Centre for Data Intensive Sciences and Applications

Welcome to the March PhD-seminar 2023


When? Friday March 10th 14.00-15.00
Where? Via zoom
Registration? No registration needed since the seminar is only online this time, if you have not received the link please contact Diana Unander diana.unander@lnu.se

This time we will only have one presentation and the seminar will be fully online since the presenter is located abroad.

14.00 – 14.10 Welcome and practical information from Welf Löwe
14.10 – 14.55 Presentation and discussion: Using multiple embeddings for visual analytics – Daniel Witschard  (ISOVIS)
14.55 – 15.00 Wrap up – information about the next PhD-seminar


Using multiple embeddings for visual analytics – Daniel Witschard  (ISOVIS)

Embeddings are numeric vector representations of complex or unstructured data. The main goal of embedding algorithms is usually to produce embeddings where items that are similar in the original data set are embedded into vectors that lie close to each other in the embedding space. This makes embeddings highly suitable as input for computational analysis tasks such as clustering, classification, and similarity calculations since it is often more straightforward to perform these calculations on numeric vectors than rather than on the underlying data. For some data types, such as graphs/networks and words/text, there exist several different algorithms (each with its specific characteristics and tradeoffs) and therefore choosing the best embedding technology for a given application is an important and often non-trivial task. However, searching for single candidates is not the only strategy that could be used–and therefore this presentation will contain examples and results (some published, and some work-in-progress) aiming to answer the research question “Is it possible to combine several different embeddings to obtain even better results and visualizations?

More information about Daniels research project https://lnu.se/en/research/research-projects/doctoral-project-multivariate-network-embedding-for-visual-analytics/ 


Welcome to our first PhD-seminar November 4th


  • When? November 4th 14-16
  • Where? D1140 – Växjö (link will provided for those who wants to attend online)
  • Registration: We would like to know how many that will attend onsite/online in order to get some fika for those onsite. So please register by November 2nd https://forms.gle/ZwwgoQ4JK4e41BBR6


14.00-14.10     Welcome and practical information from Welf Löwe

14.10-14.55     Presentation and discussion: Visual Analytics for Explainable Machine Learning in a Nutshell – Angelos Chatzimparmpas

14.55 – 15.05  Coffee break

15.05 – 15.50  Presentation and discussion: Getting the most out of health data, combing the best of two worlds – Olle Björneld

15.50 -16.00    Sum up and plan for our next seminar on January 13th


Visual Analytics for Explainable Machine Learning in a Nutshell – Angelos Chatzimparmpas

Machine learning (ML) research has recently gained much attention, with various models proposed to understand and predict patterns and trends in data originating from various domains. Unfortunately, users find it harder to evaluate and trust the results of these models as they become more complex because most of their internal workings are kept in secret black boxes.

One possible solution to this problem is the explanation of ML models with visual analytics (VA) since it enables human experts to analyze large and complex information spaces such as data and model spaces. By doing so, evidence has shown an improvement in predictions and an increase in the reliability of the results.

This talk aims to provide an overview of the state-of-the-art in explainable and trustworthy ML with the use of visualizations, as well as the development of VA systems for each stage of a typical ML pipeline. Furthermore, we will briefly introduce some of these tools and discuss how such VA techniques can help us not only understand ML models but also do this in a human-centered and steerable way.

Getting the most out of health data, combing the best of two worlds – Olle Björneld

Machine learning driven knowledge discovery on real world data based on domain knowledge. Real world data does not comply with machine learning models very well and prediction models perform suboptimal if pre-processing of data is deficient.

Based on experience from medical registry studies using electronic health data (EHR) performed in collaboration with domain experts, data analyst and statistician an automatic feature engineering framework and method have been developed. The framework is called automatic Knowledge Driven Feature Engineering (aKDFE) and have been evaluated by machine learning pipeline.

Experiment shows that prediction models performs better if aKDFE is used without losing explainability, but more experiments need to be performed in other domains to fully quantify the results. The key aspect is how to concentrate and mine inherent knowledge in transaction data to optimal machine learning driven prediction models.

A warm welcome,

Welf & Diana

KvarkenSat Innovation Challenge 2022 on Sustainable Forestry


The DISA forestry group invites you to KvarkenSat Innovation Challenge 2022 on Sustainable Forestry that will start in next week with a pre-hackathon followed by the hackathon using space-based data helping to combat climate change!

Acceptance based on submission, the best submissions may be approved early.

The challenges

Climate change brings about major changes affecting us all. Extreme weather events become more frequent and especially the amount of rainfall increases in Northern Europe, one contributor being the warmer winters. New species of both vegetation and animals enter new areas while the existing species might have even major changes in their habitats. These lead to new challenges in the forestry industry. We are looking for ideas and solutions combining existing knowledge and datasets with space-based data and datasets based on satellite measurements, in four particular themes including soil moisture, spruce bark beetles, forest ground damage and the forest value chain.

Who can apply?

The hackathon is open to students, teachers, researchers and start-ups in teams of 3-5 persons. Relevant expertise to participate include: space and satellite data, machine learning and neural networks, computer science, positioning systems, automation, image processing/recognition, engineering, logistics, business/communications and forestry.


The three best proposals across all of the themes will be awarded a cash prize (over 100 000SEK) and possible continuation/acceleration within start-ups and innovation programs.

Pre-Hack Webinar

To get familiar with the hackathon, meet the mentors and partners, and participate in Q&A-session join our webinar on 15 February at 13.00 (14.00 Finnish time).

Link to the join the webinar: https://bit.ly/KvarkenSatWebinar

More information about the hackathon: https://ultrahack.org/kvarkensat-innovation-challenge-2022

DISA Seminar November 1st on Visualization Perspectives in Explainable AI


  • When? November 1st, 2021 at 12-13
  • Where? Online, links will be sent to those registered
  • Registration via this link

This talk with Professor Andreas Kerren, will overview interactive data visualization research with a focus on the development and use of visualization techniques for explainable artificial intelligence. The field of Information Visualization (InfoVis) uses interactive visualization techniques to help people understand and analyze data. It centers on abstract data without spatial correspondences; that is, usually it is not possible to map this information directly to the physical world. This data is typically inherently discrete. The related field of Visual Analytics (VA) focuses on the analytical reasoning of typically large and complex (often heterogeneous) data sets and combines techniques from interactive visualizations with computational analysis methods. I will show how these two fields belong together and highlight their potential to efficiently analyze data and Machine Learning (ML) models with diverse applications in the context of data-intensive sciences. As ML models are considered as complex and their internal operations are mostly hidden in black boxes, it becomes difficult for model developers but also for analysts to assess and trust their results. Moreover, choosing appropriate ML algorithms or setting hyperparameters are further challenges where the human in the loop is necessary. I will exemplify solutions of some of these challenges with the help of a selection of visualization showcases recently developed by my research groups. These visual analytics examples range from the visual exploration of the most performant and most diverse models for the creation of stacking ensembles (i.e., multiple classifier systems) to ideas of making the black boxes of complex dimensionality reduction techniques more transparent in order to increase the trust into their results.

Did you miss it? If so you can watch it here: https://play.lnu.se/media/t/0_hghpwmkw

information visualization, visual analytics, explainable AI, interaction, machine learning models, trust, explorative analysis, dimensionality reduction, high-dimensional data analysis

Further reading:


Research seminar (PhD) – Nico Reski, Wed18Sep, 16:00, D1136V


Nico Reski will present the progress of his doctoral studies in Computer and Information Science so far, and the work we have been conducting at the VRxAR Labs research group. This involves topics such as virtual reality, 3D user interfaces, immersive analytics, and computer-supported cooperative work. During the second part of the seminar I am looking forward to have a discussion with the audience regarding feedback and future directions of this work.

A warm welcome, no registration in advance is needed


Second Keynote speaker at VINCI 2018 is Keynote Speech 2: Design after Nature Prof. Jon McCormack, Monash University, Australia


We have the pleasure to present the second keynote speaker at VINCI 2018 Prof. Jon McCormack, Monash University, Australia on Design after Nature. You can find information on how to register for VINCI 2018 here

Abstract: Nature has driven us in what and how we create for millennia. Biomimetic approaches to human design are inspired by natural forms, shapes and processes. In computing, nature-inspired algorithms mimic collective behaviour or biological evolution to solve hard problems in search, optimisation and learning. In this talk I’ll show how I have developed a creative visual design practice informed by processes from biological development, the architecture of natural form, and evolutionary processes. My work began by devising advanced visual models of morphogenetic development in plants. Incorporating evolutionary processes allowed designs to emerge that would be difficult or impossible to discover independently, making them “beyond human design”. In later work, I have experimented with evolutionary ecosystems and processes such as niche construction to encourage diversity in the visual style of works generated by algorithmic processes. My most recent work looks at translating from the virtual back to the real, using digital fabrication technologies driven by generative computational processes. The goal is to build dynamic, responsive, intelligent physical systems that interact directly with living organisms, symbiotically affecting their growth and development. This leads to the creation of bio-machine hybrids – bringing the biomimetic concept full circle – and heralding a new form of co-design where human, machine and nature all contribute to the design process.

Photo of Jon McCormack

Short Bio: Jon McCormack is an Australian-based artist and researcher in computing. He holds an Honours degree in Applied Mathematics and Computer Science from Monash University, a Graduate Diploma of Art (Film and Television) from Swinburne University and a PhD in Computer Science from Monash University. He is currently full Professor of Computer Science and director of sensiLab at Monash University in Melbourne, Australia. His research interests include generative art, design and music, evolutionary systems, computer creativity, visualisation, virtual reality, interaction design, physical computing, machine learning, L-systems and developmental models.

Since the late 1980s McCormack has worked with computer code as a medium for creative expression. Inspired by the complexity and wonder of a diminishing natural world, his work is concerned with electronic “after natures” – alternate forms of artificial life that may one day replace the biological nature lost through human progress and development. For more information about Jon Cormack see.