ADS AI Tech Week: Health and AI
The purpose of ADS AI Tech Week is to offer a stage to the Data Science and AI ecosystem in Amsterdam. The content invites collaboration between academic, societal and industry-oriented organisations spread out over 3 themes. The aim of this is to facilitate cross-sector, multi-disciplinary networking and knowledge sharing.
The AI Tech Week will cover 3 days (afternoons). Each day has its own theme and moderator and will speak to a different sector.
- DAY 1 (16.00-17.00): Business, Finance and AI (Tuesday, 7th of March 2023)
- DAY 2 (16.00-17.00): Health and AI (Wednesday, 8th of March 2023)
- DAY 3 (15:30 – 16:30): Public and AI (Thursday, 9th of March 2023)
Location: VU NU Building, room: 02A65
Programme
15:55 Walk-in
16:00 Introduction & Welcome
16:05 Talk #1: Machine Learning in Healthcare by Mark Hoogendoorn
16:20 Discussion
16:25 Talk #2: Machine learning and mechanistic modeling in biomedicine by Evert Bosdriesz
16:35 Discussion
16:40 Talk #3: Personalised nutrition: promises and challenges by Marijke Frantsen
16:55 Discussion
17:00 End of talks
17:10 End!
Talk #1: Mark Hoogendoorn (Full Professor of Artificial Intelligence at the Department of Computer Science of the Vrije Universiteit Amsterdam and chair of the Quantitative Data Analytics group)
Mark Hoogendoorn is a full professor in Artificial Intelligence at the Department of Computer Science of the Vrije Universiteit Amsterdam (VU) and chairs the Quantitative Data Analytics group. His research focuses on machine learning and its applications, the latter primarily applied in the domain of health and wellbeing. He is part of the management team of the VU Campus Center for AI and Health and Amsterdam Medical Data Science. He obtained his PhD in 2007 at the VU.
Title talk: Machine Learning in Healthcare
Machine learning brings great promise for the domain of health and wellbeing. While a lot of research is devoted to such applications, only very limited machine learning approaches make it to the doctor and patient. In this talk, I will provide an overview of the domain of machine learning for health, provide state-of-the-art example applications, and will go into the barriers that make applications land in practice challenging.
Talk #2: Evert Bosdriesz (VU Computer Science)
An assistant professor in the Bioinformatics group in the Computer Science department at the VU Amsterdam. He has a background in Theoretical Physics (MSc), Systems Biology (PhD), and Computational Cancer Biology (Postdoc). In his research he develops and applies “top down” machine learning and “bottom up” mechanistic modelling approaches to understand signal transduction networks and the role they play in cancer, with a particular focus on new single cell technologies.
Title talk: Machine learning and mechanistic modeling in biomedicine
Molecular profiling technologies such as DNA and RNA sequencing have given us unprecedented insight in the molecular biology of cells and how this is compromised in diseases such as cancer. In this talk, I will focus on the opportunities and challenges in applying machine learning methods to such datasets, how mechanistic modeling approaches can be complementary, and what new opportunities single cell profiling technologies provide.
Talk #3: Marijke Frantsen (Clear.bio)
Lead Data Scientist at clear.bio, a HealthTech startup based in Amsterdam. Our goal is to help people with type 2 diabetes to improve their health.
Title of the talk: Personalised nutrition: promises and challenges