A list of presentations, lectures, and workshops that I have given. Slides and source code for some of them can be found in this repository. Others are available on request.
ISP ComPros Research Group Meeting at UiO, April 6th, 2021
đŒ speaker notes / handout on Hack.md.
A 90 minutes long hands-on introduction to R and why you should or should not learn it.
- 90 minutes - brief introduction to R
- Provide motivation for learning R (or for not learning R)
- Learn 10 cool things about R
- Hands-on mini data analysis demo to demonstrate the power of R
- Is R for you?
- Basic data wrangling, modeling, data visualization
- How to continue learning?
Nordic RSE get together, December 2nd 2020, online (link to event)
đŒ Slides and material on Github
This talk is about how writing an R package has helped me leave my comfort zone and level up my R programming skills. The code I write as a researcher is mostly single-user and single-use. Writing and publishing code meant for others has helped me break old habits and get a better grasp of software engineering. R has a very streamlined ecosystem for package development that makes understanding and following best practices easy. I will talk about the things I have learned, why I think writing a package should be a rite of passage for any aspiring research software engineer, and why R is a great tool for this.
9 January 2020, Uio Research Bazaar, University of Oslo, Norway (link to event)
Co-hosted with Désirée Treichler
đŒ Slides available on request
In this workshop we will approach data visualization as a design problem, and learn how to solve this problem systematically. You will learn cognitive and design principles that help you understand what works and why, and how to explore different design solutions more efficiently.
Good data visualization will help you tell the story of your research and make your papers and presentations stand out. Yet, most researchers are never really taught how to visualize data. Instead, their design process is based on intuition, copying others, and try-and-error. This is a waste of time and often produces ineffective results.
In this workshop we will approach data visualization as a design problem, and learn how to solve this problem more systematically. You will see why visualization is a powerful way to communicate your data. You will learn design principles that help you understand what works and why, and practice hands-on how to explore design solutions more efficiently.
Participants will:
- Learn about the value of data visualization and when and why to visualize data.
- Learn about the hidden thinking behind good data visualization: understanding the context of the visualization (e.g., audience, presentation format) and how to use editorial thinking to decide what to show.
- Learn how to approach a data set for visualization, e.g., understand different types of data and the implications for visual encoding.
- Learn about different ways to encode data visually, and understand how data can be encoded more effectively by following basic principles of perception and visual design (e.g., signal detection theory, Gestalt laws)
- Understand the different elements of a chart and how to use them to make more effective visualizations (e.g., annotations, color, composition)
We will focus on explanatory data visualizations (e.g., for posters and presentations) and relatively simple types of data that can be found across most disciplines. We will not cover exploratory data analysis, bespoke data visualizations for scientific discovery, domain-specific data types (e.g., text, networks, high-dimensional data), algorithms (e.g., dimensionality reduction, clustering), or advanced visualization techniques (e.g., interaction, animation).
No programming experience is required (and no artistic ability is required, either). However, you should be familiar with fundamental statistical concepts and chart types.
The course is for researchers and graduate students who want to communicate their research more effectively, for example in papers, posters, presentations, or to the general public. Generally, it may be useful to anyone who uses data to inform, support decision making, and motivate change.
- 8 January 2020, UiO Research Bazaar, University of Oslo, Norway (link)
- 15 December 2019, KoLab Hackerspace, Mechelen, Belgium (link)
đŒ The code for this workshop lives in its own repository. The workshop uses this repl.it. There are a handful of slides on Google Docs.
Augmented reality (AR) apps add a virtual layer on top of the real world. This allows you to catch PokĂ©mon in your backyard, but it also has serious potential for hacking your environment: You can use AR to share information where it is most useful, create fantastic experiences that merge the real and the virtual, or simply label your storage cabinets in the nerdiest way possible. Once you know how to augment the real world, the options are endlessâand it is a lot easier than you think! In this workshop we will learn how to place virtual objects and annotations in the real world, so that they can be seen using a smartphone as a 'magic window'. We will learn how to animate things, how to add interactivity, and how to get data into AR. Finally, there will be a mini-hackathon where you use your new skills to build the AR web app of your (humble) dreams. We will build everything with open source web technology. All you need is a computer with a browser. The app will be hosted online and will run in any modern browser. The user will need only a smartphone to use itâno installation required.
- Learn how to use the A-Frame JavaScript framework to create 3D scenes for browser-based VR and AR, how to add and style text, simple 3D shapes, and pre-created 3D models, and how to add animation and interactivity.
- Get a basic understanding of how to bind data to A-Frame objects in order to create data-driven AR experiences.
- Learn how to turn an A-frame scene into an AR app, how to use AR markers to anchor AR objects in the real world, and how to use QR codes to make accessing your app seamless.
At the end of the workshop you will have a functioning AR app that will be hosted on the internet.
This course is for anyone who wants to take their first steps with creating augmented reality experiences. It will be beginner-friendly. Most of the code we write will be relatively simple HTML and JavaScript. You do not need experience with HTML or JavaScript, but some programming experience is highly recommended.
đŒ slides of some lectures can be found here. Others available on request.
Part of MSc-level course in special needs education (SPED4001)
2020, 2021, 2022, University of Oslo
Fall 2021, University of Oslo
Spring 2021, Spring 2022, University of Oslo
Spring 2021, University of Oslo
Part of MSc-level course in special needs education (SPED4001)
18 November 2020, University of Oslo (online)
Fall 2021, University of Oslo (online)
Part of MSc-level course in Audiology
May 2017, KU Leuven
The Carpentries is a non-profit organisation that aims to teach technical skills to researchers through hands-on workshops. For this, teaching material is created and maintained collaboratively by members around the world. I am a certified The Carpentries instructor and lesson maintainer for the material for Data Analysis and Visualization in R for Ecologists and I have been (co-)teaching at the following workshops:
- March 2021, Max DelbrĂŒck Center for Molecular Medicine Berlin (online)
- October 2020, University of Oslo
- January 2020, University of Oslo
- November 2019, University of Oslo
- February 2020 - Programming with Python (Intermediate), University of Oslo
- September 2019 - Plotting and Programming in Python, University of Oslo
đŒ Slides from some presentations available here. Others available on request.
The 500 project â what can we learn from 500 Norwegian cochlear implant users? Oslo, 23 March 2022
Berlin, 24 September 2018
How "Big Data" can support patient counselling - What can we learn from Data Logs?
Antwerp, Belgium, June 2018
Can CI data logs predict children's vocabulary?
Cernobbio, Italy, June 2018
Can data logs predict receptive vocabulary of children with CI?
Louvain-la-Neuve, Belgium, 2017
Linking natural auditory environment and language development in children with cochlear implant.
Leuven, Belgium, 2017
Does the auditory environment influence the language development of children with CI? (poster presentation)
London, UK, 2017
Using automatic data logging to investigate the auditory environment of children with cochlear implant.
Bilbao, Spain, 2017
Does the auditory environment of children with cochlear implant influence their language development? (poster presentation)
Linköping, Sweden, 2017
Does the auditory environment of children with cochlear implant influence their language development? (poster, selected for oral presentation)
Cernobbio, Italy, 2016
Auditory environment across the lifespan of cochlear implant users: Insights from data logging.
Dublin, Ireland, 2016
Auditory environment across the lifespan of cochlear implant users: Insights from data logging.
Aachen, Germany, 2016
The acoustical environment of cochlear implant users.
Namur, Belgium, 2015
Cochlear implant userâs auditory diet: Insights from data logging.
Toulouse, France 2015
CI userâs auditory diet. (poster, selected for oral presentation)
Linköping, Sweden, 2015
Cochlear implant userâs auditory diet. (poster presentation)
Vienna, 26 September 2018
Workshop for Cochlear ClinTechs
April 2020, nowhere
In 'What Makes a Difference?' beschĂ€ftige ich mit der Frage was eine Differenz, einen Unterschied, ausmacht. Es ist ein kurzes visuelles Essay inspiriert von den Werken Scott McClouds und Nick Sousanisâ. Differenzen begegnen uns auf vielen Ebenen: Die Aufrechterhaltung der Differenz von Innen und AuĂen ist oberstes Ziel unseres Körpers. In der Gesellschaft sind es die 'feinen Unterschiede', die die Menschen, gerade wegen ihrer Feinheit, nahezu unĂŒberwindlich voneinander trennen. In der Wissenschaft gilt der "significant difference" als der erbrachte Beweis. In 'What Makes a Difference?' betrachte ich Differenz aus der Sicht der Kognitionswissenschaft. Ich zeige, dass Differenzen manchmal 'just noticeable' sind, dass sie unsere Neuronen automatisch feuern lassen, dass wir Differenzen sehen wo keine sind, und keine Differenzen sehen wo sie existieren. Ich komme zu dem Schluss, dass es das Gehirn ist, dass den Unterschied macht. Die Umsetzung ist minimalistisch und auf das wesentliche reduziert. Dies ist zum einen zeitlichen und technischen BeschrĂ€nkungen geschuldet, zum anderen erinnert es an die Ăsthetik wahrnehmungspsychologischer Experimente, in denen alles nicht Essenzielle entfernt wird bis nur noch das ĂŒbrig bleibt, was zum Testen der Hypothese erforderlich ist. Statt eines Buchformats oder einer Infografik im Posterformat handelt es sich um das einer PowerPoint-PrĂ€sentation was ebenfalls an eine wissenschaftliche PrĂ€sentation erinnert.
2018, KU Leuven
đŒ slides and design documents
These presentations were for a course on Design Thinking and Making. The course was centered around critical design for the library of the future. These are the slides I made as part of the course assignments.
July 19th 2017, KU Leuven
đŒ slides and material. The slides are in Journal-Club-Bayes.pdf. The main part of the analysis is in the R Notebook loveAnalysis.Rmd. You can view a live version of it here. This repository contains the slides of my presentation and my own version of the analysis. In some places the code has been adapted to be more concise.
In this edition of the journal club we will talk about love, babies, and Bayesian statistics. The paper carefully takes us through the steps involved in specifying, fitting and summarizing a growth curve model (GCM) in a Bayesian framework. GCMs are useful in developmental research and fit naturally in the Bayesian framework. This is my first contact with Bayesian statistics and, although the paper provides a very nice introduction, it leaves a lot of questions, which I would like to discuss.
The paper we discuss is Oravecz & Muth (2017). Fitting growth curve models in the Bayesian framework. Psychonomic Bulletin and Review. The paper can be found here. The paper's accompanying git repository is here.