MachineLearning

11
Feb
2026
Christina Runkel

Christina Runkel

Born in Neuwied, Germany • Studied Computer Science at Cooperative State University Mannheim • PhD in Mathematics of Information at the University of Cambridge  • Lives in Oslo, Norway • Postdoctoral Research Fellow at University of Oslo

My interest in mathematics started as a young child and stayed with me throughout my school years. I remember particularly enjoying maths in the final years of school because of our very friendly and motivated teacher. He encouraged me to have a closer look at STEM subjects and maths in particular when trying to decide what to study at university. I could not decide between computer science and maths at first, but ended up doing computer science because of a cooperative study programme that allowed me to study and work at the same time; offering financial stability and practical skills. I ended up working at IBM in Germany where I was lucky enough to be able to choose from a large pool of projects for each of the six internships that were part of the Bachelor’s programme. While mostly focusing on IT consulting internships in my first year, I got the opportunity to work on a research project in a research lab in California in my second year – where I helped to develop new machine learning methods for chat bots – which encouraged me to go more into research for my final two internships, too.

During my Master’s it then became very obvious to me that I really enjoyed doing research and was mostly interested in the maths courses and aspects, which is why I decided to switch to applied maths for my PhD.

While really enjoying the internships that were part of my Bachelor’s programme at the Cooperative State University in Mannheim, I quickly realised that I wanted to get a deeper insight into the „traditional“ student life. My Bachelor’s programme was very structured with little opportunity to choose courses and attendance at lectures was compulsory and I always envied my friends at other universities who had a lot more freedom. Due to the tight connection of my Bachelor’s programme to companies, the curriculum was very applied with little opportunity to do any research. When deciding on a Master’s programme, those ended up being the two main motivations for me to switch to studying a regular computer science degree. During my Master’s it then became very obvious to me that I really enjoyed doing research and was mostly interested in the maths courses and aspects, which is why I decided to switch to applied maths for my PhD. 

Throughout my PhD I always most enjoyed going to workshops and conferences.

Having had the opportunity to combine my Master’s thesis with a research visit in the Cambridge Image Analysis group at the Department of Applied Mathematics and Theoretical Physics in Cambridge, I got a better understanding of the PhD system in the UK and decided to move to England for my PhD. While most PhDs in Germany are tied to a specific project, the prospect of being part of a doctoral training programme which allows for a lot of freedom sounded very convincing to me. Having been funded by a departmental studentship, I got to work on several projects from a broad range of topics like machine learning theory, privacy and security in machine learning, inverse problems and operator learning. Throughout my PhD I always most enjoyed going to workshops and conferences. I was lucky enough to be able to travel to conferences all over the world like Singapore, Japan, the US, Italy and Germany. 

Coming to Cambridge as an international student, I particularly enjoyed being part of a college community with the opportunity to meet people from all over the world and different subjects. While my friend group in both my Bachelor’s and Master’s mainly consisted of people studying computer science, maths and physics, my friend group during my PhD was much more diverse. This is also due to the fact that I started rowing as a new sport when coming to Cambridge which facilitated meeting even more people including undergraduate students of all years.

While I was not interested in image analysis yet at that time, the professor’s enthusiasm for the topic rubbed off on me and motivated me to choose a computer vision project for a one year project that was mandatory as part of my degree. 

Thinking about the most memorable moments of my studies, it was the people who motivated and inspired me most. If it was professors speaking very passionately about a certain subject during lectures or PhD students supervising projects – seeing other people’s interest for the subject always motivated me to keep looking into different fields of maths. I still remember walking into one of my first lectures in my Master’s which was part of a deep learning course. While I was not interested in image analysis yet at that time, the professor’s enthusiasm for the topic rubbed off on me and motivated me to choose a computer vision project for a one year project that was mandatory as part of my degree. 

I also was fortunate enough to have been surrounded by supportive and inspirational people all throughout my Master’s and PhD who encouraged me to go for the next step and believe in myself. Especially as a woman in a very male dominated field, being surrounded by female role models and being supported by both male and female peers and professors made a big difference for me. When starting my PhD, I tried to pass some of this on by being part of mentoring programmes for female and non-binary undergraduates and students. I was both a mentor at the Faculty of Mathematics in Cambridge where we had termly in-person coffee meetings with the mentees and for pupils in Germany via the Cybermentor programme, which offers the opportunity to mentor female and non-binary pupils remotely.  Becoming part of a mentoring scheme is also some of the advice I would give to my 18-year-old self too — to look out for networking opportunities and mentorship programmes; to find people who have chosen a similar path. 

I am now very excited to start my new position as a Postdoctoral Research Fellow at the University of Oslo where I will be continuing to work on developing new methods for machine learning research.

Published on February 11, 2026



Posted by HMS in Stories
12
Mar
2025
Catherine Micek

Catherine Micek

Born in United States • Studied PhD in Mathematics at University of Minnesota in Minneapolis, United States • Lives in United States • Occupation Data Scientist

Galileo Galilei said “Mathematics is the language with which God has written the universe.” I chose to have a career in mathematics because I wanted to be a “translator” for the language of mathematics. 

The first time I realized that I might enjoy teaching math was when I was in sixth grade.  I was writing up a solution to a pre-algebra problem for a school newspaper article, and I discovered that I loved breaking the problem down into smaller steps that could each be carefully explained. Communicating a logical and precise solution was beautiful to me.

When I went to college, choosing a major was tough because I was curious about many subjects. What drew me towards math during my freshman year was the idea of becoming a college math professor. A career as a math professor would allow me to combine the challenge of solving math problems as well as communicating the results.  Furthermore, the fact that mathematics could be applied to a variety of fields appealed to my widespread curiosity. During college, I studied applications of math to some familiar and loved subjects (such as music) as well as some new and interesting ones (such as computer science). I majored in math and minored in physics and computer science with the goal of pursuing a Ph.D. in applied mathematics upon graduation.

Graduate school was very different from my undergraduate studies. The coursework was more demanding, so I had to improve my study habits, and research required that I develop an entirely new set of skills. The nature of research was very different from the syllabus structure of problem sets and exams in a course. Since my goal was to solve a problem no one had ever solved before, it required a creative and flexible approach, one that emphasized the exploration, experimentation, and steady refinement of ideas.  But perhaps the most important lesson I learned was that there is no single “correct” way to be a mathematician. I saw that fellow students succeeded by developing a process of learning and research that worked for their unique set of talents and interests. I, too, had to develop such a process, even though it was an arduous and intimidating journey, fraught with a lot of trial and error. Ultimately, though, the effort was worth it because it built my self-confidence.

Since my goal was to solve a problem no one had ever solved before, it required a creative and flexible approach, one that emphasized the exploration, experimentation, and steady refinement of ideas.  But perhaps the most important lesson I learned was that there is no single “correct” way to be a mathematician.

At the end of graduate school, I had an unforeseen change of plans. My goal had always been to get a tenure-track job (which is the career track to a permanent academic position in America) at a local school. However, since no local positions were open the year I was graduating, I had to consider the trade-offs between my geographic location and the type of job I wanted. If I didn’t relocate, I would have to broaden my job search to include non-academic jobs (which I didn’t know much about) and temporary academic jobs (which had more uncertainty). It was scary to consider changing my long-held career plans, but I had an established support system of family and friends locally who were an important part of my life. After extensive deliberation, I accepted a two-year faculty position at a local school and began investigating non-academic career paths.  

Luckily for me, jobs in data science were starting to surge around the time I started looking at industrial jobs. Companies were looking to hire employees who understood complex statistical and machine learning algorithms and could write computer code.  Data science was a great fit for my interests and skills – I had a lot of programming experience and was willing to learn whatever additional mathematics I needed for a job – so I began looking for jobs where I could use and further develop my technical skills.  

My first industry job was building statistical models for pricing policies at an insurance company, and from there I segued into data scientist and software developer roles. Although the domains are different and the mathematical techniques I use vary, my jobs generally have consisted of formulating the mathematical problem, writing the code to train the model and implementing the solution, and explaining the results to business stakeholders. I’ve worked as a data scientist at several companies on problems with diverse applications: energy, finance, supply chain, manufacturing, and media.   Although the details of my professional life are different than if I was a math professor – the work is interdisciplinary and team-oriented – I still get to be a “translator” of mathematics. 

Even though my career path has gone differently than I originally planned, I am happy with the unexpected directions it has taken me. Keep in mind that the best career path is not about what the majority is doing or what others advise that you “should” do: it is the path you create for yourself.

Published on March 12, 2025.
Photo credit: Catherine Micek

Posted by HMS in Stories
21
Apr
2021
Jamie Prezioso

Jamie Prezioso

Born in Warren, Ohio, United State • Birth year 1989 • Studied Applied Mathematics at Case Western Reserve University, Cleveland, Ohio, United States • Lives in Washington, D.C. United States • currently a Research Scientist

Growing up, I genuinely enjoyed math from an early age. I have fond memories of solving equations and homemade arithmetic flash cards with my grandfather. He consistently and lovingly encouraged me to pursue math. And so, I did.

I had an inclination that studying mathematics would open an array of opportunities, however, I had no tangible examples of this. Nevertheless, I was drawn to pursue math.

I happily studied and excelled in mathematics throughout middle and high school. When choosing a major in college, I did not even consider math. Having never seen or learned about modern-day mathematicians in school or media, I was unaware of this entire profession. Since I was also interested in medicine, I considered studying biology. I knew of clear academic and career paths in the medical field. Ultimately, my first year in college I was undecided. I had an inclination that studying mathematics would open an array of opportunities, however, I had no tangible examples of this. Nevertheless, I was drawn to pursue math. And so, I did.

I began to discover the ways you could use mathematics to solve problems I found interesting and important, like quantifying the effects of climate change or modeling predator-prey dynamics in fragile ecosystems. I graduated from Walsh University with a Bachelor’s of Science in Mathematics. When applying for graduate programs, I had every intention of obtaining a Master’s degree in a few years and leaving the program for industry. The thought of being in school for nearly all of my twenties seemed unbearable, if not impossible. I did not want to wait for my professional career, and in some sense my “adult” personal life, to begin. Still, I was excited to pursue math. And so, I did.

Through coursework and research, I found I was truly passionate about math. I developed strong quantitative modeling and coding skills. I even got to study areas of biology and medicine.

In the Fall of 2012, I began graduate school at Case Western Reserve University. I studied applied mathematics, taught Calculus to bright undergraduates and conducted research in mathematics and computational neuroscience. It was in graduate school where I grew both personally and professionally. I had many wonderful experiences with brilliant mathematicians from all over the world, many of whom I am still close with today. Through coursework and research, I found I was truly passionate about math. I developed strong quantitative modeling and coding skills. I even got to study areas of biology and medicine. I gained confidence in myself and a deeper understanding of mathematics. And so, I obtained a PhD in Applied Mathematics.

I use my background in mathematics to research machine learning (ML) and artificial intelligence (AI) models […]

Now, I am an Applied Mathematician. I am a Research Scientist at a consulting firm in the Washington, D.C. area. I use my background in mathematics to research machine learning (ML) and artificial intelligence (AI) models, focusing on interpretability and explainability. While AI/ML models have proven extremely useful on a variety of tasks, their inherent black-box nature and lack of interpretability limits their use in critical applications, like medicine or autonomous driving. Specifically, I research and develop neural networks, mathematical models which are typically highly over-parameterized but have exhibited superior performance on high dimensional data (e.g. images), trying to better understand how these models make predictions, assess their confidence and incorporate prior expert knowledge.

I feel very fortunate to have a career which aligns with my field of study and allows me to work on problems I am passionate and excited about. I hope that my story, and the stories of the other women here, highlight the vast number of exciting opportunities and careers in mathematics, the careers that I was unaware of for so long.

Published on April 21, 2021.

Posted by HMS in Stories