Short Bio

Felienne Hermans is a professor of Computer Science Education at the Vrije Universiteit Amsterdam. She also works as a high-school CS teacher one day a week at Open Schoolgemeenschap Bijlmer.

Felienne is the creator of the Hedy programming language, a gradual and multi-lingual programming language designed for teaching and author of the book “The Programmer’s Brain” a book that helps programmers understand how their brain works and how to use it more effectively. In 2021, Felienne was awarded the Dutch Prize for ICT research.

She writes for Dutch newspaper Volkskrant every month and writes a weekly newsletter on computer science, AI and many other things.

Keynote Title

TBD

Abstract

Some progress has been made over the last decades to increase women’s participation in science, but in how far has scientific practice changed to accommodate women’s lives, practices, and interests? Has there, in other words, been shifts in the epistemological core of science? In this talk Felienne Hermans, professor of Computer Science Education, will present a history of feminist epistemology and examine the changes in science through that lens.

Short Bio

I exploit the interdisciplinary aspects of software engineering, which encompass both technical and human concerns, while developing techniques for intelligent, autonomous, and highly distributed systems. I am an Associate Professor in the CS Department at Durham University. Earlier, I was a Senior Lecturer (equiv. Associate Professor) in the Computer Science Department at Aston University. From September 2020 I am the PI of the EPSRC Twenty20Insight research project. In 2019 I was granted the Leverhulme Fellowship “QuantUn: quantification of uncertainty using Bayesian surprises.” Before, I was a Marie Curie Fellow at INRIA Paris – Roquencourt. The Marie Curie project is called Requirements-aware Systems (nickname: Requirements@run.time). Before, that I was a Senior Research Associate at Lancaster University (UK) where I also got my PhD.

Keynote Title

More Than Data: Women’s Health, AI, and the Decisions that Shape Us

Abstract

Behind every dataset, every model, and every recommendation lies something deeply human: a decision. In women’s health, these decisions are often complex, uncertain, and deeply personal. But they are not only made in clinics, they are also made in careers, in moments of doubt, and in the paths we choose to follow.

In this keynote, I will reflect on my journey as a woman in computing, weaving together my work on AI for women’s health with the personal decisions that have shaped my own path. From navigating uncertainty in research to making life choices without clear answers, I will explore how decision-making connects our professional and personal worlds.

Rather than focusing only on algorithms, this talk asks a broader question: what does it mean to design systems that support real decisions, when life itself does not come with ground truth? I will share lessons from both science and experience, highlighting the importance of voice, context, and trust, not only in AI, but in how we move through the world.