Welcome

Scroll down to read more.

Photo: Andreas Svensson

I’m passionate about finding hidden patterns and trends in data.

Biography

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.

  • Ph.D. May, 2016

    Ph.D. in Automatic Control

    Linköping University, Sweden

  • M.Sc. July, 2011

    M.Sc. in Engineering Physics

    Umeå University, Sweden

  • B.Sc. June, 2011

    B.Sc. in Economics

    Umeå University, Sweden

Accelerating Monte Carlo methods for Bayesian inference in dynamical models

Thesis
J. Dahlin
Linköping Studies in Science and Technology. Dissertations. No. 1754, 2016.
Publication year: 2016

Objectives

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.

The objective in these fields is often to condense the data into a model, which can be used to gain understanding, to make decisions or to forecast future behaviours. However, many models and the algorithms that fit them to data cannot yet cope with the complex, dirty and large data materials. More work is required to better scale with the amount of information and to accelerate current methods for model building.

I am passionate about finding hidden patterns and trends in data such as numbers, images and documents to learn more about medicine, economics and society at large. During my PhD studies, I spent five years learning and developing more efficient algorithms for Data Analytics.

I now aim to make use of my expertise to improve the world in terms of health care, medicine and the environment. I believe that learning from data is essential in this quest by enabling improved data-driven decision making and forecasting.

Research Interests

Finding hidden patterns and trends in data.

Accelerating Monte Carlo algorithms for Bayesian inference.

Modelling and prediction with short data records for many individuals.

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

Some projects that I have been working on

  • Automatic localisation of cancer tumours

    Applying recent advanced in deep learning to classify mammography images.

  • Accelerating rendering of computer generated imagery in movies

    Developing sequential methods the updates the solution to the direct illumination problem from one frame to another.

  • Better estimates of risk in financial portfolios

    Developing inference methods that better takes into account jumps in asset prices when estimating Value-At-Risk for portfolios of assets.

  • Adaptive methods for computational Bayesian inference

    Developed robust and fast methods based on Markov chain Monte Carlo for estimating the posterior distribution in dynamical models.

  • Detecting hidden groups in social networks

    Developed methods for finding communities in social networks constructed using uncertain information.

Connect with me

I like to connect with new people and to share my experience and knowledge with others. So do not hesitate to get in touch with me if you have any questions connected to my research, the source code on GitHub or anything else.

The simplest way to reach me is to make use of the form to the right. Please, enter your e-mail so that I can get back to you.