WomenInMaths

21
May
2025
Laura Lewis

Laura Lewis

Born in China • Studied mathematics and computer science at California Institute of Technology (Caltech) in USA • Master’s in mathematics from University of Cambridge in UK • Lives in USA • Quantum information student, pursuing PhD at the University of California, Berkeley

Throughout my educational journey meandering through pure math, theoretical computer science, physics, and ultimately arriving in quantum information, I’ve seen that all these fields have deep foundations in mathematics, regardless of their outward label.

Early in life, I was drawn to math for its concreteness. To add two numbers together, there was a fixed set of rules, in contrast to other subjects we learn in elementary school, e.g., spelling which (especially in English) has many arbitrary rules and exceptions.

I was lucky to have a previous college math professor as my high school math teacher. He taught advanced math courses not typically covered in the high school curriculum, e.g., real and complex analysis.

With this initial interest, my experiences during high school solidified it and greatly influenced my academic path. I was lucky to have a previous college math professor as my high school math teacher. He taught advanced math courses not typically covered in the high school curriculum, e.g., real and complex analysis. With this, I was able to get a head start on math and got a glimpse of how it is explored in higher education: less through calculations and numbers, but with proofs.

Another pivotal experience was when I attended a program at the Massachusetts Institute of Technology (MIT) during the summer of my junior year in high school. There, I was challenged with advanced courses and projects but, perhaps most importantly, it was where I was first exposed to quantum mechanics. It immediately fascinated me due to its mystery, where even the first axioms are still debated. This is especially in contrast to other high school physics subjects, e.g., kinematics and electromagnetism, which are taught as having already been solved. This first experience with quantum mechanics planted a seed which would grow in college.

I double majored in pure mathematics and computer science, and as a part of the freshman seminars, one professor mentioned the intersection of these fields with quantum physics: quantum computing. I was fascinated.

When I started my undergraduate degree at the California Institute of Technology (Caltech), I kept in mind my previous exposure to quantum physics and kept my eyes peeled for any interesting opportunities. I double majored in pure mathematics and computer science, and as a part of the freshman seminars, one professor mentioned the intersection of these fields with quantum physics: quantum computing. I was fascinated. This subject would allow me to explore my interdisciplinary interests in math, physics, and computer science, and I thought it was a great fit. That summer, I reached out to the professor and started a project with him on how to efficiently check the correctness of a powerful quantum computation using only your laptop. With this experience, I saw how important a strong mathematical foundation is for this type of research, which focuses on rigorously proving the security of such verification protocols.

It was also at this point in my education where I started to notice the gender imbalance in math and quantum science, where I was the only female pure math major in my year in undergrad. This was not at all specific to Caltech but representative of the field as a whole.

During my undergrad, I also worked on designing machine learning algorithms to predict  ground states. A ground state is the lowest energy state of a system, where one can think of a ball lying at the bottom of a bowl. A good understanding of ground states can provide us with insights into different properties of quantum systems, so this is an important problem in quantum physics. In this project, I was able to leverage my mathematical background in analysis to provide rigorous theoretical proofs on the performance of my algorithms. It was fascinating to see how math could help pave the way for novel scientific exploration in important physics problems. I received the Barry M. Goldwater Scholarship for my research (awarded to undergraduates in the USA for outstanding research), which increased my confidence to pursue the subject further.

It was also at this point in my education where I started to notice the gender imbalance in math and quantum science, where I was the only female pure math major in my year in undergrad. This was not at all specific to Caltech but representative of the field as a whole. I hope that by continuing to pursue a research career, I can inspire other young women to follow their passions and dive into mathematics with confidence.

After college, I pursued two master’s degrees in the UK through a Marshall Scholarship (awarded to recent college graduates from the USA to perform two years of graduate study in the UK). The first was at Cambridge in mathematics, a course which is well-known for offering an extensive array of advanced math classes. The second is a research degree at the University of Edinburgh in computer science, where I am free to explore a research topic of choice. These past two years have allowed me to hone my research interests and learn new mathematical tools to attain these goals. Soon I will start my Ph.D. at University of California, Berkeley, focusing on quantum information, and I’m excited to see where my pursuit of mathematics leads me next in advancing our scientific understanding of the universe.

Published on May 21, 2025.

Photo credit: Daniel Chen

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