Recent graduate from Drexel University (USA), Matthew Parsons wants to pursue his lifetime goal: working for ITER. He aims to develop an algorithm capable of predicting disruptions, sudden and unavoidable losses of particles. An algorithm that will be invaluable for the world’s biggest fusion experiment. The 22-year-old with a Bachelor of Science in physics is currently applying for a Fulbright Grant that will enable him to work with the staff at ITER for a year.

You said in an article, that you want to change the world when it comes to solving the demanding energy issues. Why do you think fusion will be the solution?

I think one of the main selling points of fusion is that there is an abundant amount of fuel. A fusion reactor also operates independent of weather, daylight or night. It will always be available and it will not produce long-lived radioactivity like fission does.

When did you first hear about fusion?

When I was a Boy Scout at the age of about eleven I got my first merit badge, not for making tents or camping, but for learning about nuclear science, fusion and fission. I liked the energy aspects of it and of course getting a glimpse of the finer mechanics behind our universe. I later decided to study physics and find out what opportunities are available in this area.

You are using JET’s data for your studies on disruptions. How did you first learn about JET?

In my first year of college, in 2011, I chose current fusion research as a topic for my writing course and that is when I first heard about JET … and ITER of course.

Why are you developing models for a EUROfusion experiment in the US?

For several years JET has had an ongoing project, called the “Advanced Predictor of Disruptions” and they were seeking help with developing it further. Right at the end of my coop, a practical training programme I did at PPPL, I learned about that project. That is why I did a second coop, also at PPPL, to help develop a tool that predicts disruptions. In physics research, the traditional way to go about solving problems is hypothesis driven. You come up with a number of ideas on the theory side and prove it. With disruptions, there are so many things going on at the same time, it is really complicated and much more extensive than we are able to describe. One interesting idea is to predict disruptions using statistical methods. We have a lot of data from JET.

Why do you want to support ITER?

During my first year writing project, I remember looking at websites for all of the experiments. Everyone was doing research in support of ITER and I got an inkling of the significance of the project. I definitely wanted to be part of the team. I am very optimistic about the changes which the Director-General Bigot just announced. One of the main concerns of the US Congress, which approves money for ITER, is spending the money abroad when it could be used here instead. I think what they are paying for ITER is almost insignificant when you look at the entire project and its future outcome: a very small investment for a very great reward.

Matthew Parsons submitted his application for a Fulbright grant at the be ginning of October. He attached a very decisive letter of support from David Campbell, the Head of ITER’s Science and Operations Department, stating among other things: “We believe that the experience which he [Parsons] has gained in his area of particular interest, the development of data-driven and physics-based techniques for the prediction/detection of disruptions, will provide an excellent basis for the research project which he would pursue at ITER.[…]”