Blog

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!

Date published: Oct 29, 2025

Image credit header: SIAM

Posted by HMS in Blog
17
Sep
2025
Poetry as Lens: Two Historical Women Mathematicians

Poetry as Lens: Two Historical Women Mathematicians

by Jessy Randall

In 2015, I attended a talk on Sarah Frances Whiting given by Colorado College physics professor Barbara Whitten. Whiting was part of a group of women who cataloged stars at Harvard University in the late 19th century. I sat in the audience scribbling notes about these amazing women – I always listen better with a pen in my hand – and got particularly interested when Whitten showed a slide of a murky blob, an example of the star photographs these women used in their work. To my eyes, it looked like nothing much. Really just a smear.

After the talk, I looked up additional information. (Did I mention I’m a librarian?) I learned that one of Whiting’s colleagues, Annie Jump Cannon, cataloged hundreds of thousands of stars and, years later, could still recognize and identify the blurry photos she’d used doing that work. I pictured her being shown a blobby shape and saying “Oh, yes, that’s good old XZ437583, I remember that one.” Or “Hello, PQ2843b!” I started writing poems about Cannon, persona poems written from what I imagined to be her point of view. 

Annie Jump Cannon (1863-1941)
(first appeared in Asimov’s)
 
1. Annie Jump Cannon Cataloged Stars
 
Annie Jump Cannon
cataloged stars.
 
The work was tedious.
The pay was terrible.
 
But every day for forty years
she went to work
and held the universe together.
 
2. Annie Jump Cannon Goes Home from the Lab
 
She can’t stop seeing them:
the photographs,
black and white smears of stars.
 
They look like throwaways.
They look like nothing.
But not to her, to her they’re clear
 
as alphabets, because 
she’s good at what she does.

Her name led to others, and I found myself spending my lunch hour in the science section of the Colorado College library stacks. Soon my desk was piled high with thick books about historical women in STEM fields. There were SO MANY MORE women scientists and mathematicians than I ever knew. Than I ever dreamed! I wrote a lot more poems. 

Around this time, some powerful men bragged that with enough fame and power, they could get away with anything, including sexual assault. This made me really mad. So mad that I decided to do something about it! But as a librarian and a poet, what could I do, exactly? Well, I wrote a collection of poems about historical women in STEM fields. Maybe not the most effective form of resistance, but the project staved off my feelings of helplessness and hopelessness. 

I got particularly attached to a set of “biobibliographic source books” edited by Louise S. Grinstein,* and went through them page-by-page looking for women to write about. Frequently, some detail of the lives or work of these women leapt out as a perfect metaphor. When I read the entry on Soviet mathematician Nina Bari, the phrase “mathematics for ladies” led to this poem:

Nina Karlovna Bari (1901-1961)
(first appeared in Strange Horizons)
 
After the revolution, she passed the boys’ exam 
and became the first woman in the Luzitania,
students of Nikolai Luzin known for 
their interest in a new kind of math,
 
descriptive math, something more like 
philosophy, sometimes described as
mathematics for ladies.
 
This particular lady worked on functions
converging “almost everywhere”
(a precise term in trigonemetricheskii)
 
and she herself converged almost everywhere, too:
Paris, France; Lvov, Poland; Bologna, Italy;
even a mountain pass named for her lover, Nemytski,
whom she later married.
 
She married Nemytski, but some say her real love
was Luzin. They say she was despondent at his death,
and that when she was not yet sixty (in other words, 59), 
she threw herself in front of a Moscow Metro train.
 
There’s no way to know. It could have been an accident.
But when a woman had made her name calculating 
functions that converge almost everywhere,
we have to think she knew what she was doing.

In the 1920s in the Soviet Union, “mathematics for ladies” (математика для дам / matematika dlya dam) was a derogatory term for descriptive mathematics such as function theory. As in English, the word for “ladies” in Russian has a different connotation than the word for “women” – ladies (дам / dam) being more fancy and posh, not the Soviet ideal of real-world working women (женщины / zhenshchiny). 

I found this idea pretty hilarious. A special kind of math just for women? Different math for different genders? Aren’t numbers the same for everyone? Numbers and formulas and equations don’t change depending on your gender. But then I remembered those statistics about the gender pay gap – that for every dollar a man makes, women make 84 cents, or 90 cents, or 66 cents, depending whom you ask. 

In Bari’s time, math was supposed to be for practical purposes: engineering, architecture, and so on. “Mathematics for ladies” wasn’t real math – real mathematicians considered it to be useless, more like philosophy than math. Much has changed in the past hundred years. Abstract math, or pure math, is now standard in college and university math programs, and pure math scholars receive as much respect as their comrades in applied math (if, perhaps, not always as much funding).

For more information about the books, links to online poems, and subject indexes, visit http://tinyurl.com/MathforLadies.

Literature

*Women of Mathematics: A Biobibliographic Sourcebook. Ed. Louise S. Grinstein and Paul J. Campbell. New York: Greenwood, 1987.

*Women in Chemistry and Physics: A Biobibliographic Sourcebook. Ed. Louise S. Grinstein, Rose K. Rose, and Miriam H. Rafailovich. New York: Greenwood, 1993.

*Women in the Biological Sciences: A Biobibliographic Sourcebook. Ed. Louise S. Grinstein, Carol A. Biermann, and Rose K. Rose. New York: Greenwood, 1997.

About the author

Born in Rochester, New York, USA. Studied English Literature at Columbia University in New York City. Master’s Degree in Library Science from UNC-Chapel Hill. Lives in Colorado Springs, Colorado. Curator of Special Collections at Colorado College and author of two collections of poetry about historical women in STEM fields: Mathematics for Ladies (Goldsmiths Press, 2022) and The Path of Most Resistance (Goldsmiths Press, 2025).

More information: http://tinyurl.com/MathforLadies

Date published: Sep 17, 2025

Image credit header: Nethery Wylie

Posted by HMS in Blog
13
Mar
2024
A Grandmother’s Lasting Gift

A Grandmother’s Lasting Gift

by Anna Konstorum

When the team at Her Math Story reached out to me to share my story, I was honored for the opportunity. I wrote about my non-linear path through biology, biomathematics, and data science. I also wrote about my early inspiration to do mathematics, which came in part from the time I spent solving mathematical problems with my grandmother during my childhood. After I submitted my story, I thought more about the purpose of Her Maths Story, and about my grandmother. The stories shared by the participants of Her Maths Story are meant to inspire, and to show women the many paths taken by those that came before them, and to also give voice to stories that may not have been told otherwise. And, I thought, the woman who most inspired me to do math has a story herself worth sharing, which I am once again honored to do in these spaces.

My grandmother was a high-school mathematics teacher and eventually vice principal for the majority of her career. She was a kind, warm woman, who shared her love of math with me through fun mathematics games and activities, creating a space where math was something to look forward to and enjoy in good company. Yet, her kind and gentle nature belied an incredibly difficult life, her family lived through Stalin’s Great Purge and the Holocaust, and had to grapple with rising anti-semitism in Stalin’s post-war Soviet Union. Through all this, she was able to pass on to her children and grandchildren a gratitude and appreciation for the many beautiful things life has to offer, and part of that inheritance that I am incredibly grateful for was the love of mathematics that she shared with me. I share her story here in hope to inspire readers to see the beauty of what they are working towards, whether in mathematics or elsewhere, in spite of the difficulties we all face. I want to share the richness of the inheritance she left me.

My grandmother, Innessa (Inna) Bashneva, was born in 1923 in Minsk, Belarus, or what was then the ‘Byelorussian Soviet Socialist Republic’, only recently born itself after the Russian Revolution and ensuing civil war. She came from a Jewish family, indeed, her father’s original last name has been Rutgauz, but as a young revolutionary, his codename had been ‘Bashnev’, which means turret in Russian. He eventually changed his last name to this, a not uncommon practice at the time. By the time Inna was born, her family had managed to find stability in the new regime, with her father working for the local government and her mother staying at home to raise her.

Everything changed during what became known as Stalin’s Great Purge in 1937-1938, where in his quest to consolidate power, Stalin ordered the deportation, imprisonment, or execution of real or suspected rivals. One night in 1937, with only the children at home, the secret police came to take my great-grandfather away, the family found out later that he had been shot. Immediately after, her mother Esther went to stay with friends for her safety, and 14 year-old Inna had to care for her brother for several months while her mother was in hiding. When Esther returned, she went back to work as a nurse to support herself and her children.  The family was branded as ‘vragi naroda’ (enemy of the people), which would make it more difficult to obtain a good education, jobs, or healthcare.  Such families were shunned out of fear of association, Inna had many friends in school who had also been branded as such, and they had to rely on each other for comfort and friendship.

My grandmother at age 18, just before the war broke out.

I will pause my story to explain that she never spoke about these, and ensuing, events with me, they were shared with me by my father. But if I calculate correctly, it must have been during this tumultuous time, at the very latest, that her love for math must have been born, for in a few years time, and under very difficult circumstances, she would make the choice that would lead her to spend the rest of her life doing and teaching mathematics.

In late spring of 1941, my grandmother attended her high school dance with other students to celebrate the end of the school year, and of their high school education. The next day, the Germans invaded, and the family had to flee. Her younger brother was in a summer camp at the time, and they did not see him, or know of his whereabouts until after the war. They fled first by foot, then by train, and ended up in the town of Biysk in central Russia, located 4,500 kilometers east of Minsk.

Although my grandmother was now a refugee from the war, she was also still a young, ambitious, and intelligent woman who had just finished high school. She and her mother would spend the next four years in Biysk, and Inna enrolled in the Biysk Teachers Institute (which is now a University) to study mathematics education. When the war ended in 1945, the family returned to Minsk, where my great-grandmother and her sisters, whose husbands had also fallen victim to the Great Purge, pooled their resources together to build a house for themselves and all their children. Inna, upon her return, enrolled in the Belarusian State University to continue her education in mathematical pedagogy.  Gregory Konstorum, a friend who had been at their high school dance, had made it back from the front only in 1946 as he had been recovering from injuries he had sustained during the war. When he returned, he found that he had lost most of his family to the German occupation. He and my grandmother were reacquainted, and married shortly thereafter.

A school trip to Moscow with teachers and students. My grandmother is center bottom. My father, who also attended the same school, is just behind her.

After Inna graduated from University, she became a high school mathematics teacher, eventually rising to the role of vice principal. In addition to teaching classes in algebra, geometry, and calculus, she also taught more advanced students elements of probability theory and programming, as well as helped to prepare them for mathematics competitions and to take entry exams for some of the top technical universities in the U.S.S.R.  I once asked my father if she ever considered furthering her own education or doing research in mathematics. I remember a pause, and then a reminder that her family were still ‘vragi naroda’, that she was lucky to have had such a good education and a good job, especially as my grandfather struggled to find work after studying for a law degree due to another Stalinist clampdown, this time on Jewish intellectuals. I understood.

She was beloved as a teacher and a vice principal. Long after her retirement, she would correspond regularly with several former students by mail. She was known for her kindness, her generosity, and her goodwill. Upon her retirement after almost 40 years of teaching mathematics, she focused all her energy on helping her sons with their young families (I had been born just a year earlier).

In the early 1990s, my parents and I immigrated to the United States. It was a difficult and risky process, and yet almost immediately after we arrived, my father started laying the groundwork for Inna and Esther, who were still in Minsk, to join us in our new home. In the meantime, I was struggling. The long immigration process left me feeling uprooted and confused by all the changes, which slowed down my progress in learning English, and in making new friends. I didn’t fully comprehend the geopolitical forces that led my family to make this great leap in order to give me the chance for a better future in the U.S.  My parents at the time were working hard to get a foothold themselves into this new world, and while I didn’t want to make things harder for them by acting out, seeing their challenges may also have prevented me from reaching out to them for more support. 

About three years after we moved, Esther and Inna were finally able to join us, and they moved in with us. I watched in amazement as my grandmother, approaching her 70th birthday, leaned into her new surroundings, and began to learn everything she could about her new world, taking me with her in the process. She would listen to the radio for hours working to pick up the nuances of the English language. Whenever we went to the store, she would talk with the cashier, practicing her newly learned language skills.  She embraced with open arms what I was so determined to reject.

My junior high school ‘graduation’, with my grandmother standing proudly by as I display my Honor roll ribbons.

And then, there was the math. Possibly readers will think of a stereotype of an Eastern European mathematics teacher, drilling her sole pupil to the pinnacle of mathematical achievement. That was not her though, she understood that was not what I needed. Instead we would pick out math games and puzzles at specialized stores that we could work on together at home. Back at the house, we would sit on the floor, unpack our new purchases, and play. I recall that she would only converse with me in English while we worked through the problems in order to also strengthen my language skills, even though it was more difficult for her. While I no longer remember the specific problems we worked through, I do remember the feeling of joy, peace, and challenge as we made our way through the puzzles.  I remember my anxiety falling away in those sessions, replaced by a focus and engagement with the problems.

My grandmother passed away when I was 14 years old, after a long battle with a cancer that returned after a first diagnosis in the former U.S.S.R. As per her character, throughout this time, she maintained a kind and graceful disposition. As if the cancer, just like the Germans and the Soviets, was unable to change or quench her generous and good spirit. In the four years that she had lived with us, I had transformed from a cynical young girl to an Honors student with many prospects and dreams for the future.  Her role in that transformation was vital.

Recently, I have begun to think anew about the time Inna spent studying mathematics in Byisk, as war was raging all over Europe and in her home country. I wonder if during those unimaginably dark times, she found respite in the beauty and joy of studying mathematics. That she found something that she could do, enjoy and share, and that would help guide her way forward. And then many, many years later, when she found her own granddaughter in need of a ballast, she gave her the gift she had so much needed and received in those times.

Although my career in applied mathematics is a journey that has had many highs and lows, I find that seeking out the joy in my work helps to center and focus my thoughts and priorities – it is as if my grandmother is still reminding me to stay with the beauty and challenge, that it can help to guide my way forward.  And for that gift, I am forever grateful.

Posted by HMS in Blog
10
Jan
2024
Top 10 Tips for Applying for PhDs

Top 10 Tips for Applying for PhDs

by Kaitlyn Louth

Applying for a PhD can be a very daunting process. But worry not because if you’re considering applying for one and don’t know where to start, here are my top 10 tips to help you prepare and increase your chances of securing the right position for you:

1. Research different types of PhD programmes: There are a few different types of PhD programmes, e.g. applying for a specific advertised project, a programme with integrated study where you continue to take modules, or applying by submitting your own research proposal. So it’s important to research the differences and which type of programme would suit you best! For example, if you know that PhD study is for you, but are unsure on the exact area of research, then consider applying for a programme with integrated study, where you have the opportunity to ‘test out’ rotation projects in the first year before deciding on your thesis.

2. Identify potential supervisors: Talking to your current supervisor, mentor or professor is a great starting point for finding potential PhD supervisors or projects. With their expertise, they should be able to offer suggestions for people who are working in your areas of interest, or forward open PhD position opportunities to you. Checking websites such as findaphd.com, where you can subscribe to a mailing list about positions, or europeanwomeninmaths.org that have a newsletter with open positions, are good ways to hear about what is available out there. You can also directly look for professors who are conducting research in areas that interest you, read their publications and familiarise yourself with some of their research. Reach out to them to see if they are accepting new students. Be sure to mention why you’re interested in working with them and ask what their application process looks like. European Women in Maths also have a mentorship scheme which is very supportive if things can become a bit overwhelming; it is becoming more and more common to have mentorship schemes within universities too. 

3. Polish your application materials: When applying for a PhD, it’s important to submit polished and professional materials. Your CV should be up to date and tailored to each programme you’re applying to. Equate Scotland provide women in STEM with a free CV review service, and STEM Graduates have some great tips on how to structure CVs such as what headings to include. Be sure to include any relevant experience, especially research experiences such as internships, placements and any publications (although remember publications are definitely NOT necessary to secure a PhD position). Your personal statement should explain your research interests, why you’re interested in pursuing a PhD in mathematics/statistics, and how your background and experience make you a good candidate for the programme. Remember that your final year research project at university is of course research experience. Reach out to your local student career service too, they can provide advice on application materials for higher education opportunities, not just for industry jobs!

4. Request letters of recommendation: You’ll likely be asked for a couple of references from those who can speak to your academic abilities, research experience, and potential as a mathematician or statistician. Choose recommenders who know you well and can provide specific examples of your strengths, these will typically be your academic supervisor or academic tutor. Be sure to give your recommenders plenty of time to write their letters because there are often strict deadlines, and provide them with any information they might need to write a strong letter, such as your CV and personal statement. Also it can be useful for them to know details of the programme, such as the types of candidates they’re looking for, so they can tailor their reference letters. 

5. Take additional maths courses or undertake extracurricular research: Consider taking additional courses online or take the time to do independent study on any areas which interest you research-wise, and which fill knowledge gaps from your undergraduate course. In particular, learning how to code in a new language is extremely beneficial to strengthen any application. You can also consider looking out for opportunities to do research experience. This could be an extended summer project at your home institution, or a research internship elsewhere, for example. This will give you a taster of whether you would enjoy research, and is a great addition to your application and an interview conversation point. These things are definitely optional and dependent on time constraints, so even wider reading around your areas of interest will strengthen your application.

6. Attend information events and open days: Things such as information fairs, university open days and insight events for PhDs are really great to hear directly from current PhD students and their experiences, as well as network with other mathematicians in the same position as you. For example, The Piscopia Initiative holds an annual forum every year that discusses the experiences of women and non-binary students doing PhDs, as well as providing invaluable advice on the application process. They provide travel funding for this too! Open days are really useful to obtain a feel for the university, its people and the city itself. The people you work with and the environment you work in are super important to consider. Also, PhD Your Way is an annual, online event aimed at people from underrepresented groups in mathematics who want to understand all there is to know on applying for a mathematics PhD, with opportunities to speak to current students from various universities.

7. Be prepared for interviews: If you are invited for an interview, be prepared to discuss your research interests, relevant coursework, and any relevant experience you have. Be sure to research the programme and faculty ahead of time so you can ask informed questions. You might also be asked about your future goals and how you see yourself contributing to the research team and department. Sometimes interviewers may ask you to prepare a presentation on a previous research topic, so it is a good idea to get feedback on this beforehand from your current academic supervisor, for example. Be prepared to answer questions on the presentation afterwards. 

8. Consider the culture and community: It’s important to consider the culture and community of the programme you’re applying to. Look for programmes that prioritise collaboration and support for their students. You should reach out to current graduate students in the programme, especially students of the supervisors you’re interested in working with. This will give you a sense of the community and what it’s like to be a student there, and an idea as to whether the supervisor is a good working match for you. Consider factors such as location, cost of living, and the availability of resources such as libraries, research facilities, and funding opportunities.

9. Be aware of application deadlines and requirements: Make sure you are aware of the application deadlines and requirements for each programme you are applying to. Be sure to give yourself plenty of time to gather materials and submit your application on time, and give your references plenty of time to write their letters of recommendation. Some programmes may require additional materials such as academic transcripts and graduate certificates, so be sure to read the application requirements carefully and follow them closely.

10. Be persistent and flexible: Applying for a PhD can be a competitive process, so it’s important to be persistent and flexible. I would recommend applying to 3-4 programmes, however if you are not accepted into your top choices, taking a gap year to gain more experience and strengthen your application is a great option too, rather than accepting a programme which may not be 100% for you. Remember there is no ‘right’ time to do a PhD, everyone is different. 

By following these tips and taking the time to research potential programmes, polish your application materials, and build relationships with potential advisors and peers, you can increase your chances of being accepted into a programme that will help you achieve your academic and professional goals. Remember to stay persistent, flexible, and open to new opportunities along the way. Good luck!

Note that these tips are based on my personal experience and are not exhaustive, so use them at your own discretion!

About the author

Kaitlyn Louth

Kaitlyn Louth is a second year PhD student joint between The University of Edinburgh and Heriot-Watt University. Her research is in Bayesian Modelling and Statistical Machine Learning for Critical Illness Morbidity Prediction. In particular, using Bayesian hierarchical modelling in a neural network setting to identify changes in morbidity rates for different diseases over time, including regional or socioeconomic differences, and characterising rates for specific cancers and conditions such as heart attacks and strokes. When she is not PhD-ing, she will typically be singing karaoke, dog walking around Holyrood Park or having a little boogie (ballet, tap and very recently aerial)!

Posted by HMS in Blog
27
Oct
2021
One Day in the Life of a Software Developer

One Day in the Life of a Software Developer

by Carolin Dirks

To be honest, most maths students who are just about to finish their studies have no specific plan of what kind of job they want to pursue after graduating. Up to that point, they might have realised what they did not know in their first semester, namely that there are plenty of opportunities in different areas of industry and academia for a mathematician besides the “obvious” choices, like the financial sector and insurance companies. Artificial intelligence, automation technology, big data, deep learning, computer vision – just a few fields of great interest for modern industry, and all of them are very closely related to maths. Most of them seem to promise a much more exciting job opportunity than an insurance company – with so many possibilities, why did I finally decide for a job as a software developer in an insurance company? The short answer: Because it offers a huge lot of fun, exciting tasks, complex mathematical and computational problems, and besides, great colleagues and an outstanding working atmosphere.

Let’s have a look at the long answer. For me, during my last years at the university it became clear that I wanted to be a software developer. Solving specific tasks using logical skills and computational tricks and contributing to something “useful” were the important parts for me, in addition to a strong desire for a preferably stress-free and enjoyable working atmosphere, while I did not really care about the specific application behind my work. The job advertisement at a big and well-known German insurance company sounded exactly like what I was looking for, next to the very good reputation of the employer regarding the labour conditions. So I took the chance, honestly without a specific imagination of how a “typical day” as a software developer would look like.

Now, 1.5 years later, I am still not able to say what a typical day looks like, simply because every day can be very different. Every day can pose different tasks and new challenges, with almost no repetitions, with lots of new things to learn, with lots of new insights – and the more I understand how things work, the more I can participate actively in new areas of responsibility. A developer is not only the aimless “executor”, but also needs to keep an overview of the whole software architecture, stay in touch with the “client” (in my case, the company itself, especially those who are going to work with the new software after its release) and other departments and work together with the rest of the team in order to develop a viable product. Thus, the best way to describe what I am really doing is to divide my tasks into three “areas”: The learning part, the conceptional part and the implementational part.

Carolin Dirks

My first year in my new job was dominated by the learning part. A mathematician is typically not educated in many practical skills, a mathematician is educated in independence, learning receptivity and frustration tolerance – in being able to understand complex problems and find smart solutions by her- or himself. Basically (and hopefully not sounding overbearing) a mathematician is able to understand almost every problem, and this is in my opinion one of the main reasons a mathematician is hired. Consequently, I needed to learn a lot, about programming languages and especially about state-of-the-art tools and technologies in software development. This was a whole new world for me – before, I had literally only implemented “plain code” without a suitable development environment, without fancy testing tools and without connecting to databases. And for me, there are very few places which are more suitable for getting a wide insight into so many different fields connected to development. Learning is not only considered to be necessary, but also promoted – and everyone in my department is encouraged to spend time on learning. Additionally, we have the philosophy that, roughly speaking, every developer in my team should be basically able to do every task – of course everyone has some kind of focus, based on his or her knowledge, but everyone is also encouraged to undertake tasks where he or she is a complete beginner.

Today, learning new things is still a daily business in my job. Another part which becomes more and more important is the conception and discussion of particular features of the new software. The “clients” (in our case, the “specialist department”, those who, in contrast to me and my team, know how an insurance as a product needs to work) decide about new features they want. This can be a very small and simple request like “I want this button to be green instead of blue” or a big new feature like the possibility for the customer to report a damage case. The developers (like me) discuss the technical requirements and details, check if everything is technically possible, roughly figure out which parts of the software are affected and what has to be done and wrap everything up in one or more specific tasks. In addition, the developers can contribute their own ideas or write “IT-only-tasks” (tasks which do not bring a visible new feature, but are necessary for some other reasons).

Consequently, the last part is the implementation part – namely solving the tasks. This (mostly) means implementing new pieces of code, integrating them into the complete software (after a quite strict reviewing process by other developers) and writing automated tests for the new features. One task can take from a few minutes (like the green button) up to several weeks, often accompanied by further discussion rounds with the “insurance experts” or with other developers. Besides, a task can be done completely alone or even in a team of several people – in every case, the whole team discusses everyone’s tasks in a daily meeting together, where problems can be put on the table or opinions can be exchanged. All in all, everything is based on teamwork: If you don’t know the answer to a question, lots of phone calls and sometimes a whole bunch of people staring at the problem later always lead to a solution.

All three parts together make this a perfect job for me. As an applied mathematician, I am still able to make use of the skills I acquired during my studies and still solve complex problems. The job does not only require programming skills, but also the ability to “delve into” specific issues and to analyse all sides and effects of a problem, while always raising new challenges and opportunities to learn new things – but without the pressure of exams and the question of “what should become of me” in the future.

Posted by HMS in Blog
18
Aug
2021
One Day in the Life of two Mathematicians Juggling with Data

One Day in the Life of two Mathematicians Juggling with Data

by Mara Hermann & Marisa Mohr

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.

Literature:

[1] https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century

[2] https://spectrum.ieee.org/view-from-the-valley/artificial-intelligence/machine-learning/andrew-ng-xrays-the-ai-hype

[3] https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568

[4] https://towardsdatascience.com/introduction-to-recommender-systems-6c66cf15ada

[5] https://www.stitchdata.com/blog/5-things-you-should-know-for-career-in-data-engineering/

[6] https://www.ox.ac.uk/admissions/undergraduate/courses-listing/mathematics-and-philosophy

Posted by HMS in Blog
09
Jun
2021
A Feminist Rant

A Feminist Rant

Or a Plea for Change

by Joana Sarah Grah

Do you still come across the common stereotypes against mathematicians in general and women mathematicians specifically? Maths is boring, maths is for loners, maths is unsexy (and done by unsexy people – I just stumbled upon this again recently when reading a quote-retweet by Hannah Fry replying to someone who claimed there are no “hot” people that are good at maths – just for the record, I know quite a few), maths is dry and above all – maths is for men!

I don’t know about you but I’m so tired of it. When did we exactly start to think that being good at a subject at school is something to be made fun of or to be ashamed of? I have heard this so many times: “Oh, I’ve always hated maths.”, “Only geeks and losers like maths.”, “I always sucked at maths.”.  But in a – you know – kind of proud way? What’s wrong? Do you like not being able to calculate the appropriate tip when you’re eating out? Do you enjoy not understanding probabilities, hence not being able to evaluate risks for instance? Did you never see the exponential growth of infections during the pandemic – which has always been exponential in the first place – coming? It’s always easier to deny things we don’t understand but are afraid of. In the current situation this is particularly dangerous and even probably harmful for others. Nothing to be proud of if you ask me.

A solid foundational education in mathematics is essential, no doubt. But maths is so much more than being good at calculating stuff. In fact, I couldn’t name any area of application where maths doesn’t play a role. Natural sciences like physics, chemistry and biology, earth sciences, astronomy, medicine, economics, arts restoration – those are just some examples that come immediately to mind. The variety of mathematical fields and the respective methods is similarly vast – there’s so much to explore and really something to be passionate about for everyone. In addition, maths is absolutely no discipline where teamwork isn’t encouraged. In fact, you discuss and brainstorm with colleagues day-to-day (although there are exceptions of course). Interdisciplinarity is key to most problems and projects arising in applied maths.

Now let’s get to the point that bothers me the most and that is the reason we set up this webpage. Unfortunately, it’s still a common misperception that maths is not for women. Pretty pathetic given that we’re living in 2021 you ask? Yes, absolutely, but it turns out we’re still living in a patriarchy. That is why we need to be feminists. 

At the beginning of your studies, you probably won’t realise the disproportion between women and men in maths. You’ll notice that you have very few or even no women professors. Most of the academic staff is likely to be men. The gap becomes more obvious the further you get. Finding women working in the same field at conferences is probably much more difficult than finding men. Seeing women on discussion panels and giving talks will be the exception rather than the norm. It is getting a bit better though and many people are aware of the problem and encourage diversity. Yet the majority of women seem to decide at some point of their academic career that they don’t want to pursue it further. Why is that? Anti-feminists, mostly men, often claim that it’s their personal choice to leave because they prefer a part-time job, a job in a less competitive environment, a job that fits their “abilities” and “interests” more, because they want to have a family and won’t be able to have children and an academic career. Nothing wrong about any of this but the crucial point is having the choice. It is indeed possible to both have a family and a professorship. And it is indeed possible to be a professor while still prioritising your leisure time, your mental health, your family, your friends. Not all women are given those opportunities. Most women don’t have the choice. It is a structural problem, an institutional problem, a societal problem. Maybe you missed the important discussions because you left an informal meeting after a conference day, as you were the only woman and felt uncomfortable, or because you didn’t have childcare for the whole night. Maybe you risk a huge fight with your family, or even ending the contact altogether, or you lose a relationship, because you’re spending too much time writing grants (instead of attending family events, going on your long-planned vacation or caring for your kids – or having kids). Maybe all the people in power making decisions are men and they like to surround themselves with like-minded men.

We need to make women in maths visible for the next generation who are desperately searching for role models because they don’t see them. We need to amplify the voices of women in maths because oftentimes the voices of men in maths are much louder. We need to showcase the variety and – more often than not – non-linearity of career paths including failures, doubts, setbacks, maybe starting all over again, maybe changing fields completely, maybe having children. We need to raise awareness for the lack of resources in schools and universities to highlight women in mathematics, for the fact that mental health is actually physical health and just as important as making sure you stay up-to-date with the literature and back up your work regularly. We need to normalise not working crazy hours on a regular basis, having a family, not having a family, admitting that you don’t know something, asking “stupid” questions (I know it’s stale but there really are no stupid questions, most of the time those are the important questions to ask) and having interests that have nothing to do with maths.

Why do I write this now rather than at the time when we launched our page at the beginning of the year? Because I was afraid I would sound too aggressive, I would probably exaggerate things and because I’m sharing very private opinions and experiences. On the other hand, it was about time. I reflected a lot about this recently and realised how much of it I suppressed or dismissed as innocuous. What really fuelled my anger was when I saw injustices happening to other women, to friends, to the next generation. Most of the time they seem subtle but they do impact your day-to-day work life significantly. I experienced women suffering from imposter syndrome that came across so strong and confident yet still being at the mercy of the broken system and socially acceptable misogyny. Besides the structural problem, there is the everyday sexism all of us are familiar with. Do you find it hard to literally be heard in a discussion? Do you have to raise your voice a bit extra? I certainly had to sometimes. Another classic is when a man paraphrases something you just said and gets all the praise for it. Is this something we just have to cope with? What about strangers at conferences asking you out for dinner during a poster presentation? Uncomfortable to say the least. Something we have to  bear? I have once been told that I should apply for a professorship simply because I’m a woman and these days it’s super easy for women to get a position, basically everyone is accepted. I don’t think that’s acceptable and I wish I had been more assertive in this situation.

I don’t want to close on a negative note though. Thankfully, I had so many more positive encounters during the past years in academia than negative ones. Men and women who were genuinely interested in discussing research, appreciated my advise, gave me very valuable advice, motivated people – especially women – who were struggling and doubting themselves, facilitated socialising and networking at academic events, showed their own vulnerability and insecurities, shared their failures and how they overcame hurdles, educated themselves and were feminists. Let’s take them as an example.

Let’s try to be a bit more understanding, a bit more empathetic and a bit more supportive in this already stressful, fast-paced, competitive environment that academia mostly is. Let’s speak out clearly if we witness any kind of bullying, sexism and harassment. Of course things have to change on a much bigger scale and first and foremost systemically. But every one of us can make a difference – no matter how small – so let’s start today!

Posted by HMS in Blog