Particle Physics is in the fortunate position to be able to collect enormous amounts of data using sophisticated experiments. This data is then used to learn as much about the nature of the subatomic world as possible, hoping to find cracks in our theoretical framework. This is a highly non-trivial task. As modern data science has proven in many other fields to be beneficial in data analysis and statistical inference, my research focusses on tackling the problems in particle physics with modern machine learning techniques.

A complete list of my publications can be found on Inspire and all of them can be freely accessed on arXiv . A selection of these publications is provided here.

Sampling high dimensional parameter spaces can take a long amount of time. However, if your goal is to find e.g. a boundary in this space, not every part of this parameter space is equally interesting. Typically you are more interested in the region close to the boundary than far away from it. Using machine learning it is possible to spend as little time on the uninteresting part. This technique is called *active learning*. In this work we show the application of it in the exploration of model spaces and show that it can speed up our searches.

The first use of a machine learning algorithm to accelerate and generalise high-dimensional exclusion limits. We used a RandomForest algorithm to encode both 8TeV and 13TeV ATLAS exclusion limits in a 19-dimensional supersymmetric model. Where a conventional exclusion determination could take up to hours, the trained algorithm is able to do this for any model point within a fraction of a second.

The trained algorithm is called SUSY-AI. It can be downloaded from http://www.susy-ai.org , where also an online version is available.

Research in high energy physics more often than not implies working with high-dimensional parameter spaces. The Netherlands eScience Center developed an open-source tool to visualise together with us that is able to make the visualisation of high-dimensional parameter spaces as easy as drag-and-dropping elements in a website. Visualisations are linked and can be shared with fellow researchers.

An online prototype is available at http://www.idarksurvey.com/ .

I believe it is an honerable part of the job to be able to explain the ins and outs of physics (or science in general) to the general public and to be active in a community as a scientist.

- Taught a 7-week course on Complex Numbers for high school students
- Volunteered at the Dutch Physics Olympiad 2017
- Volunteer at the pre-university college of the Radboud Science faculty

I maintain a YouTube channel on which I explain the fundamentals of particle physics through animations and analogies. You can find this channel here.

Apart from the outreach to the general public, i am also active in teaching at Radboud University. Already having a qualification to teach in the first few years of high school, i am now actively working towards a qualitication to teach at any university and university of applied sciences in The Netherlands. A non-extensive list of courses i have been involved in includes:

- Machine Learning in Particle Physics and Astronomy as course developer and teacher
- Standard Model and Beyond as teaching assistant and teacher
- Introduction into Experimentation as course developer and teacher

I am always looking for more ways to make particle physics and machine learning more accessible to the public. If you want to share your ideas or might be interested in working together on an interesting project, don't hesitate to contact me!