Recurring events
UM Data Science Research Seminar Series
The UM Data Science Research Seminar series serves as a forum to learn about, discuss and critique high impact publications from all facets of Data Science.
Women in Data Science (WiDS) Maastricht
In 2023, the Institute of Data Science organized the 4th edition of Women in Data Science (WiDS) Maastricht. Currently, we are organizing the 5th edition of the WiDS Maastricht Conference and the WiDS Maastricht Datathon.
WiDS Maastricht Conference 2023: A Lookback
On Tuesday the 7th of March 2023, the 4th edition of the Women in Data Science (WiDS) Conference was held in-person at Maastricht University.
The conference included keynote speakers, technical talks, interactive sessions, a panel discussion, and a data science award ceremony. The invited speakers discussed the impact of data science on the social good and came from academia and industry. The discussed topics featured a new improved perspective on health data sharing, both conceptually and from a distributed learning perspective.
You can look back at the event here (in words and pictures), or visit the event page.
Thesis Defense Dr. Chang Sun
We are proud to announce that Dr. Chang Sun has successfully defended her thesis "Privacy-preserving personal data analysis" on Monday 14 November 2022. Congratulations to Chang and her supervisors Prof. Dr. Michel Dumontier and Dr. Johan van Soest!
Thesis summary
An ever-increasing amount of data is generated by our citizens and used in our daily life every single day. These massive amounts of data can be used to improve digital technologies and develop data-driven innovations that can impact every aspect of people’s lives. However, a lack of sharing of, access to, and reuse of data from multiple organizations hinders the analysis possibilities and hence potential insights from the data. Several challenges have been recognized, such as technical barriers, security, data-protection compliance to one or more legal jurisdictions, privacy concerns, and trust issues.
We aim to develop new privacy-preserving data sharing and analysis techniques that strengthen and extend the (re-)use of personal data while maximally protecting individuals’ privacy. To achieve this aim, this thesis addresses the research challenges of personal data sharing and (re-)use from the perspectives of data organizations, scientific researchers, and individuals.
