Mathematically Optimized Mobility Maximilian Merkert is Junior Professor at the Institute for Mathematical Optimization
As an applied mathematician, he particularly likes the connection between fundamental theory and current application. What fascinates Professor Maximilian Merkert is how similar mathematical questions can arise in different disciplines. Before accepting the call to TU Braunschweig in October 2021, he taught and conducted research as a postdoc at Otto von Guericke University Magdeburg. We asked him to answer our questionnaire.
Professor Merkert, have you arrived well at TU Braunschweig?
I received a very warm welcome. At this point, I would like to say a big thank you to everyone who helped me get started! Unfortunately, due to the pandemic, I have not yet been able to get to know Braunschweig as a city and life at the TU in many of its facets. But I am very confident that I will feel at home here.
Why did you choose TU Braunschweig?
TU Braunschweig offers a very good professional environment and is excellently positioned, especially in my field, i.e. applied mathematics and the application focus on mobility. As a young scientist, you usually can’t choose the place where you are first appointed, so I am very pleased to have received the call to Braunschweig.
What exactly do you deal with in your research?
Mathematical optimization is concerned with finding the best possible decision from a set of possible decisions, i.e. one that minimizes or maximizes an important variable. This can – but does not have to – be the maximization of profit; depending on the context, fairness, satisfaction or the energy consumption of a system can also be optimized.
One would also like to be able to mathematically guarantee the quality of the result. This is a great challenge and is especially difficult if there are many, very different possible actions, if the system to be optimized reacts sensitively to small changes, and/or if input parameters of the model are subject to uncertainties. However, all this is not uncommon in practice; this is also the case in applications from the field of mobility, which I deal with in particular. Examples are the optimization of traffic at urban intersections controlled by traffic lights or the generation of energy-efficient train schedules.
My research is concerned with modeling optimization problems from practice in a suitable way and designing or improving methods for determining optimal solutions for these models.
What are the main research areas and projects you will be working on at TU Braunschweig?
I would particularly like to get involved in the TU’s core research areas “Mobility” and “Future City”, to which some of my research topics fit very well, such as the aforementioned traffic optimization. Mathematically, I am interested in mixed-integer nonlinear optimization, i.e., the connection of discrete optimization decisions (e.g., which signal a traffic light shows) with continuous decisions in dynamic systems (e.g., which speed a vehicle has). Both discrete-combinatorial and continuous-nonlinear optimization are already strongly represented at the Institute for Mathematical Optimization, so I hope to bridge the gap here. Furthermore, I find the development in the field of Data Science very exciting and would like to contribute to the related mathematical questions.
What excites you about science?
I like to question things and systematically get to the bottom of causes. The beauty of mathematics is that you can rigorously prove statements. They then remain always and absolutely true and these proofs are also verifiable by others (with appropriate training).
In applied research, of course, one is always dealing with models that are supposed to represent reality. They never do this perfectly, but often surprisingly well. What is exciting here is that mathematical statements have meaning in the application. Applied mathematics itself also has an empirical side, for example when it comes to the performance of certain algorithms. Here, experiments have to be confirmed from time to time, or findings have to be corrected. All this makes this field very versatile.
A look at the future: What are your plans?
Of course, I first want to arrive properly. This includes filling my PhD position, making my topics and advances courses known to the student body and, of course, establishing research collaborations with others within TU. There are links not only within mathematics, but also with the other departments of the large Faculty 1 or with other faculties via application topics. I have already had the opportunity to exchange ideas with some potential cooperation partners, but there are also many with whom I would still like to talk. And most likely I am overlooking some possibilities. If anyone reads this who would like to talk to me about mathematical optimization, I would be happy to hear from you.