Born in Braunschweig, Germany • Studied Math (diploma) at the Technical University in Braunschweig, Germany • Highest Degree Doctorate in Math (Dr. rer. nat.) • Lives in Meine, Germany • Occupation Professor at the Hochschule Hannover – University of Applied Sciences and Arts, Department of Business Information Systems, Field of Data Science
Analytical thinking has always been easy for me. Therefore, I enjoyed the rules and patterns that occur in math from early on. Luckily, I recovered quickly after the German high school greeted me with the minimum pass mark “adequate” in the first two math exams in 7th grade. In 9th and 10th grade, we had a very strict “old school” teacher who left a lasting impression. We always had to stand up to greet him, and if you used a swear word in class, you had to wash the glasses in the chemistry room during the next break. He was strict, but he liked me and I learned a lot. In 11th grade I spent a high school year in the US and after this year I wanted to take math as one of my advanced courses. That was a tough decision because all I did at the American high school was statistics whereas in Germany everyone had started with curve sketching. After my return to Germany, the first exam in 12th grade was about this topic. I didn’t know anything about it and I had 6 weeks of summer break to study. A former very kind teacher helped me with the material and I studied by myself and achieved a good mark. That was a major milestone to my decision to study math, since I was able to teach myself the topics of almost a whole school year. But I still wasn’t sure. Math or psychology?
After all the ups and downs you typically encounter during this phase – 3 years for me – I finished my doctoral thesis in math (graph theory) two weeks before my first daughter was born.
Both sounded very attractive to my 19-year-old self. The plans to move to Braunschweig with two of my friends were already settled and I finally chose math because it was giving me a wider range of options on what future opportunities to follow – because I had no clue what to do after my studies at that point. In the beginning we were quite a few students, but in the end only 4 of us were left in pure math – 25% women 😉. I chose most of my courses in abstract math – algebra, combinatorics – and did as little applied math as possible. I really enjoyed the study of group and ring structures and the book Algebra by Serge Lang was always by my side. I already dreamed of becoming a professor myself.
Yet, in the end, the question what to do with all the knowledge I gained crept more and more into my consciousness. That is why I didn’t pursue a strictly academic career, nevertheless I still wanted to secure the option, and chose a PHD position in business at Bosch (formerly Blaupunkt) in Hildesheim. No more group and ring theory, suddenly I had to write code in C++ for algorithms in navigation systems. I had avoided any computer science so far, thus, I was thrown in at the deep end. But I never regretted this step because I discovered that coding is not all at all as difficult as I thought – after all it’s logical – and I learned a lot about working in a bigger company. After all the ups and downs you typically encounter during this phase – 3 years for me – I finished my doctoral thesis in math (graph theory) two weeks before my first daughter was born.
I found the fitting position where I can combine my passion for analytical thinking, my academic background, and my work experience (…).
I stayed home with her and somehow managed the defence of my doctoral thesies with a 5-month-old baby and still deprived of decent sleep. After 8 or 9 months at home, my brain started asking to be challenged again, and I began to apply for jobs in industry. As a young mother I wanted to start part time, but as a woman holding a doctorate in mathematics that was not as easy to get as I hoped. After a long search, including several offers with 40 hours and more, I was finally rewarded by starting a job at VW Financial Services. My one-year-old daughter was able to stay at the company’s own childcare facility and I started with 27 hours a week as a systems analyst in the business intelligence department in IT. In almost 10 years I made my way from analyst, to project lead, to team lead all the way to head of two sub-departments and got enrolled in the management trainee program – most of this in part time including a maternity leave when I had my second daughter in between. Then, suddenly, another option which had gotten a little out of sight but was still a silent dream popped back in.
And that is my way to my current position as a professor in business computing, especially data science. I found the fitting position where I can combine my passion for analytical thinking, my academic background, and my work experience – all of that with the advantage of being my own boss, still doing interesting projects with different companies, giving talks about AI for lay audiences (schools, senior clubs, …), and guiding young people on part of their own story.
Born in Hanoi, Vietnam • Studied Mathematics at RWTH Aachen in Germany • Highest degree: Master of Science in Mathematics • Lives in Utrecht, The Netherlands • PhD candidate in infectious disease modelling at the Julius Center for Health Sciences and Primary Care in Utrecht (The Netherlands)
I was born in Vietnam, grew up in Germany, lived in the UK for about two years in total, and moved to the Netherlands for my PhD four years ago. Having lived in various countries, I always saw myself as a cultural hybrid – bridging the gap between different cultures and traditions. My PhD topic similarly connects two different but intersecting disciplines: I develop mathematical models to tackle the spread of infectious diseases.
When you would have asked me what my future job would be when I was 10 years old, my answer would probably have been “a detective”. I loved solving puzzles and finding solutions to a problem. What I particularly enjoyed about maths was its simplicity: In its pure form, you only need your mind and maybe a pen and a paper.
I knew I wanted to continue to do research in something math-related, but I also realised that I wanted my work to have an impact in the real world.
After graduating high school, I decided to pursue a Maths degree at university. The reason was simple: I was eager to learn more about how to solve abstract problems through logical reasoning. Despite its reputation, you do not need to study maths as a lone wolf. A lot of my university time included working together with fellow students, discussing various solutions, and looking at a problem from different angles. Studying maths at university level was not always easy for me but I had a lot of fun, and I think that’s what counts in the end. When I was about to finish my degree, I felt a bit lost as I realised that I never really had a particular job or career in mind, and I had no real plan for my life. I knew I wanted to continue to do research in something math-related, but I also realised that I wanted my work to have an impact in the real world. However, I had no idea how exactly I could combine these two interests.
By chance, I came across the 80,000 Hours non-profit organisation that tries to guide graduates towards a career that fits their personality but also “effectively tackles the world’s most pressing problems”. This gave me an impetus to contemplate more thoroughly my career choice and I started to do research on the applications of maths to address real-world problems. I quickly learned about the serious risks that infectious diseases pose to our world and how mathematical modelling can provide valuable insights into the field. Luckily, I was able to find a PhD position in infectious disease epidemiology in Utrecht. In hindsight, accepting this position was one of the best decisions in my life as I can genuinely say that I am very happy with my work, my research group, and in particular with my supervisors. They gave me just the right balance between guidance and freedom, and a positive environment to thrive.
Since the start of the COVID-19 pandemic, however, I am using my background to model the spread of SARS-CoV-2 in various settings, for example in hospitals or secondary schools.
When I started my PhD my main topic was to study the transmission dynamics of antibiotic-resistant bacteria in hospitals. Since the start of the COVID-19 pandemic, however, I am using my background to model the spread of SARS-CoV-2 in various settings, for example in hospitals or secondary schools. It has been a very challenging time as my workload has doubled but at the same time, I feel very grateful to have the opportunity to use my skills to ‘do good’ while truly enjoying my work.
The current COVID-19 pandemic demonstrates perfectly that mathematics does not necessarily have to be far from reality, and that it can be a powerful tool for solving real-world problems.
Infectious disease modelling is rather versatile: It requires translating biological problems into the language of mathematics, analytically investigating the research question using the developed model, and finally translating the results back to the real world to obtain implications for infection control policy. The current COVID-19 pandemic demonstrates perfectly that mathematics does not necessarily have to be far from reality, and that it can be a powerful tool for solving real-world problems. Maths used to be underrated and maybe even underappreciated but by showing people how mathematics can be used to stop the spread of infectious diseases, I hope we can spread a little bit more love for mathematics.
The daily routine of a mathematician in the field of Data Management & Analytics can be diverse: Data collection, preparation and analysis, the design of artificial intelligence (AI) models, and much more. The opportunities to get involved in a data project are usually not limited to one’s own field. We, Mara (Senior Big Data Scientist) and Marisa (Senior Machine Learning Engineer), are two mathematicians who juggle data in a variety of ways every day. In this blog post, we describe what a day as a data juggler is like and how we use mathematics in our everyday lives.
If you study maths, you are faced with a wide range of possible career paths. But you should definitely take a look at the field of data management & analytics – not just because the Harvard Business Review called the data scientist’s profession the sexiest job of the 21st century [1]. In recent years, many specialised job titles have emerged, for example “Data Engineer”, “(Big) Data Scientist” and “Machine Learning (ML) Engineer”. However, they all have the same aim: to process data in such a way that useful information can be extracted (learned) from it and computers can act intelligently based on this knowledge. In particular, working with and implementing AI algorithms requires more than just AI experts – it’s a team sport. Regardless of their job title, it takes many different specialists working together as a team and complementing each other. Other areas of computer science such as database management or software engineering are also becoming increasingly important.
Marisa, what is your role as an ML engineer in the team and when do you still use maths?
Due to the above-mentioned diversity and the numerous connections to other team members, it is difficult to describe a typical day of an ML engineer because every day is characterized by new challenges – fortunately. However, even with the most complex challenges, our mathematical-analytical approach does not make us despair.
The mathematical modelling of data in a learning algorithm, be it through a slightly more applied, specialised linear regression, or through a fancy artificial neural network, usually takes up no more than the last 5-10% of a whole data project. For a prediction to work really well, the end-to-end idea is crucial. Where does the data actually come from? And what data do I need to arrive at a valid result? Do I have the right data? Can I get to more profitable data, or do I have to change the prediction goal? It’s crucial to understand the big picture. After all, you need exactly the data that fits the problem you want to solve.
All of AI […] has a proof-of-concept-to-production gap. […] The full cycle of a machine learning project is not just modeling. It is finding the right data, deploying it, monitoring it, feeding data back, showing safety — doing all the things that need to be done to be deployed.
Andrew Ng [2]
In general, an ML engineer is a person who helps deploy machine learning or artificial intelligence algorithms in a productive environment so that they can be used in the day-to-day business without difficulty. That sounds like a lot of infrastructure operations and software engineering, and yes, that can be a big part of an ML engineer’s job. You have to understand the existing IT landscapes and systems at the customer level to decide how to build a pipeline in those existing systems between the data and the output of a prediction, and how to deploy everything at the end. But as mentioned before, AI is a team sport. Of course, as an ML engineer, I’m not the specialist in everything, but it’s important to stay on top of everything.
Marisa Mohr
Now, how much mathematics is needed in this interdisciplinary field as an ML engineer primarily depends on the level and interest of the individual in the mathematical-statistical techniques that are being used. There is this type of ML engineer who spends all day building infrastructures or programming software to make an intelligent algorithm run productively in the client system. This kind of ML engineer is certainly more influenced by computer science than I am as a mathematician. I admire that, but I could never get lost in coding, and the good thing about being an ML engineer is that you don’t have to. The profession is so multi-faceted and multi-dimensional that everyone can follow their own passion and take their personal role in the team – with the bonus of dabbling in other roles every now and then.
As a mathematician, I have taken on various roles over the years. During a project phase, I often take on the role of a general strategist or project manager, ensuring that the team follows the same vision to bring together input and intelligent output in the productive environment. Then, when data modelling specialists are required in the project, I have the opportunity to follow my mathematical passion in the form of smaller data explorations and visualizations, through the evaluation of mathematical relationships in the data, to the selection and training of learning algorithms. The latter also includes consideration of accuracy, training time, model complexity, number of parameters, and number of features. In addition, parameter settings and validation strategies have to be selected, underfitting and overfitting have to be identified by understanding the bias-variance trade-off, and confidence intervals have to be estimated. A deep dive into maths for ML can be found on Medium [3]. As a mathematical minded ML engineer, my role can therefore be similar to that of a data scientist from time to time.
This role change and diversity is what I love about working as an ML engineer, or working in a data project team in general. Another ML engineer could certainly take many more technical roles, especially when it comes to gathering the appropriate data without which no ML or AI model works. And that’s where Mara comes in.
Mara, what do you do all day as a data engineer and when do you still use mathematics?
After my studies in mathematics, I started working as a data scientist for an IT company. When I applied for the job, I was asked in the interview what title I would prefer: data engineer or data scientist. At the time, I was convinced that the latter was the only reasonable choice for a mathematician like me. Even during my studies, I was a working student in the fields of data science and in addition to that, I also attended lectures on data mining, neural networks and other related topics.
The connections between mathematics and data science are numerous – in fact, data science is mainly the application of mathematical models to various use cases. And I wish this fact would be taught more often and more emphatically at university.
Have you ever wondered what all that mathematical theory is good for? If you are a mathematics student – have you ever been frustrated about all the types of matrix factorizations one has to learn in numerical mathematics? Or perhaps you are a high-school graduate contemplating the high art of analysis and algebra but you fear it will end in nothing?
I can soothe you: The use cases for mathematics and its theories are boundless.
Mara Hermann
One of my favourite examples that I encountered during my job as a working student are recommender systems. A great introductory article on this topic can be found on Medium in which recommender systems are defined as “algorithms aimed at suggesting relevant items to users” [4]. Those items could be for instance products in an online shop or movies on a streaming platform. The interaction between items and users can be represented by a sparse matrix where each entry describes e.g. how a user rated a specific movie or if a user bought a given product. One approach to retrieve information and learn recommendations from this matrix is to decompose it into two smaller and denser matrices, the so-called matrix factorization. One matrix then describes the user representation and the other one the item representation – a great illustration of how a mathematical framework can be used in practise, just to name one example. Also other mathematical methods find use in the theory of recommender systems.
Now I fancied about how various and “sexy” [1] the applications of pure (and sometimes dry) mathematics in data science can be. But if you read this article carefully, you may have noticed that I wasn’t asked about my work as a data scientist but as a data engineer. Why?
As already mentioned, working on AI or – generally speaking – a data project is a team sport and in this course you also get in touch with other roles and switch positions from time to time. With my mathematical background I always had great respect for the role of a data engineer which I thought would be reserved for “real” programmers with an IT background. In the beginning of my studies I wouldn’t have thought that I would ever be interested in coding and, like Marisa, I will probably never be as much into programming as someone who studied computer science. But data engineering is so much more than sitting in front of the laptop, producing green letters on a black screen while typing at the speed of light.
The “unsexy” sibling of data science sure inherits more aspects from computer science than from mathematics [5]. As a data engineer, one designs, implements and monitors data pipelines which may feed a Data Scientist’s ML models. Additionally, data storage and quality are a huge part of the cake. Programming skills and willingness to permanently learn new technologies are indispensable in this job.
With this role description in mind, it’s true that you don’t necessarily need maths for being a data engineer. But that doesn’t mean that mathematicians can’t be good or even excellent data engineers at all. Their education entails a lot more than knowledge in algebra, analysis and many other subjects. It is often said that mathematics and philosophy are closely interrelated, some universities like Oxford even offer lectures combining both disciplines [6]. Even without attending such a course, a mathematics student acquires a lot of soft skills which are basic tools in the everyday life of a data engineer: One has to handle complex systems consisting of different data sources connected through various pipelines. With logical and analytical thinking one can better understand and design ETL (extract, transform and load) processes. Thoroughness and checking for accuracy are key to monitoring data pipelines and ensuring high data quality. Resilience, deduction and reasoning are of great help during performance tuning or debugging data pipelines. With some of these capabilities in your tool kit you have a great foundation for the role of a data engineer, practical experience comes with time.
Thus, the opportunities for a mathematician in the data sector are broad. Different types of people and skills are required and there are numerous further training possibilities. Also, data projects can be very diverse, since data is everywhere: e-commerce, food and fashion retail, logistics, mobility, smart buildings,… One can always find a use case which fits one’s taste. I can definitely recommend taking the chance and gaining an insight into this branch.
Regardless of which field of study or career path you choose, I can only encourage you to look beyond the horizon and also get a taste of other roles and fields than the ones you are already familiar with. Be it positive or negative, it will be a learning experience for you. And you will be an enrichment for every team if you can think out of the box.
Born in Trier, the oldest city of Germany • Birth year 1966 •Studied Business Mathematics at the University of Trier • Highest degree Diploma in Business Mathematics •Lives in Mainz, Germany •Leading teams in Software Development and acting as Chief Agility Master in the Airline IT Industry
In primary school, I struggled with math. My mother put a lot of effort into making me understand the difference between “plus” and “minus”. We were the first kids in Germany familiarized with set theory, working with books but also with these small boxes with plastic shapes of squares, circles, and triangles in different colors. My fascination for math started with geometry, with divisibility rules that our primary school teacher encouraged us to identify by ourselves and with the first mathematical proofs. When I was at grammar school, our teacher in mathematics told my mother: “She will never study mathematics, she is too lazy.” He was right about the laziness. My nickname is sloth, as I love lying in my hammock reading books. But I was fascinated by the ability of mathematicians to transform one problem into an equivalent one we can (easily) solve. The University in my hometown organized an open day and I attended some lectures. That’s when I decided to study math. It was a lecture about infinity and one on how to describe oscillations. This convinced me finally. When I was at university, our professors told us: “Later in your job most of you will never deal with mathematical problems like at University.”
Contrary to my professors’ prediction, I was one of the rare species among my fellow students who applied what we learnt at University.
My professor in numerical mathematics gave me the opportunity to work in a research project on optimization in robotics. Moreover, I received the opportunity to present the project at the industry exhibition in Hanover. He gave me trust, which created self-confidence I never had before. He changed my life. At this exhibition, I met my later husband. As he was living in the Rhine-Main-Region, I skipped my plan to obtain a PhD at my University. Instead, I searched for a job. This is how I started working in a very fascinating industry, the airline IT, as a software engineer in the area of flight optimization. Dijkstra for many years was and still is the algorithm of choice for solving shortest paths problems. At least it is a good basis. It is no longer sufficient due to many influencing factors such as regulations of air traffic flow. Cost optimality means reduction of fuel consumption, but also of overflight costs that are very hard to model. Contrary to my professors’ prediction, I was one of the rare species among my fellow students who applied what we learnt at University. Of course, not all problems in our industry are of this complex nature. However, developing algorithms and implementing software was complicated enough to keep me enthralled. So finally, both were wrong, my math teacher who said I would never study mathematics and the professors. Or did I want to prove them wrong?
It is a welcome change in a captivating profession of forming high performing teams, of dealing with trust-building and the soft facts of human interaction.
After 7 years, I decided to do something completely different. With my knowledge about software production, I joined a small team, the staff in the strategy department of our company. I gained insight into many different departments, sales, production, evaluation of acquisitions and business plans. Finally, I realized software production fascinates me most. So I returned, working in the role of a project manager for a completely new product development. Growing more and more into the leadership role, I was responsible for forming teams to build and operate many of our software products, applications managing the schedule preparation and operation of our airline customers worldwide. After 20 years, I returned to my roots, flight optimization. Developing algorithms for trajectory optimization is not my occupation any longer. Today, I am acting as a sponsor for our projects with the Zuse Institute in Berlin. It is a welcome change in a captivating profession of forming high performing teams, of dealing with trust-building and the soft facts of human interaction. I feel privileged, working in an international environment with diverse teams. Enhancing my knowledge by newest research in neuroscience and systems thinking is combining my private interest and profession.
The combination of rationality and empathy is not only possible; it is the theme of my story.
My favorite shape is the circle. Or is it more an upward spiral? Trust creates self-confidence. This is what I learnt in the research project at University and from my professor and my husband, who encouraged me very much in my professional development. Feedback and reflection create learning and improvement. The most amazing teams I know learnt from their mistakes and never stopped deriving actions to improve. Fearlessness creates the willingness to take responsibility. This describes very well the environment in which I could and still will grow from one role to the next. I had and still have colleagues and superiors I can talk to very openly, speaking my mind. I am not “punished” but supported in case things go wrong. As a mathematician, I have shown my ability to solve complex problems, as a leader I need to support teams to grow in a changing world. I love the following quote from Virginia Satir very much: “We get together on the basis of our similarities; we grow on the basis of our differences”. The combination of rationality and empathy is not only possible; it is the theme of my story.
Born in Münster, Germany • Birth year 1988 • Studied Mathematics in Münster, Germany • Highest Degree PhD in Mathematics • Lives in Hamburg, Germany • Occupation Research Scientist in Medical Imaging
The decision of what to study was not clear to me for a long time. I always liked math, but I could not really imagine what a job as a mathematician could look like. Only after discussing with family and friends at the end of my high school time, but especially with my godfather who very convincingly told of his positive experiences of working with mathematicians and of the usefulness of their skills, I discovered the diversity of applications. Being a person who had always been struggling a bit with making decisions, I immediately liked the idea of not limiting my future job perspectives in industry by the choice of the subject. I probably made my final decision during one of the annual university events where high school students can attend different university lectures for one day. Since I was quite undecided, I prepared a schedule and planned to attend lectures in different departments, amongst others in the medical and pharmaceutical department. I had seen some math lectures before and I liked them a lot, so I wanted to explore other options and focus on subjects other than mathematics to try and see if I would like those even more. So, I decided to attend a pharmaceutical lecture, but I knew immediately that this was not going to be my profession. I left 10 minutes after the lecture started and just went over to the math department again to yet attend another lecture. As soon as it started, I realized that the only reason I went there was to treat myself at the end of the day, because I knew I would enjoy it. That insight finally led me to the conclusion that I did not need to continue searching for anything else. I had already fallen in love with mathematics, especially the logic and the fact that everything makes sense if one just follows every single step in calculations or proofs accurately.
In the end, it did [work out], and I am more than happy that I took the risk to fail.
In the beginning of my math studies, I was surprised about the speed of the actual lectures and how different they were from the classes taught in school. I never regretted my decision, but the first two or three semesters were not easy for me to master. However, things became easier once I was able to specialize further in my studies. Even though I always thought I wanted to stay away from numerical mathematics, I eventually ended up putting the entire focus on applied mathematics and I also specialized in this field during my Master’s. Despite my previous hesitation, I quickly realized how much I liked the lectures and that they suited me more than the purely theoretical ones. The question about whether to do a PhD or not was a tough one again. I was doubting myself, but I already knew deep down that I had to give it a try. Otherwise, I would have always regretted not trying and wondered whether it would have worked out. In the end, it did, and I am more than happy that I took the risk to fail.
(…) I am happy that I still need many of the concepts and techniques that I learned at university.
After finishing my PhD, I left academia and I am now working in industry. I feel lucky that I still work in the same field I researched when I was at the university with similar applications in medicine. Therefore, the transition from academia to industry was quite smooth. Even though mathematicians are often in high demand on the job market for their way of thinking, but not necessarily for the direct knowledge obtained in math lectures, I am happy that I still need many of the concepts and techniques that I learned at university. I work in medical image computing and contribute to different aspects of enhancing CT and MRI acquisitions. Hence, I still apply some learned algorithms and I can also still be creative in the way of optimizing and adapting them to be suitable for specific applications.
Born in Lippstadt, Germany • Birth year 1988 • Studied Mathematics in Münster, Germany • Highest degree M.Sc. in Applied Mathematics• Lives in Friedrichshafen, Germany• Occupation software developer (automotive sector)
I have always been fascinated by mathematics, to be precise by calculations and computations. My parents first noticed my interest in maths at the age of 5. We often played a game called Kniffel/Yahtzee where at the end all points had to be accumulated and that was my favourite part of the game. I just loved adding up all these numbers.
My interest got even stronger during high school: in the year book one of my descriptions by class mates was „i = √(-1)“. This expression summarised pretty well my time in high school. I adored mathematics and I never had any problems in studying and understanding the subject and its concepts. But then I decided to study mathematics in university and the problems began…
We motivated each other and I slowly started to love mathematics again especially the beauty of mathematical proofs.
In the beginning, I struggled a lot in how to study. I know that sounds weird but in school I never had to study to get good grades. In school we never proved any theorem, we just used all these formulas resulting from them. However, in university I learned why these formulas are correct. In the first years of studying mathematics, I learned the basics of analysis, linear algebra, stochastic, logics and numerical analysis. I failed a lot of these exams and at some point, somewhere around the fourth semester, I even thought about quitting and doing something else. Fortunately, at this point I realised that most maths students struggled with the same or similar problems. This common issue and uniting quest created a strong sense of community among the students and that was one of the best parts of studying mathematics for me. Everybody, even the professors, were very helpful and supportive and I never felt alone. We motivated each other and I slowly started to love mathematics again especially the beauty of mathematical proofs. At the beginning of the master studies, I attended courses in applied mathematics with practical applications in the field of biomedicine, e.g. image processing in MRI, PET and CT; in numerical analysis classes I learned to write code and implement algorithms. That was my first experience in coding but to be honest I was not expecting to be a software developer one day.
I also conducted job interviews and I have learned that it is not important what you did, it is important what you love and where you want to be in the future.
After finishing my master thesis, I did not have any clue about where to go or what to do, it was hard to find job advertisements where mathematicians are mentioned. So, I signed up in several job portals and got job offers as a software developer. First I started in a consulting and engineering company and gained work experiences as a developer and a project manager. I also conducted job interviews and I have learned that it is not important what you did, it is important what you love and where you want to be in the future.
At the moment, I am working for a company which is a worldwide supplier of driveline and chassis technology for cars. Specifically, I am responsible for shifting strategies. That means I am getting a so called “change request”. Within this change request I get a specification about the functional change of the software. For example, the customer (automotive manufacturer) wants the car to behave in a certain way, like shifting to second gear only when engine speed is above a defined threshold. My task then is to understand the request, to change the software/code, to test the new software and to document everything I did. Of course this is an easy example and the reality is much more complex but the complexity and the diversity of my job is what I like.
Looking back, I am so happy that I studied mathematics because it got me where I am right now. If I could tell my 20-year-old self a piece of advice: “Just do it, you will learn so much about yourself, about logical thinking. It is a long way, be patient with yourself, surround yourself with like-minded people, they will help you to stay on track and enjoy your time at university. Do whatever you like and makes you happy.”
Born in Münster, Germany • Birth year 1988 • Studied Mathematics in Münster, Germany • Highest degree PhD in Mathematics and Statistics from the University of the Basque Country in Bilbao, Spain • Lives in Cologne, Germany • Current Occupation: Applied Mathematician at Bayer
It started all in 4th grade. After being really bad at mental arithmetic, I started to enjoy mathematics for the very first time when concepts became a bit more complex. When I was 9 years old I decided not only to study but also do a PhD in mathematics. So after finishing the A-level, this was exactly what I did. Of course it was hard and different from the maths they teach in high school but I got to appreciate the pure and perfect way of mathematical proofs. However, it was not before the end of my Bachelor that I learned about the diverse applications of mathematics in Biology and Medicine. I never grew very fond of the theoretical part but just saw it as a tool you need to understand and master in order to apply the theory to real world problems. Even though I always had the dream of doing a PhD in mathematics, doubting my skills and abilities made me question this dream. What finally convinced me to continue research and start a PhD in maths was a very honest talk by a female professor at a meeting of women in maths. By coincidence I found the PhD position in Bilbao (Spain) in computational neuroscience and directly knew that this was my topic.
The most exciting part of research for me was and is solving a problem. It is like a scavenger hunt: you follow traces, read instructions and do trials, which surprisingly involves a lot of creativity.
With the focus on personalised models for a phenomenon related to migraine, I got the opportunity to learn a lot of different strategies from numerical methods to solve differential equations, to curvature approximations and data processing. I worked with neurologists, physicians and medical doctors and learned a lot about interdisciplinary communication. The most exciting part of research for me was and is solving a problem. It is like a scavenger hunt: you follow traces, read instructions and do trials, which surprisingly involves a lot of creativity. Of course it is not all fun, running the simulation for the umpteenth time and writing papers is never going to be my favourite part.
Right when I started to write my PhD thesis, I fell sick and was all of a sudden experiencing personalised medicine from the patient’s point of view. It totally swept me off my feet because I had to pause my PhD for a while and could not stick to the schedule that I had planned. During this time I got a lot of insights in the diversity of medical treatments and was surprised by the differentiated treatment strategies. However, I also saw the potential for data-based fine tuning in the treatment strategies. After this forced break I focused even more than ever on the things that I really wanted: finish the PhD, see the world and find a job in mathematics with an impact.
The first of these points I tackled as quickly as possible. Even though I enjoyed research I could feel a weight lifting from my shoulders when I finally defended my thesis. The second point, traveling for a year after the PhD had always been a fixed idea in my head but talking to friends and family brought up a lot of doubts: would this look bad in my CV? Would this have a negative impact on my career? Would traveling alone be dangerous? However, after very encouraging conversations with professors and friends who had already travelled alone for a longer time, I just took the leap. I bought the plane tickets and went backpacking from Peru to Patagonia in the very south of Chile and through New Zealand by myself. In the beginning before leaving it was scary but in the end it was one of the best decisions in my life, and I learned so much about different cultures, traditions, people and communication that no book or course could have ever taught me.
After hiking the Patagonian highlands, starting as an applied mathematician in a pharmaceutical company is now my next big adventure.
The question if I want to continue research after obtaining my PhD already haunted me during my PhD studies, but when I got back from my big trip I finally knew the answer. I wanted to use my maths skills to help people in the medical sector. Consequently, I solely searched for maths jobs in pharmaceutics where I have just started as an applied mathematician. Changing from the university to a company opens up a totally new universe which I am still exploring but I am very curious and excited to better understand. So after hiking the Patagonian highlands this is now my next big adventure.
During my studies and my big trip I was very lucky to meet encouraging role models, supportive fellow students and inspiring like-minded people that helped me find my way – thank you all.
