Photo of Daniel Jönsson

Daniel Jönsson

Associate Professor, Head of Unit

Working in the intersection between visualization and machine learning.

Making complex information understandable

Deciphering complex datasets is no easy task. Raw numbers and abstract statistics often fail to capture the essence of data, hindering human comprehension. My work aims to bridge this gap between humans and data through the power of visualization and machine learning. By combining these two fields, I try to find ways of unraveling hidden patterns, extracting valuable insights, and enabling data-driven decision-making.

The heart of my work lies in developing methods for transforming complex, high-dimensional data into intuitive visual representations. In essence, I develop visualizations that breathe life into the data and allow individuals to grasp its intricacies effortlessly. Brain imaging.Visualization of time-varying brain imaging data. I often work in cross-disciplinary teams together with experts in fields such as neuro science, radiology, or artificial intelligence (AI), to research novel visual representations and interaction techniques. I am one of the core developers of the free rapid visualization prototyping software Inviwo [D. Jönsson, IEEE TVCG, 2019], which is used by several research groups and companies.  

The picture shows visualization of time-varying brain imaging data [D. Jönsson, IEEE TVCG, 2016]. Received an honorable mention for best paper at the prestigious IEEE VIS conference.

Daniel Jönsson.Daniel Jönsson at a workshop about education at MIT 2018. Photo credit Thor Balkhed My expertise extends beyond visualization alone. My research involves both methods for interpreting machine learning algorithms as well as making use of them to process and analyze vast amounts of data. By seamlessly integrating visualization and machine learning, I provide a comprehensive toolkit that empowers humans to extract knowledge and gain insights from large complex data.  




Alex Knutsson, Jakob Unnebäck, Daniel Jönsson, Gabriel Eilertsen (2023) CDF-Based Importance Sampling and Visualization for Neural Network Training Eurographics Workshop on Visual Computing for Biology and Medicine Continue to DOI


Farhan Rasheed, Daniel Jönsson, Emma Nilsson, Talha Bin Masood, Ingrid Hotz (2022) Subject-Specific Brain Activity Analysis in fMRI Data Using Merge Trees 2022 IEEE WORKSHOP ON TOPOLOGICAL DATA ANALYSIS AND VISUALIZATION (TOPOINVIS 2022), p. 113-123 Continue to DOI
A. Baeuerle, C. van Onzenoodt, S. der Kinderen, Jimmy Johansson Westberg, Daniel Jönsson, T. Ropinski (2022) Where did my Lines go? Visualizing Missing Data in Parallel Coordinates Computer graphics forum (Print), Vol. 41, p. 235-246 Continue to DOI
Daniel Jönsson, Joel Kronander, Jonas Unger, Thomas Schön, Magnus Wrenninge (2022) Direct Transmittance Estimation in Heterogeneous Participating Media Using Approximated Taylor Expansions IEEE Transactions on Visualization and Computer Graphics, Vol. 28, p. 2602-2614 Continue to DOI


Anders Ynnerman, Patric Ljung, Anders Persson, Daniel Jönsson (2021) Multi-Touch Surfaces and Patient-Specific Data Digital Anatomy: Applications of Virtual, Mixed and Augmented Reality, p. 223-242 Continue to DOI