MathematicalStatistics

Amanda Minter

Amanda Minter

Born in UK • Studied Mathematics at Lancaster University in Lancaster, UK • Highest Degree PhD in Infectious Disease Modelling • Lives in UK • Occupation Director of Equations of Disease C.I.C.

Growing up, universities were always a bit of a mystery to me, my parents didn’t go to university. But I was encouraged by my parents and schoolteachers that going to university would be the path for me. I thought that going to university and studying would help me change the world for the better. I enjoyed maths from a young age, it was a subject which came naturally to me. I found the lessons easy, but then at university, studying maths, I struggled.

Whether it was the format of lectures or the more abstract topics, the subject I loved didn’t come naturally anymore. I worried that I had reached my limit in my understanding of mathematics. After a few disappointing grades, I knew something would have to change if I was going to get a good degree. I had to do something different – I had to learn differently. 

I knew with enough time I could figure out most things – or know when it was taking me too long and I should ask for help!

I wasn’t used to having to put effort into learning maths, but now I would attend classes, then practice, read several books, find examples online, until I understood the concept. In those years at university, I learnt how to learn. And it paid off, not just at university, but further down the line as well.

I stayed at my university to do an MSc in Statistics. Although I loved group theory, I wanted to work on something more applied.  Following my MSc, I started a PhD in infectious disease modelling. Studying for a PhD was all about learning new things, and now I had learned how to learn. I knew with enough time I could figure out most things – or know when it was taking me too long and I should ask for help!

In universities I had been aware of being a first-generation university goer and of not having been to private school, and also of being White.

After my PhD, I stayed at university to do research applying mathematics to the problems of global health, but I found myself becoming disillusioned with academia. As a postdoctoral researcher I worked on some amazing mathematical problems and with some great scientists modelling infectious diseases. But I found myself reflecting on my place in global health research. In universities I had been aware of being a first-generation university goer and of not having been to private school, and also of being White. But I never really thought about what it meant to be White, British, and working in global health. 

My definition of success has changed a lot from starting at university and wanting to change the world with maths.

I was motivated to work in infectious disease modelling to use maths for good, but in my role as a postdoctoral researcher I felt I was not helping to support the decolonisation of global health. I decided to leave academia to set up the social enterprise I run now. I aim to create accessible training opportunities for learners in the Global South.

My definition of success has changed a lot from starting at university and wanting to change the world with maths. And to the aspiring mathematicians, the struggling ‘not a mathematicians’: know that the path to success is not linear, or even constant, but something which keeps changing the more you learn.

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Karem Guzmán Elgueta

Karem Guzmán Elgueta

Born in Los Vilos, Chile • Birth year 1990 • Studied B. Sc. in Mathematics and ​ B. Sc. in Mathematical Engineering at Universidad de Santiago in Santiago, Chile • Highest degree Master in Statistics • Lives in Santiago, Chile • Occupation Financial Advisory Consultant

My relationship with mathematics began when one day in high school a classmate asked me: “Karem, you are very good at mathematics, have you thought about studying it at university?” I will never forget this question because it was this one that made me aware of my love for mathematics. Since I was little, I had a knack for numbers and the subject always entertained me, so I decided to formally continue studying Mathematical Engineering at University.

During college, mathematics opened a new world, a lot of theory and logic work. It was not easy, including many hours of study and frustrations, and not always achieving good exam results. It was a long and hard road, but with resilience I managed to finish successfully.

I liked the time flexibility these jobs gave me, but they didn’t make me happy, as I felt a sense of intellectual emptiness. This is why I decided to go back to university to pursue a Master’s degree in statistics and you can’t imagine how much I liked it!

After graduating, I worked for two years as a high school teacher and as an assistant professor at the university. I liked the time flexibility these jobs gave me, but they didn’t make me happy, as I felt a sense of intellectual emptiness. This is why I decided to go back to university to pursue a Master’s degree in statistics and you can’t imagine how much I liked it! I was fascinated by the subjects associated with models and their theories (data mining, predictive modeling, supervised and unsupervised learning as well as time series, among other models), so today I am a lover of statistics.

Could you imagine what would happen if one day you make a withdrawal from your card and the bank denies it for not having funds? A systemic shock would surely occur, so these models are essential.

I am currently working for a professional services firm and I have experience in credit risk modeling projects, e.g. provision models under local regulations and International Financial Reporting Standards, countercyclical provision models, forward looking models, management models such as admission, behavior and collection, as well as in liquidity risk projects, e.g. construction of flow projection models/methodologies. Credit risk models, in general, aim to mitigate the risk of non-compliance with contracted payments, so provision models estimate an amount of money that a financial institution could eventually lose if all its customers decide not to pay. Management models, such as admission models, aim to estimate a client’s ability to meet their payment obligations, and thus help the financial institution to select its clients. On the other hand, liquidity risk models seek to ensure sufficient cash flow to comply with all normal operations associated with a financial institution: deposits, drafts, transfers, withdrawals of investment funds, etc. Could you imagine what would happen if one day you make a withdrawal from your card and the bank denies it for not having funds? A systemic shock would surely occur, so these models are essential.

My work is dynamic and very demanding, but I love it because I never do the same thing. There are always new projects and clients to care for, so I enjoy my work every day. I invite all women who like mathematics to dare and study it without fear, and if you also have an inclination for finance, then the world is yours!

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Dr Beate Ehrhardt

Dr Beate Ehrhardt

Born in Walsrode, Germany • Birth year 1987 • Studied Mathematics in Bremen, Germany • Highest Degree PhD in Mathematical Statistics • Lives in Bath, UK • Occupation Mathematical Innovation Research Associate at Institute for Mathematical Innovation, University of Bath

I am a 33-year-old applied mathematician and data analysis expert with a PhD in Mathematical Statistics from University College London. I hold a permanent, research-only position at the Institute for Mathematical Innovation (IMI) at the University of Bath. Before joining the IMI, I worked as a Senior Research Statistician at the global pharmaceutical company AstraZeneca. I have a 2-year-old daughter and am expecting my second child any day.

Growing up with two sisters and a brother, my father never told us there was a difference between boys and girls. Instead, he instilled in us an understanding that we can achieve what we want with hard work. As a result, whenever people tell me I cannot do something I take it as a challenge rather than a dead-end.

I love mathematics. I love learning. I love people. And I love science. But most of all I love when all of these four things come together. 

Ask for advice but know how to interpret it

Any kind of advice you receive from others says much more about them than about you. When I was deciding what to study after my A-levels, a teacher for advanced maths advised against “studying mathematics because it is too hard”. He was wrong. I loved every second of my undergraduate programme in mathematics. All of a sudden I was surrounded by like-minded people and could solve riddles day in and day out. Studying mathematics was the best choice for me. It was intense – and yes – it was hard work, but it was so rewarding. I learned to describe the world in equations, see the world in trends, identify patterns, and extract information from all the noise. I found a way to explain the world and found out I was really good at it! Looking back on it now, I understand that my teacher was not judging whether I would be good enough to study mathematics but rather was projecting his own experiences and difficulties studying maths. That is why I would suggest: Ask for advice but know how to interpret it.

I particularly enjoyed the statistics part of my undergraduate degree but wanted to understand further the maths behind it. So, I decided to pursue a PhD in mathematical statistics. Having been abroad to Cardiff, UK for an ERASMUS exchange during my undergraduate, I knew I wanted to be in an international environment surrounded by people from many different backgrounds and cultures for my PhD. When I heard about a PhD position at University College London on the mathematics of networks I was immediately intrigued. Before signing up, I met twice with my future supervisor, which was an incredibly good opportunity to get to know him and his team a little bit. I believe the PhD experience is strongly influenced by the research group you are joining and thus, I would very much recommend trying to find out about them as much as you can. In contrary to the common stereotype that a PhD in mathematics is lonely, I experienced quite the opposite. I joined a small research group of brilliant colleagues – some of whom I still call up nowadays to discuss research ideas, and I also was part of a cohort of PhD students that formed a support network for each other. There was always someone to discuss Maths with, or to join me for a pint when a break was needed.

(…) the very best you can do for you and your career is to discover what gets you out of bed in the morning with a smile

During my PhD, I discovered my talent for proving theorems, and there were multiple opportunities to do a Postdoc on related topics. However, being good at something does not always mean it is what you enjoy doing most. At UCL, I was fortunate to be exposed to many different types of research, which enabled me to understand that what really fascinates me are the insights one can draw from data and the corresponding impact rather than the actual tools used. So, after four years of carefully building a network and investing time and effort to build a strong foundation for a research career, I made (what felt like) a radical decision to leave academia and to join the research-end of industry where I can apply my knowledge to add insights to science with an immediate impact to the real world. Many colleagues and friends were shocked by my move including the research group I was part of, which made the decision even harder.      

Now, five years after finishing my PhD, I know it was undoubtedly the right move for me for two main reasons. First, the line between industry and academia is not as rigid as I thought. The move from a research-in-industry position back to academia is increasingly common, and the work I do now at the Institute for Mathematical Innovation is from a mathematical point-of-view very similar to my work at the pharmaceutical company. Second, and most importantly, the move enabled me to experience research in a very applied setting. Most of the work I have done post-PhD has involved engaging with multi-disciplinary teams working together towards an overarching goal. Each new project comes with its own data analytical challenges while at the same time allowing me to learn about research in a variety of disciplines. Whether it is tiny scissors that allow us to edit DNA (called Crispr Cas9) or contributing to our knowledge about the growth of black holes, the work is always fascinating. Everybody’s motivations are different and the very best you can do for you and your career is to discover what gets you out of bed in the morning with a smile.

Posted by HMS in Stories