uppsala University, the division of Statistics and Machine Learning at Linköping University as well as at the School of Engineering at the University of Newcastle, Australia.I have also worked as a Research Scientist at Sectra AB.

Today

I am currently an entrepreneur with my own consultancy in Artificial Intelligence (AI), Machine learning (ML) and Data Science as well as co-founder in multiple startups aiming to spread AI knowledge to companies and to revolutionize agriculture with AI.

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.

Research

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. During and after my PhD studies, I have spent time as a visiting academic at the University of New South Wales,  Australia and have been a PostDoc at the division of Signals and Systems at Uppsala University, the division of Statistics and Machine Learning at Linköping University as well as at the School of Engineering at the University of Newcastle, Australia.I have also worked as a Research Scientist at Sectra AB.

Today

I am currently an entrepreneur with my own consultancy in Artificial Intelligence (AI), Machine learning (ML) and Data Science as well as co-founder in multiple startups aiming to spread AI knowledge to companies and to revolutionize agriculture with AI.

  • 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.

Short CV summary

Objectives

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.
 

Characteristics

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.
 

Short CV summary

Objectives

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.
 

Characteristics

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.
 

My companies

I am currently an entrepreneur working to spread knowledge about AI and Data Science to companies and organizations.

Kotte Consulting AB

My consulting company Kotte Consulting AB offers both help with developing new AI/ML solutions and education in these fields.

Teambuild on Tech

Teambuild on Tech help businesses to adopt a data-driven mindset and to help develop the organisation and people to make efficient use of AI and at the same time earn/save money.

My companies

I am currently an entrepreneur working to spread knowledge about AI and Data Science to companies and organizations.

My consulting company Kotte Consulting AB offers both help with developing new AI/ML solutions and education in these fields.

Teambuild on Tech helps businesses to adopt a data-driven mindset and to help develop the organisation and people to make efficient use of AI and at the same time earn/save money.

vinnova whiteboard2 scaled

Some of my past work projects

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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.

rubble

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.

ehr

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.

My research

My research is situated somewhere between Computational statistics and Machine learning. Three common themes are:

  • Bayesian inference: using prior information and quantifying uncertainty,
  • Dynamical systems: the behaviour changes over time,
  • 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 themes and methods is found in the introductory chapter of my PhD thesis.

My research

My research is situated somewhere between Computational statistics and Machine learning. Three common themes are:

  • Bayesian inference: using prior information and quantifying uncertainty,
  • Dynamical systems: the behaviour changes over time,
  • 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 themes and methods is found in the introductory chapter of my PhD thesis.

Some of my 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.

Self-improvement

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.

Photography

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.