CSH retreat scientists



Impressions from this year’s Hub retreat


From August 31 to September 1, the second Hub retreat for our scientists and stuff took place in beautiful Helenental in Lower Austria.

It was not only great fun again. As you will notice when you scroll through the impressive list of CSH Resident Scientists, the Hub  considerably grew in the past year. For all these newbies as well as the well established people, this retreat offered a great opportunity to get to know—and as we can see during lunch- or teatime in our courtyard every day: to like—each other.



(To be continued. And no: To my knowledge no Hub dog yet… )



Samuel Martin Gutierrez 2 500x500 c VA
Samuel Martín-Gutiérrez has been a PostDoc at the Complexity Science Hub Vienna since May 2021.

I joined the Hub last May, so this was my first retreat. As a newcomer, it was a very nice experience, as I got to know people that I don’t normally interact with during the everyday work at the office.

We started the retreat with a simple but fun quiz game. We were divided into groups and the title of a paper written by a member of the Hub was read aloud. The first group that guessed the author won a point. Then, the author would briefly explain some interesting stuff about the paper.

In my group, most people were newcomers, so it was difficult for us to get some points. We even missed a couple of easy ones (papers that some of us had published), as other groups were faster. But it was cool to see that other people at the Hub knew about our research, and it was a fun way to get a broad overview of our colleagues’ works.

After the game, we started the workshop. Several projects were proposed and the idea was to work on whatever project we found the most interesting. Three caught my attention: measuring the complexity of music, creating artistic works based on our research and studying cooking recipes using networks. I ended up joining the last project, as cooking is one of my favorite hobbies.


We started collecting some recipes databases and discussing about what kind of information we would like to extract from them. For example, we considered trying to find equivalent ingredients: those that can be interchanged in a recipe obtaining a very similar result. This is useful if you want to prepare an exotic dish and your usual supermarket doesn’t have some of the ingredients. A complementary idea was to find key ingredients that, when included, excluded or changed by others, led to a completely different kind of dish. For example, in a pasta recipe, cream or eggs can be used to get a creamy texture, like in carbonara. But if you use coconut milk instead, while the resulting texture may be similar, the flavor changes completely, and the dish acquires an Asian feeling.

These were the kind of insights we were hoping to get by using, for example, networks of ingredients, where two ingredients would be linked if they appeared together in a given recipe. However, we quickly realized that when preparing a dish, the process is just as important as the ingredients (or more), and this would add a layer of complexity to our analyses. Thus, we turned to a simpler kind of gastronomic products where the preparations do not (arguably) play such a central role: cocktails.

It was a nice surprise to discover that one of the members of the work group, Liuhuaying Yang, had already worked with cocktail networks and she had created beautiful visualizations (https://interactive.zaobao.com/cocktails/index.html).

From this point, our main idea was to build a network of cocktail ingredients—two ingredients are connected if they are used in the same cocktail—and hopefully find hidden links that were not present in this network but that would lead to innovative, surprising and delicious combinations.

Unfortunately, we didn’t have the time to achieve this final objective, but we found that it was a (mostly) unexplored area, so we may continue this work at some point in the future.

The only project that we found that tackled a similar challenge consisted in making an AI read a lot of cocktail recipes and then generate new ones.

Besides the stimulating discussions and the fun activities—the archery, the pub quiz, etc.—, the best part was that, if before the retreat I already felt comfortable at the Hub, after it I really started feeling at home.


Frank Neffke, faculty member at the Complexity Science Hub © private

After a long time in which spontaneous interactions had become difficult, this retreat was like a breath of fresh air.

Being new to the Hub, it was also a great way to get to know so many of my new colleagues.

I was truly impressed to see so many talented people from different disciplines eager to team up and apply their skills to understand the world and then try to make it a better, safer, and more sustainable place to live in.

I’m looking forward to becoming a part of this.


Jan Bachmann, PhD Candidate at the Complexity Science Hub

The retreat was amazing. Out of pure curiosity, I decided to join the music complexity project for the “omg it’s braining” workshop.

The history and development of complexity in music and science in general lead to fruitful discussions and it was a great opportunity to learn about music theory.

One of the many highlights was the final presentation round of the workshop. Here we all learned about the network of cocktail recipes, an analysis of the popular community game ‘Mafia’,  and participated in real-life contagion games.

For me as a newcomer at the Hub, the retreat was also a great possibility to get to know colleagues outside from the day-to-day work environment. Performing archery in the hotel’s backyard, sweating in the sauna, or solving difficult questions during the pub quiz were the perfect activities to do just that.

I can’t wait for next year’s retreat!



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