But these are just the basics, Simone points out, just an algorithm used by his research group. “What I do exactly as a computer scientist, is to optimize and accelerate the algorithm”, he explains. For that, he uses parallel computing and distributed computing. “What parallel computing means is that one computer does multiple things at the same time by using different cores,” Simone breaks down the first concept. “And distributed computing is using many computers all at the same time to work together to achieve a result,” he adds. The computer scientist is really excited about the fact that he has access to distributed computing setups at the LMU or the Leibniz Supercomputing Centre as this is something he wouldn’t have if he was just working somewhere on his own. “My job is really easy to tell but hard to do,” he summarizes his work, “because there are plenty of problems which arise whenever you try to use distributed computing.” One of the challenges, for example, is managing the communication of the computers within the cluster. Since he is effectively using many different computers, he has to consider that they all have different memories and that the communication can be very slow. To design an efficient distributed computing algorithm Simone therefore tries to reduce the amount of communication to a minimum while still making them work together. “You have to find that trade-off which will then effectively enhance the speed of the algorithm that you are running,” he explains.

Finding new applications for the algorithm

But he is not only aiming to accelerate the algorithm: “My job is to make it as easy as possible for the user to use. So, my goal is to make my work as little an inconvenience as possible for other people.” In the end, he wants to build an option, that can just be switched on if needed, and the code works faster. Right now, his physics colleagues are already applying his acceleration to work on their problems, that can take multiple days to calculate. With Simone’s code, that duration can be reduced significantly. “I am really glad that my job has an immediate impact in the life of my colleagues and to whoever is using that library that we are building”, he says. There are still some problems with the compatibility of code, but the computer scientist is optimistic, that a seamless implementation that can be used by everybody will be ready really soon. While he is currently working at a theoretical physics chair, he is already thinking about possibilities to apply his QTCI algorithm also “outside of physics”.

As an engineer by training, Simone of course knows the basics in physics, but describes himself as far from understanding what his physics colleagues are actively doing. “But I realize that every person has their own understanding, their own knowledge”, he explains. Therefore, focusing on doing his own best work, adding his specialized skills to the overall project and building tools that help as many people as possible doing research in their respective fields is what he likes to do. One of the things that he loves most about being a computer scientist are the many different possibilities to apply what he is working on. “Right now, my plan is to try to apply QTCI to financial mathematics,” he describes an example for applications that lay outside the focus of his current research group. He has always been fascinated by that topic, as he tells, because he likes everything that is abstract, like math or coding. To explore the direction of financial mathematics further, he is already in contact with a professor from TU Munich. “The cool part about my Ph.D. is that if I come up with an idea at any point, I can definitely try it out without being scared of the consequences,” he states.

Facing and overcoming challenges

Simone grew up loving to play videogames and staying on his computer all day long. That he can now do exactly that in his job is something he really enjoys. “Of course, from an objective point of view, a sedentary life is bad,” he admits. “But I enjoy staying at the computer even for twelve hours a day. It is something that many people would not like. But for myself it’s a good experience.” He likes that his work does not require a lab, all he needs is his laptop. And since his job is not physically demanding, he has the energy to go to the gym and work out in his free time.

Balancing his work and his free time was actually something that the computer scientist had to learn when he started his Ph.D. When he moved to Munich, he spent many Saturdays at the office. “I feel that in Italy, especially in Milano, we tend to overwork a bit, whereas in Germany people take mental health and work-life-balance more seriously,” he draws a comparison. At the beginning he struggled a bit with re-organizing his week. After being initially shocked that everything is closed on Sundays, he now adjusted well to what he calls the German lifestyle: “After living in Germany for a while you start to understand that in Italy you waste your weekends doing chores, whereas here you know that when Sunday comes you are really free.” These days he doesn’t find himself regularly without food on Sundays anymore but can really enjoy the “cute” city of Munich and its many green areas.  

It was mostly the move to another country, away from his family, that challenged Simone at the beginning of his Ph.D.: “The culture is different, the language is different, it took me quite some time to get used to this new country”, he admits. “So, at the start, my job was not as efficient as it is now.” And, as it is true for most Ph.D. candidates, his first months were mostly about understanding what he is doing, knowing almost nothing beforehand about the algorithm he is now working on. But he quickly found his way into it. Today, roughly half a year later, he is feeling confident with his progress. “Of course, stress is part of the Ph.D.,” he says. “But in Milano we say that stress is the fuel for achieving something. So, whenever I feel stressed, I feel motivated to work,” he adds. Simone also finds great joy in the fact, that diving deep into particular topics and learning new things, something he always loved to do, is now actually a part of his job. “I will read this book and I will read that paper, but I know I am not wasting time, because it is part of the Ph.D. itself to study your topics and your arguments,” he summarizes this part of his work routine.

The one thing that the Ph.D. student does not particularly like regarding his academic work is writing it all up at the end. “Right now, what I am doing is technically hard. But I really enjoy this type of complexity, I find it really stimulating,” he explains. “Whereas a challenge that I faced during my Master’s thesis and I will surely face again during my Ph.D. is writing the thesis, writing a paper.” The computer scientist is so used to think in abstract categories and in terms of numbers or functions that he finds himself somewhat stuck when in comes to writing his results down. But, as with every other challenge that comes his way, Simone is looking forward to facing these difficulties and overcoming them. 
 

Published 29 August 2025; Interview  29 April 2025