2024-12-12 –, Mission 1
Research today is undeniably data-driven, with some of the most compelling insights emerging from the analysis of sensitive data. Examples include personal information, health records, and commercial data, all of which hold significant potential when properly leveraged. Combining these types of data with advanced analytical techniques can open up entirely new avenues of discovery, leading to breakthroughs that would otherwise remain out of reach.
In this panel discussion I want to go with you through my experiences in working with sensitive as a former researcher at Statistics Netherlands (CBS) and the TU Delft, and how SURF is offering and continuously developing state-of-the-art software and infrastructure solutions to enable this type of research. I want to share the possibilities of working with sensitive nature and open up the discussion surrounding this matter.
I will be going through the following key topics:
- Sensitive data in research: what does this entail?
- Challenges and (current) solutions on computing with sensitive data
- What's at the horizon at SURF, the Netherlands, and Europe?
- What are Trusted Research Environments?
Ahmad has a background in Physics and Computer Engineering at the TU Delft. He worked for several years as a software engineer at CERN in the core development team of BioDynaMo: a modular high-performance agent-based simulation platform. He later applied this software in Statistics Netherlands (CBS), where he was tasked to create an agent-based digital twin of the Dutch population to model various socio-economic behaviors. He firsthand experienced the challenges of working with sensitive data using high-performance computing resources. This experience highlighted the need for a smoother, more accessible process, inspiring him to drive improvements in infrastructure and services at SURF to facilitate research with sensitive data for others in the field. Currently, Ahmad is leading various projects at SURF that aim to support researchers in securely accessing and working with sensitive data, including medical, social, and commercial datasets.