Women* in Data Science 2025

In 2016, the United Nations designated 11 February as the International Day of Women and Girls in Science, aiming to foster gender equality within STEM fields. Despite possessing equal capabilities, the global average indicates that only approximately 35% of STEM graduates are women*. Ensuring a diverse representation in science is essential to prevent a narrow perspective and to stimulate the emergence of new ideas, talents, and creativity.

Within our research network, we take pride in supporting and showcasing the pioneering efforts of female* researchers who are pushing the boundaries of their respective fields, making substantial contributions to the scientific community. Join us in commemorating the accomplishments of female* researchers, as we strive to raise awareness and inspire more women* to pursue careers in STEM.

The University of Vienna is uniting to celebrate Women* in Science through a collaborative campaign involving Computer Science, Mathematics, Physics, Chemistry, Earth Sciences, Geography and Astronomy, Life Sciences, Microbiology and Environmental Systems Science, Max Perutz Labs and our Research Network Data Science @ Uni Vienna.

By uniting various faculties, centres, and networks, the University of Vienna highlights the significance of diversity in science and emphasises the fundamental role of women* in advancing the field.

Find out more about the campaign:

The following is a list of remarkable women* who are active in data science and a brief overview of their accomplishments:

From left to right:
  • Edina Marica, Master Student in Data Science, is interested in machine learning and theory of algorithms, with a focus on graphs. Photo © private
  • Yllka Velaj, Assistant Professor for Computer Science, focuses on designing algorithms for graph mining. Photo © private
  • Christa Cuchiero, Professor for Quantitative Risk Management, is doing research at the intersection of stochastic analysis, machine learning, and mathematical finance. She is especially interested in classes of universal stochastic processes, approximation theory in dynamic situations, data driven risk interference, and machine learning in finance. Photo © private
  • Diana Széliová, Post-Doc Researcher at the Research Group Biochemical Network Analysis, investigates cell biology and evolution with mathematical models of metabolism. Photo © Christoph Kawan

From left to right:
  • Rebecca Kahn, Post-Doc Researcher in Digital Humanities, investigates how knowledge is transformed and transmitted when large-scale collections of digitised museum data are published and shared online. Photo © Ralf Rebmann
  • Ana Stanusic, Prae-Doc Researcher in Data Science, focuses on improving the understanding of data quality through data visualisation and including domain knowledge in data quality analysis in the field of fleet management. Photo © private
  • Stefanie Brandstetter, Prae-Doc Researcher in the Research Group Biochemical Network Analysis, focuses on the application of chemical crosslinking and mass spectrometry towards characterising conformational changes of complexes and profiling of protein networks. Photo © private
  • Laura Lotteraner, Prae-Doc Researcher in the Research Group Visualization and Data Analysis, focuses on understanding environmental issues concerning ground and surface water and its polution using data visualisation and modern data analysis approaches. Photo © Stephanie Scholz 

From left to right:
  • Claudia Plant, Professor for Data Mining, focuses on extracting knowledge from massive data. Photo © Barbara Mair
  • Tara Andrews, Professor for Digital Humanities, explores how we can take advantage of data methods and digital collections to learn new things about medieval history, even when our data and collections have a lot missing. Photo © Ilse Herzinger
  • Eva Szilagyi, Prae-Doc Researcher in Theory and Applications of Algorithms, is interested in design of fast dynamic geometrical algorithms. Photo © private
  • María José Rimón Martínez, Prae-Doc Researcher at the Research Group Biochemical Network Analysis and part of the PROHITS Doctoral Network, focuses on developing genome-scale metabolic models (GEM) of thermophilic prokaryotes for study as microbial cell factories. Photo © private

From left to right:
  • Sylvia Kritzinger, Professor for Methods in the Social Science at the Department of Government, collects and analyses survey data to examine public opinion and electoral behaviour. Photo © private
  • Andrea Graf, Master Student of Bio Data Science, focuses on mathematical modelling of biotechnical processes. Photo © private
  • Yseult Héjja-Brichard, Post-Doc Researcher in the Fusani Lab at the Department of Behavioral and Cognitive Biology, focuses on sexual signals and courtship behaviours in birds and fish using deep learning and machine learning methods. Photo © private
  • Tatyana Krivobokova, Professor for Statistical Methods, is a statistician working both on applied and theoretical problems. Photo © Caro Hoene

Q & A about Women in Data Science

What measures do we take to encourage women to study STEM subjects or do research in the STEM field? What measures are we taking to make it easier for women to enter the field?

The Research Network Data Science @ Uni Vienna is actively committed to inspiring women to study and pursue a career in STEM fields and to making entry as low-threshold as possible.

  • We take part in programmes such as the Vienna Daughters' Day and the Women in Science initiative to specifically motivate schoolgirls and young women to engage with scientific topics and pursue a career in STEM.
  • Female board members of our research network act as role models, for example by participating in the LEA Role Model Programme like Christa Cuchiero and Sylvia Kritzinger
  • Our experts in the research network give talks at networks such as Women@POI to empower and inspire women in the IT sector. 
  • The commitment to Women in Science is highlighted on our website throughout the year to continuously draw attention to this topic and make women visible.
  • When selecting students for our Master's programmes in Business Analytics and Data Science, we ensure that women are not disadvantaged and place great emphasis on diversity.
  • We offer initiatives such as Women in AI a platform, for example by integrating them into events such as Data Science Day, in order to strengthen networks and specifically promote women.
What progress has already been made in increasing the number of women in our organisational unit in recent years?
  • 57% of the female members of our Board are also represented on the Executive Board, which emphasises their central role in strategic decisions.
  • Women make up 54% of the students admitted to our interlinked Master's programmes in Business Analytics, Data Science and Digital Humanities. In addition, the proportion of female graduates in these programmes is 53%, which demonstrates the sustainable promotion and successful completion of female students.
  • The position of Scientific Coordinator was successfully transferred from a male to a female appointment.
What obstacles or challenges still exist with regard to gender equality in the STEM disciplines and how does our research network deal with them?

A key obstacle in STEM disciplines continues to be unconscious biases and stereotypical thinking patterns that perpetuate gender inequalities in some minds. Even though we do not see any qualitative problems in terms of women's achievements, overcoming such mental barriers requires continuous efforts and challenges us to create an inclusive environment for all.

  • We plan to offer DEI workshops to raise awareness of biases and promote a culture of openness and equality.
  • The proportion of female graduates among our Master programmes is already higher than that of male graduates. This shows that we can specifically support women and guide them successfully through their academic careers.

On this occasion, we would like to showcase three famous women who we consider to be pioneers in our field:

Ingrid Daubechies

Ingrid Daubechies (*1954) is a pioneering mathematician and physicist, known for her work on wavelet theory, which has far-reaching applications in image and data processing. She was the first woman to hold a tenured professorship in Princeton's mathematics department and championed women in mathematics as President of the International Mathematical Union (2011–2014). In January 2025, she was honoured with the National Medal of Science.

Read an article by Ingrid Daubechies and our board member Philipp Grohs on u:scholar:
Rima Alaifari/Ingrid Daubechies/Philipp Grohs/Rujie Yin, Stable Phase Retrieval in Infinite Dimensions, in: Foundations of Computational Mathematics, Vol. 19/No. 4, 2019, pp. 869–900.

Florence Nightingale

Florence Nightingale (1820–1910) was a British pioneer of nursing and a trailblazer of modern statistics. She revolutionised healthcare through the use of statistical methods, particularly during the Crimean War, when she used detailed data analysis to reduce the mortality rate in military hospitals. Nightingale was one of the first to use charts to visualise health data in order to influence political decisions. Her mathematical legacy continues to shape epidemiological research and health management today.

Ada Lovelace

Ada Lovelace (1815–1852) was a British mathematician and pioneer of computer science. She developed the first concept of an algorithm for a machine - she is therefore considered the first female programmer in history. Her visionary ideas on the automatic processing of data laid the foundations for modern computer science.