Women in Machine Learning Workshop 2026
November 19 @ 12:00 – November 20 @ 12:00
Connect, collaborate, and shape the future of Machine Learning.
Machine Learning (ML) is at the forefront of technological development and is transforming fields ranging from healthcare and finance to security and transportation. As its influence on research, industry, and everyday life continues to grow, it is increasingly important that diverse perspectives are represented in shaping its future.
The Women in Machine Learning Workshop brings together early career researchers in ML and AI. This workshop is specifically designed for women, underrepresented gender groups, and others with an interest in machine learning and increasing diversity in AI research. It creates a space to connect, exchange ideas, gain inspiration, and build a stronger and more inclusive ML community.
Whether you are new to Machine Learning or already working in the field, the workshop offers an opportunity to meet researchers and students, learn from invited speakers, discuss experiences and ideas, and discover ongoing research.
The workshop is organized as a lunch-to-lunch event and will include invited talks, research presentations, demonstrations, discussions, networking opportunities, and a poster session.
Venues
Ada Lovelace, House B, Floor 2, Linköping University MazeMap
Zero, Zenit, Floor 2, Linköping University MazeMap
Who can participate?
The workshop is centered on women in Machine Learning and aims to support greater gender diversity in the field. We welcome participants who share this goal, with a particular focus on women and underrepresented gender groups.
The workshop is especially relevant for students, researchers, and professionals who are interested in Machine Learning, Artificial Intelligence, Computer Vision, Data Science, and related technical fields. Participants at all stages, from those who are new to the field to experienced researchers and practitioners, are warmly welcome.
No specific level of experience is required. Whether you would like to learn, share your work, meet others in the community, or simply explore opportunities in Machine Learning, we encourage you to join us.
Invited speakers
Ericka Johnson, Deputy Head of the Department of Thematic Studies (TEMA), Linköping University
Title: GenAI and world-building with synthetic data
Abstract: Synthetic data is used to assure privacy, create portable data, address data imbalances, and amplify small datasets. But what is synthetic data? What does it represent? And is this even a relevant question? In this talk, I will show you results from our experiments making synthetic tabular data, discuss our work with data repositories trying to document it, and open for a discussion on how domain experts from various fields are using it.
Bio: Ericka Johnson is Professor of gender and society, Linköping University, Sweden, and director of the Swedish national graduate school for the Wallenberg AI, autonomous systems and software program – Humanity and Society. Her research focuses on how data represent the worlds and objects they claim to describe, with a special interest in medical data. She has an interdisciplinary background in sociology, gender studies, and science & technology studies. Johnson is the author of several monographs and anthologies, including How that Robot Made Me Feel (MIT Press 2025), and the recent Big Data & Society article (with Lee & Hajisharif) ‘The Ontological Politics of Synthetic Data’, in which she details intersectional hallucinations. She is a member of the Royal Swedish Academy of Sciences.
Ekta Vats, Assistant Professor and Vice-Chair of WASP Diversity and Inclusion Group (DIG), Uppsala University
Title of talk: Teaching Multimodal AI to Know What It Doesn’t Know
Abstract: Modern AI systems increasingly need to reason across several kinds of data at once, such as text, images and video, and to do so reliably in settings where mistakes carry real costs. This talk presents ongoing research on multimodal learning, covering methods that combine vision and language, and applications ranging from handwritten text recognition and the recovery of hidden text in historical manuscripts to problems in the life sciences. A recurring theme is trust: a model that confidently gets things wrong is often worse than one that admits it is unsure. We will show how uncertainty quantification can give multimodal models that kind of self-awareness, and discuss what remains open.
Bio: Ekta Vats is an Assistant Professor in Machine Learning and Docent in Computerised Image Processing at the Department of Information Technology, Uppsala University, and a Beijer Researcher at the Beijer Laboratory for Artificial Intelligence Research. Her research focuses on multimodal learning, vision-language models and video-LLMs, with applications spanning the life sciences and cultural heritage, including multispectral imaging to uncover hidden texts in old manuscripts. She is a member and vice-chair of the WASP Diversity and Inclusion Group.
Lena Klasén, Adjunct Professor, Linköping University
Program
Contact
The workshop is organized by representatives from Linköping University, in collaboration with WASP Diversity and Inclusion Group.
For questions about the workshop, registration, or participation, please contact:
Amanda Berg, amanda.berg@liu.se.
