DAISData and AI Systems Lab

Contactgegevens:

Prof.dr.ir. Geert-Jan Houben
Mekelweg 15, 2629 JB Delft
g.j.p.m.houben@tudelft.nl

The DAIS Lab is a state of the art data-driven AI technology infrastructure. Its mission is to nurture technology and systems for domain- and data-driven AI. DAIS aims to make infrastructure AI-Sandboxes available to different AI and application domain stakeholders, who require expertise of advanced data management techniques, data-driven AI technologies and a scalable infrastructure to process (CPU/GPU clusters, fast network interconnects), store and catalog (software interfaces and toolsets) large amounts of data (domain specific databases and training sets).
The DAIS Lab operates within the universities of Leiden, Delft and Erasmus, and the Medical Centers LUMC and ErasmusMC. Using AI is a key enabler in almost all research areas nowadays, DAIS offers infrastructure including dedicated domain sandboxes that can add expertise of major societal challenges and train non-CS researchers in using AI solutions. The DAIS approach centralizes this competence and offers it “AI as a service”.

In addition to the traditional challenges addressed by data science research (e.g. volume, speed and processing scalability). The DAIS Lab develops and provides access to state-of-the-art hardware and software for data collection, analysis, visualization as well as building machine learning models. The technological infrastructure is available for usage in the DAIS Lab premises as well as remotely. The DAIS Lab also maintains scientific programmers that aid DAIS Lab members from different disciplines in of the five participating institutions, without deep knowledge on data management, cluster computing and AI to leverage the latest developments in the area of data-driven AI.
The types of research that the DAIS Lab enables can be AI for health and well being, AI-supported predictive maintenance and model design in aerospace engineering, molecule interaction simulations aided with AI models in chemistry, large-scale data analysis of machinery and hardware developed by the electrical engineers, decision support expert systems for law, collection-storage and analysis of data collected from sensors in large built structures (e.g., ports, transportation routes, bridges, dams).
The infrastructure will target the scientific challenges (Grand AI Research Challenges) as set out by the NWO AIREA research agenda (nov 2019).

Aansluiting bij strategische ontwikkelingen
Topsectoren: 
Life Sciences & Health
ESFRI:
No
NWA-Routes: 
Op weg naar veerkrachtige samenlevingen
Tussen conflict en coöperatie
Waardecreatie door verantwoorde toegang tot en gebruik van big data
Smart, liveable cities