UNIGE Data Science Day

The UNIGE Data Science Day is a scientific symposium for researchers at the UNIGE, which takes place at the beginning of the academic year.

Each year, the CCSD stimulates a collective reflection on a particular theme. This reflection is introduced during the UNIGE Data Science Day and continued in the framework of other activities launched by the Center throughout the year. This theme is intended to be precise enough to encourage a significant scientific contribution and a rich dialogue, but also transversal enough to allow for interdisciplinarity.

For its 2024 edition, the theme of Data Science Day is "Cross-Disciplinary Dialogues: Celebrating Collaborative Excellence in Data Science".

This meeting will take place on September 10, 2024 from 1:00 pm to 6:00 pm at Uni Mail.

2024 Theme: Cross-Disciplinary Dialogues

Entitled “Cross-Disciplinary Dialogues: Celebrating Collaborative Excellence in Data Science”, the 2024 edition of the Data Science Day seeks to bring together researchers and practitioners who have embarked on joint ventures, marrying methodological expertise from statistics and computer science with domain-specific knowledge from various disciplines. This year's Data Science Day aims to highlight and celebrate the achievements born from such collaborations, including those nurtured within the UNIGE's Competence Center in Data Science since its inception in September 2020. We invite these collaborative teams to share their success stories, challenges, and insights gained through their partnerships. Our goal is to unearth best practices and illuminate the essential role of collaboration in advancing Data Science research and application.

Therefore, we welcome submissions that explore topics including, but not limited to:

  • Insights from teams currently navigating collaborative data science projects, highlighting interim challenges and adaptive strategies.
  • Case studies of successful collaborations between methodological and domain specialists, outlining the process, challenges, and outcomes.
  • Discussions on projects that were halted or faced significant obstacles, sharing the lessons learned and the resilience required in such endeavors.
  • Innovative strategies for fostering interdisciplinary cooperation in data science projects, including overcoming barriers and leveraging diverse expertise.
  • Best practices and insights derived from collaborative data science research, with a focus on methodological integration and mutual learning.
  • The role of centers and networks in facilitating interdisciplinary research in data science, including lessons learned from the Competence Center in Data Science at UNIGE.
  • Future directions for collaborative data science research, highlighting emerging opportunities, fields of application, and potential impacts on society.

Submissions are encouraged from researchers across all levels, from graduate students to senior faculty members, representing the broad spectrum of disciplines engaged in data science. Proposals will be for oral presentations.



Interested in taking part of the Data Science Day 2024?

 click here >>

(registrations are open until the 1st of September 2024)

Detailed Program


13h00-15h00 : Plenary session - cross-disciplinary dialogues (room MS130)

- Volodymyr Savchenko, Faculty of Sciences, "Web-based platforms enabling Open Research Data across disciplines"

- Kaushal Sharma, Faculty of Sciences, "Annotations of perceived arousal from speech and heartrate: impact on multimodal emotion recognition performance"

- Hugues Turbé, Faculty of Medicine, "Research on self-explainable AI models in image classification".

- Jamil Zaghir, Faculty of Medicine, "FRASIMED: efficient dataset creation through crosslingual annotation projection".

- Mina Bjelogrlic, Faculty of Medicine, "HERO: towards large scale discoveries of protection factors in Health".

- Jean-Philippe Goldman, Faculty of Medicine, "DocuFlow a NLP pipeline in clinical context".

- Benoît Girard and Giuseppe Chindemi, Faculty of Medicine, "Analysis of free social interaction from eye to ai".

- Matthias Studer, Faculty of Social Sciences, "Studying Social Inequalities During the Covid Crisis using Administrative Data and Innovative Longitudinal Methods: Interdisciplinarity Challenges and Prospects".

- Tommaso Venturini, Faculty of Social Sciences, "Large-scale fieldwork: discourse analysis of the 1st Climate Global Stocktake".

- Gregory Giuliani, Institute for Environmental Sciences, "Mapping Land Cover Change: A Crossroad between Environmental, Computer, and Geospatial Science".

- Sebastian Engelke, Geneva School of Economics and Management, "Machine Learning for Climate Extremes".

- Laurent Moccozet, Institute of Information Sciences, "RCnum: an online semantic and multilingual edition of the Registers of the Geneva Council during the time of Calvin".

- Julien Prados, Faculty of Medicine, "Are Data-Science Platforms a good model for collaborative excellence ?".

- Giovanna Di Marzo, Faculty of Social Sciences, "Interdisciplinarity through Two 4EU+ Initiatives".

- Vestin Hategekimana, Faculty of Social Sciences, "WeData a step in unige’s data science community".

- Lamia Friha, DiSTIC, "Fostering Scientific Discovery Through Linked Data".

- Mathieu Vonlanthen, DiSTIC, "Hedera a Linked Data Platform for Research Data".

- Jean-Blaise Claivaz, DIS, "Byte-Sized Help On-the-Go for Research Data & Open Science".


15h00-15h30: coffee break (hall room MS130)


15h30-17h00 : Round tables

Organised by François Grey (Geneva School of Economics and Management)


Alice Scattolin (Geneva School of Economics and Management), Anna van Es (Geneva School of Economics and Management), Thomas Maillart (Geneva School of Economics and Management), François Grey (Geneva School of Economics and Management).


The Research Institute for Statistics and Information Science represents a merger of two institutes that have played major and complimentary roles in the University’s portfolio of data-science-related activities. In this round table panel session, two PhD students from RISIS will introduce their research and discuss their hopes and ambitions for RISIS, in dialogue with two senior researchers from RISIS. This discussion of expectations for the newly-formed institute will include the entire audience, in the spirit of “vivre ensemble” that the our University is promoting.

Organised by Noémi Duperron (DIS).


Dimitri Donzé (DIS), Floriane Muller (DIS), Anouk Santos and Talal Zouhri (DIS).



Are you familiar with Research Data Management (RDM), the FAIR principles, the data life cycle, data documentation, licensing, preservation strategy, and RDM storage? Are you considered a resource person by your colleagues? Do you wish you could help them and share your know-how?  The RDM Skills Lab is for you. Join fellow researchers and RDM specialists to discuss cross-disciplinary issues in a dynamic and interactive format. The aim of this lab is to set the foundations to improve collaborative networks to strengthen research support and explore ways for researchers to have their data management skills recognized and acknowledged, both within the University and in the broader job market.

previous editions

2023 edition - Data Science for All : How to teach and integrate Data Science in diverse Disciplines

2022 EDITIon -  Promises of Artificial Intelligence: An Interdisciplinary Revolution

2021 edition - Shaping a Better Future with Data: Data Science for Sustainable Development Goals

2020 edition - Epidemics and (Big) Data: contributions and challenges of data science in the study of diffusion phenomena