DataScience

29
Oct
2025
Association for Women in Mathematics at the SIAM/CAIMS 2025 Annual Meeting

Association for Women in Mathematics at the SIAM/CAIMS 2025 Annual Meeting

by Jamie Haddock & Anna Little

Introduction to the Association for Women in Mathematics (AWM)

The Association for Women in Mathematics (AWM) is a nonprofit professional society, founded in 1971, whose mission is to create a community in which women and girls can thrive in their mathematical endeavors and to promote equitable opportunity and gender-inclusivity across the mathematical sciences. The AWM has around 4500 members. Over 3000 of its members are students, many of whom belong to one of the over 130 AWM Student Chapters at their home institutions.  AWM workshops at U.S. national meetings such as the Annual meeting of the Society for Industrial and Applied Mathematics (SIAM) and the Joint Mathematics Meetings are organized by one or more of  the 26 active AWM Research Networks (AWM-RNs). AWM-RNs are intentional communities of researchers working in a common subdiscipline of the mathematical sciences in which senior mathematicians lead projects and mentor graduate student and early-career mathematicians. The program’s goal is to foster long-term collaborations and knowledge sharing, with each cohort of mathematicians helping to anchor the next in a successful mathematical career. 

AWM at the Society for Industrial and Applied Mathematics / Canadian Applied and Industrial Mathematics Annual Meeting in 2025

The Society for Industrial and Applied Mathematics (SIAM) Annual Meeting was held in conjunction with the Canadian Applied and Industrial Mathematics (CAIMS) Society in Montréal, Québec, Canada from July 28–August 1, 2025. As part of this conference, AWM hosted a series of events during the two-day AWM Workshop held July 28–29.  

The 2025 AWM Workshop was organised by the Women in the Science of Data and Mathematics (WiSDM) Research Network. Researchers in this network are broadly interested in problems motivated by working with real world data.  Topics of particular interest recently have included variational and deep learning models for image processing and computer vision, randomized iterative methods for tensor decomposition and regression problems, applications of optimal transport within biological data, and robust manifold estimation. The WiSDM Research Network has held four research collaboration workshops biannually since 2017.  

Co-organizers of this year’s AWM Workshop at SIAM/CAIMS: Jamie Haddock and Anna Little

The authors, Jamie Haddock and Anna Little, were invited to co-organize this year’s AWM Workshop after their participation in the 2023 WiSDM workshop at the Institute for Pure and Applied Mathematics (IPAM), and were excited to contribute to this important annual community-building activity.  

Picture of Jamie Haddock
Jamie Haddock

Jamie is the Iris & Howard Critchell Assistant Professor of Mathematics at Harvey Mudd College.  Her research focuses on data science, optimization, and machine learning, with particular interest in randomized iterative methods.  She is a three-time WiSDM workshop participant – she participated in 2019, was a project co-lead in 2023, and a project lead in 2025.   Additionally, she has been an active member of AWM since graduate school, organizing mentoring and research activities for early-career mathematicians, and is an active member of SIAM, including acting as secretary for the SIAM Activity Group on Data Science and sitting on the Organizing Committee for the SIAM Conference on the Mathematics of Data Science in 2024. 

Anna Little

Anna is an Assistant Professor of Mathematics at the University of Utah and her research interests include geometric and graph-based methods for high-dimensional data analysis and signal processing with group invariant features. She was a participant in both the 2017 and 2019 WiSDM events; the mentorship she received was extremely valuable in helping her establish a strong research trajectory, motivating her to serve as a research group leader at both the 2023 and 2025 WiSDM events. Together, Jamie and Anna sought to bring the collaborative and inclusive spirit of the WiSDM Research Network to the SIAM Annual Meeting.

Activities at the AWM Workshop at SIAM/CAIMS 2025

The AWM Workshop provided an opportunity for community building among participants across career stages and all research areas in applied and computational mathematics, and was comprised of several exciting events: a two-part minisymposium featuring several speakers from the 2023 WiSDM Research Workshop at IPAM, a mentoring luncheon where each student or postdoc participant met with their paired mentor, the AWM–SIAM Sonia Kovalevsky Lecture, a panel discussion with four mathematicians at a variety of career stages, and a minisymposterium in which graduate students and postdoctoral fellows presented their research and received feedback from mentor-judges.  Below, we dive more deeply into the career panel and minisymposterium to give readers a chance to experience what it was like to be at the workshop for themselves!

The career panel at the AWM Workshop was wide-ranging and candid, offering both practical strategies and personal reflections from mathematicians at different career stages. Panelists shared how they approach choosing research directions in data science, emphasizing the importance of reading survey articles broadly, engaging in interdisciplinary conversations, and being willing to pivot when a project stalls. They spoke openly about mentorship and sponsorship, noting that while careers can be built without strong mentors, cultivating a network of advisors and advocates can be transformative, especially in male-dominated spaces. On the ongoing challenge of balancing research, teaching, and service, panelists encouraged participants to practice saying “no” to requests that don’t align with their goals, to prioritize external professional activities that build networks, and to protect their time. They also addressed the stresses of uncertainty in today’s academic job market, urging students and postdocs to focus on what they can control, to build supportive networks, and to remain open to unexpected opportunities. Themes of burnout and imposter syndrome resonated strongly with the audience; panelists reminded participants to seek joy in their work, to accept that careers progress in nonlinear seasons, and to value the unique perspectives they bring to the field. The conversation concluded on an encouraging note: senior panelists underscored the responsibility and opportunity to make a difference “in the room” as one advances in a career, and urged participants to pursue problems, collaborations, and communities that inspire them.

The AWM minisymposterium for graduate students and recent Ph.D. recipients was very successful, and the room was full of exciting research.  The minisymposterium has become one of the most valuable components of the AWM Workshop, particularly for graduate students and postdoctoral researchers. For many participants, it is their first opportunity to present their work in a national forum and to receive feedback from established mathematicians outside of their home institutions. This format not only allows early-career researchers to refine their communication skills and develop confidence in sharing their results, but also fosters one-on-one conversations that often lead to collaborations, invitations to speak, or mentoring relationships. The supportive, constructive environment of the minisymposterium is especially impactful in helping young researchers see themselves as part of the broader applied mathematics community and in validating the significance of their contributions at an early stage in their careers.

The authors are deeply grateful to all who made the 2025 AWM Workshop a success. They found the workshop both energizing and inspiring and felt it was a privilege to help create a space where early-career researchers felt seen and supported, where mid-career mathematicians could share their wisdom, and where the entire AWM–SIAM community could come together.  Jamie and Anna left Montréal with new ideas and a renewed sense of the importance of intentional community building within mathematics. They encourage those interested in data science to join the Women in the Science of Data and Mathematics (WiSDM) Research Network, and hope to see many of you at the next AWM Workshop. 

Get Involved! 

AWM is a network of mathematicians who support women in the mathematical sciences, and all are welcome to join this community! To learn more about how to get involved with research groups, check out the AWM website. Don’t see your research field? Consider starting a network. Do you attend SIAM conferences and are you interested in being a mentor or poster judge? Contact the AWM SIAM Committee chair.  Social change doesn’t just happen, and neither do the programs!

Published on October 29, 2025.
Photo credit header: SIAM

Posted by HMS in Blog
15
Oct
2025
Anna Breger

Anna Breger

Born in Austria • Studied Mathematics and Music • PhD in Applied Mathematics from University of Vienna, Austria • Assistant Research Professor at University of Cambridge

It was the beauty of abstract aesthetics that first drew me to Mathematics. Finding calm and excitement in analytical thinking and mathematical problems, it has always been clear to me that I will study Mathematics at the University.

Being from Austria gives you the huge privilege to obtain great education for free at nearby universities. That is how, right after my final high school exam, I enrolled for my Maths undergraduate studies without even thinking about the future. I still remember very clearly one of my first Math lectures at the University of Vienna. In a room with hundreds of excited and nervous students, the professor took us by surprise: “Look left and look right, most of you won’t make it through the first study term!” Back then I encountered that as a challenge I was happy to participate in, today I wonder how such pedagogical manners could be acceptable.  

The excitement, the frustration, the joy – it felt like training acrobatics of the mind

A competition – that is how it felt the first years of studying and I dearly enjoyed the long hours studying and solving mathematical problems with my amazing colleagues that soon became close friends. The excitement, the frustration, the joy – it felt like training acrobatics of the mind and I embraced the clarity of pure Mathematics, presenting an undefeatable truth. 

What I have not told yet – alongside Mathematics I obtained another degree at a different institution, namely in music pedagogy for violin performance and later also studied early music with baroque violin. (In Austria you cannot obtain two majors or a minor in a different study area; now I think that this system would have fitted me much better.) I did worry a lot that people would not take me seriously either in Maths or in Music when they’d find out, and that is why I kept hiding my respective “second” identity in both communities for a very long time from most people. Luckily, I also met people that inspired me to keep up both interests and I am still very grateful for them. When I received a prestigious research fellowship towards the end of my PhD studies in Mathematics, for the first time I felt strong enough to speak publicly about my two paths. Often, I was asked: “So what will you choose? Maths or Music?” My answer has always been: “Both, of course!” 

Maths and Music gave me the perfect balance to challenge both my analytical and creative skills on an emotional and structural level

Maths and Music gave me the perfect balance to challenge both my analytical and creative skills on an emotional and structural level during my university studies. I could not have gone forward and succeeded in one without the other. Later, both activities gave me such amazing opportunities to travel and meet people, where often it benefitted both my professions! And lastly – this brings me right in the present – eventually I have managed to combine both professions in an interdisciplinary research project that I am now carrying out.

But first, back to my path in Maths! My first undergraduate course in mathematical image processing showed me how enjoyable it can be to visually experience the results of a mathematical solution. I began to love the idea of using mathematics to process or even create a digital image, and the satisfaction to see the result of a successful algorithm (for example to make a noisy image clearer). I kept this excitement and was very grateful to find a supervisor for a Master’s thesis on image analysis – the project even included medical images from a hospital! I had not planned to stay for a PhD, but when I was offered to stay in the research project, I felt excited to deepen my understanding of mathematical image analysis and applications further.

The calm that once gave me comfort in pure Mathematics I now found in the compromises that have to be made in translational research

Soon my passion for interdisciplinary research was released, and gradually I started loving the edginess that comes when applying Mathematics to real-world problems. The calm that once gave me comfort in pure Mathematics I now found in the compromises that have to be made in translational research when trying to bridge theory, application and task-based needs. 

Many little twists and turns have brought me to where I am now and I am absolutely thrilled about my interdisciplinary research project at the University of Cambridge, working on image analysis and historical music manuscripts. Having encountered various obstacles challenging my unusual path, I still would tell my younger self to delve into both passions, and I would advise everyone to stay true to themselves and feel free to go their own personal, individual path. 

Published on October 15, 2025.
Photo credit: Flora Wiederkehr

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
26
Feb
2025
Anna Ma

Anna Ma

Born in the US • Studied Mathematics at the University of California, Los Angeles • Highest Degree PhD in Computational Science from the Claremont Graduate University • Lives in the US • Occupation Assistant Professor of Mathematics at the University of California, Irvine

When I was a kid, there were lots of things I wanted to be: a lawyer, a teacher, a singer, and even, at one point, a maid (I loved organizing and cleaning as a kid, too!) The thought of being a professor, let alone a professor of mathematics, never crossed my mind. I enjoyed mathematics as a kid but wasn’t the “math wiz” in school. I simply enjoyed it. In other classes, I had to memorize all these seemingly random facts, dates, and names of cell parts and their functions. In math classes, all I needed to do was understand the underlying concept, and I would be able to solve many problems!

My first memory of just the thought of being a mathematics professor came in high school. I joined a class geared towards first-generation college students and presented a project on my dream career as a high school math teacher.

Around middle school, I decided to pursue mathematics as a career. My parents immigrated to the US as refugees during the Vietnam War and worked as nail technicians and factory workers so the only people I knew who “did math” were the math teachers I interacted with at school. Thus, I set my sights on becoming a high school math teacher. My first memory of just the thought of being a mathematics professor came in high school. I joined a class geared towards first-generation college students and presented a project on my dream career as a high school math teacher. One of my classmates turned to me after my presentation and said, “I think you’re aiming too low; I think you should be a math professor.” I told her there was no way I could ever accomplish that, and I left it at that. 

While trying to figure out what other careers existed for mathematicians, I stumbled upon Applied Mathematics and research: the wonderful world of creating new and exciting mathematics for real-world applications. [..] From there, I was hooked. 

In college, I began taking math classes beyond calculus: logic, analysis, algebra, combinatorics, and numerical analysis. Logic and Numerical Analysis were two of my favorite courses, and it occurred to me that if I were a high school math teacher, I’d never have the opportunity to “do numerical analysis” again. (Was I being a little dramatic? Yes. But did I know what I wanted? Also, yes!) While trying to figure out what other careers existed for mathematicians, I stumbled upon Applied Mathematics and research: the wonderful world of creating new and exciting mathematics for real-world applications. My first research project was to help develop an algorithm for the Los Angeles Police Department to clean reporting data automatically. Next, I worked on a project analyzing Twitter (now called X) data to categorize Tweets automatically into content-based topics that did not rely on keyword searches. From there, I was hooked. 

In college and grade school, it was difficult to see how intertwined mathematics was with the world around us. Through these projects, I began to see mathematics and the world through a new lens.  The realization that mathematical concepts and theory could directly impact and improve real-world problems is inspiring, and this shift in perspective not only enhanced my appreciation for mathematics but also fueled my passion for pursuing further research and applications that bridge theory with practice. 

In academia, you raise the next generation of mathematicians, discover and create new mathematics, and serve the scientific community and beyond.

Working in academia is an incredibly unique opportunity. In academia, you raise the next generation of mathematicians, discover and create new mathematics, and serve the scientific community and beyond. At the same time, academia can be really difficult because everyone has opinions about what you should and shouldn’t be doing and how you should and shouldn’t be spending your time. Early on, I decided I would do what made me happy. If that wasn’t enough for academia, then I wouldn’t be happy doing it anyway. There really is no other job like it in the world. Currently, I am working with multiple graduate students, recruiting new students for an undergraduate research project, writing proposals, and writing manuscripts to introduce new and improved algorithms and theorems to the mathematics community. One of the most surprising things I’ve discovered about this career is how much traveling I get to do. Every year, there is typically at least one international trip (Paris, France last year for the SIAM Applied Linear Algebra conference!) and a few domestic trips for conferences, visiting collaborators, and presenting research at other universities and research institutions. My day-to-day life in my career is never the same, which makes the work and life very exciting. 

Published on February 26, 2025.

Posted by HMS in Stories
16
Oct
2024
Michelle Snider

Michelle Snider

Studied Physics & Mathematics at Smith College, Northampton MA, and Mathematics at the University of California San Diego, CA USA and at Cornell University, Ithaca, NY USA • Highest Degree: PhD in Mathematics • Lives in United States • Occupation: Senior Data Analyst at SRT Labs and Adjunct Research Staff Member at Center for Computing Sciences, Institute for Defense Analyses

I was always interested in math and science, maybe because I was just good at it. I chose to go to a women’s college because even at the high school level, I had been the only girl in the class, and experienced some unhealthy dynamics that can occur in a gender-imbalanced environment. Finishing my double major in Math and Physics in an enthusiastic and supportive environment, I decided I was happy to keep learning for the sake of learning, so I started applying to graduate school. 

I went to the University of California, San Diego because they had big math and applied math departments, and since I didn’t have a specific area of focus yet, this would give me lots of options. Two years in, I realized I had not narrowed down the list of mathematical topics I was interested in so much as the list of professors I was not interested in working with. I set up meetings with potential advisors across the departments, who did work in numerical analysis, representation theory, combinatorics, and even math education research. Rather than giving me an impromptu lecture, one professor spent 5 minutes setting up a problem, then handed me the chalk and said “Go up to the board and work out an example.” I thought to myself, I guess I’m an algebraic combinatorialist now!

My specific expertise seemed to be less relevant than my willingness and ability to jump into new research areas and tackle hard problems.

Six years and a cross-country move later, I finished my PhD. I had determined that I didn’t want to pursue an academic track, but with such a pure math background, I wasn’t sure what other options I would even have. That is, in academia, it is quite common that you have no idea what else to do except be an academic because no pure math professor I have met has ever done anything other than be a pure math professor. While I had a wonderful opportunity learning how to think mathematically, I had no guidance about how to transition my research to real life. After sending my resume to companies and national labs across the spectrum of options, I landed an interview at the Center for Computing Sciences in Maryland, a federally-funded research and development center, where the organizational ethos seemed to be to hire a bunch of smart people and remove all the administrative distractions so they can just focus on solving hard problems for the US government. The people I met at my interview were excited about their work, but also had interesting hobbies and work-life balance. My specific expertise seemed to be less relevant than my willingness and ability to jump into new research areas and tackle hard problems.

The AWM is a community of mathematicians from around the world who care about building up a network to help us all succeed and I love being a part of it.

Along the way, I had an opportunity to join the Association for Women in Mathematics (AWM) on a day trip to the US Capitol in Washington, DC, to meet with the offices of elected officials and advocate for supporting underrepresented minorities in STEM.  The AWM is a community of mathematicians from around the world who care about building up a network to help us all succeed and I love being a part of it.  These visits give us a chance to let our voices be heard, and to bring awareness to the importance of STEM across society—many politicians have never met a mathematician before, and we had the chance to try to counter some of the stereotyped images in the media. I met an amazing group of mathematicians, and before I knew it, I was the one organizing these Capitol Hill visits, then serving on several committees. 

A few years ago, an opportunity came up to work with a small technology company with a great company culture. My job title is Senior Data Analyst, but again I was hired not for specific expertise but for my flexibility in taking on new challenges. I get to work across a broad swath of the company, talking to clients, designing solutions, and yes, analyzing some data along the way. I love being able to apply mathematical thinking to problems perhaps not thought of as classical mathematical problems, like how to help universities save energy by connecting their air conditioning system to their class scheduling system.

I could not have predicted the path that I’ve been on, and certainly would never say that I had a plan all along. I am happy to do lots of different things, but it matters a lot to me who I spend my time with. Picking each step based on the people I enjoy spending time with seems to be working just fine so far. 

Published on October 16, 2024.

Posted by HMS in Stories
01
Mar
2023
Anna Konstorum

Anna Konstorum

Studied Biology/Bioinformatics at McGill University, Canada, and University of California, Los Angeles, USA, and Mathematics at University of California, Irvine, USA • Highest Degree PhD in Mathematics • Lives in United States • Occupation Research Staff Member at Center for Computing Sciences, Institute for Defense Analyses

I came to applied mathematics slowly, and circuitously – but sometimes that makes for the best stories. When I was young, I fell in love with the complexity of biological processes, and thus I chose to study biology for my BSc. My grandmother was a math teacher and I have fond memories of us playing all sorts of educational math games growing up, which instilled in me a joyful, non-competitive view of math. But I never saw myself as a mathematician, it was just something I enjoyed ‘on the side’.

I sat there in complete astonishment of the beauty and power of math to describe a world that I had realized I had always wanted to see in a mathematical light.

It was only when doing my Master’s, when I took a course focused on using dynamical systems to study the life sciences, that I came to see that mathematics needed to be more than a hobby for me. I sat there in complete astonishment of the beauty and power of math to describe a world that I had realized I had always wanted to see in a mathematical light. And, I felt then, everything clicked. That my love for math and complex systems such as biology were not separate, but actually completely intertwined. It was this realization that led me to do my PhD in mathematics. I performed research modeling interactions of growing tumors with their microenvironment and took classes in a wide range of mathematical subdisciplines. It was very difficult as I knew I had less experience with mathematics than many of my peers, but I also had complementary skills in working on real-world scientific problems, which gave me a unique vantage point to think about the methods I was studying. When I kept my focus on the subject matter, I knew I was where I needed to be. It was one of the hardest, but most rewarding experiences in my life.

I work at the interface of data science and applied mathematics to help address challenging problem sets in national security, and more generally in the computational and data science realms.

Something you come to understand by taking a strong pivot, is that both you and the world have the capacity to honor a new stage in your life and career, especially if you approach the challenge thoughtfully and creatively. I had come to understand that for me, the next stage that I wanted to reach was to expand my applied mathematics capabilities to new domains in addition to the life sciences. And, really, I was ready! Studying the life sciences from a mathematical perspective prepares you to handle a variety of complex data problems. The field is full of extremely noisy data – but data that has, if you chip at it long enough, fascinating patterns and meaning underneath the noise. I now get to do just that as a Research Staff Member at the Center for Computing Sciences, Institute for Defense Analyses (CCS/IDA). I work at the interface of data science and applied mathematics to help address challenging problem sets in national security, and more generally in the computational and data science realms. I’ve used approaches ranging from applied dynamical systems (PDEs and ODEs) to, more recently, unsupervised learning methods employing matrix- and tensor-decomposition frameworks. I also hold an adjunct faculty role in the Laboratory for Systems Medicine at the University of Florida, which allows me to continue to collaborate on projects in mathematical and systems biology.

I wish I had known to take advantage of all [professional societies] have to offer earlier in my career.

What I’ve come to realize is that your unique interests and capabilities, even when they may not fit easily into a clear label, do have a place in this world where they will be valued. My background in mathematical biology has given me a unique perspective on the challenges I face in my current role, both from a mathematical and applied sense. And it makes for some fun intersectional research.

Finally, I’d like to make a quick shout-out to the power of professional societies. I wish I had known to take advantage of all they have to offer earlier in my career. Societies like the American Mathematical Society (AMS), Society for Industrial and Applied Mathematics (SIAM), Association for Women in Mathematics (AWM), and Society for Mathematical Biology (SMB) all provide opportunities to network via conferences and meetings, and to learn more about opportunities in and outside of academia utilizing the skills you learn. You don’t need a minimum degree to join – just an interest to connect with like-minded researchers.

Published on March 1, 2023.

Posted by HMS in Stories
22
Jun
2022
Karrie Liu

Karrie Liu

Born in Hong Kong • Studied Mathematics at University of York, UK • Highest Degree MSc in Applied Mathematics • Lives in London, UK • Occupation Freelance Mathematician / Founder of an analytical advisory company

Growing up in two distinct family cultures (Chinese parents in Hong Kong and “adoptive” English parents in the UK), I noticed that girls weren’t often encouraged in the same way that boys were. Many Asian parents would prefer that their daughters marry and focus on family rather than pursuing studies in higher education. Due to this, I wish to be a role model to younger generations, especially girls, so that they may be inspired and have the courage to follow their dreams. My ultimate goal is to improve the world through maths, data science and technology. Hence, that is why I set up an analytical consultancy company called the analytical advisory company Hypatia Analytics Ltd in 2019, which allowed me to spend more time on different types of charity work.

My ultimate goal is to improve the world through maths, data science and technology.

Since graduating from university, I have been applying my skills to continuously show people how they can use mathematics in healthcare and life sciences. During my tenure at the National Health Service (NHS), I participated in several diversity and equality projects. The NHS lacks information on ethnicity and I noticed that researchers had to use the general label “South Asian Name programme” to gather more details. I headlined a project discovering whether extra details can improve the name-test accuracy and to carry out diagnostics tests using patients’ self-reported ethnicity as the standard compared to test results. The outcome has been widely adopted in the Bradford/Leicester council area, improving NHS data and enabling valuable insights for local health economics planning.

Since data science is a relatively new type of career, many people haven’t yet fully understood why it is needed and how to apply it in the real world. Education is the key for people who want to be specialized professionals, but they also need to make the field accessible to the general population. My role as a trustee at The Institute of Mathematics and its Applications (IMA) allowed me to chair three national conferences to showcase how mathematics can be used with data science and helping others to get more help from the industry. 

My company [..] acts as an analytical advisor for charities providing statistical support for clean water programmes, using data science and technology to improve design and optimise resources needed to implement systems.

Skill-based volunteering is also very close to my heart; with my company Hypatia Analytics Ltd I have had the opportunity to voluntarily lead tech and maths projects engaging with the public and different charity organisations. Hypatia Analytics Ltd acts as an analytical advisor for charities providing statistical support for clean water programmes, using data science and technology to improve design and optimise resources needed to implement systems. The charity’s aim is for people’s lives to improve from having clean water close to their home. Hence, more children have time to attend school and the prevalence of illnesses is decreased.

In the summer of 2021, Hypatia Analytics Ltd in partnership with a charity promoting mathematics set up a Math & Data Summer programme called “Discover Data”. This program is a series of introductory workshops on how applied mathematics with real-world evidence can be used to address the world’s problems to students aged 14-17. However, the program  did not stop there, it had set up a monthly meeting to teach more, and we are now planning Summer 2022 face-to-face workshop.

I believe data and mathematics are at the heart of better decision-making and hope that people can benefit from it.

Published on June 22, 2022.

Posted by HMS in Stories
15
Sep
2021
Maylin Wartenberg

Maylin Wartenberg

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.

Published on September 15, 2021.

Posted by HMS in Stories