We are very excited to be a key contributor to the DACCOMPLI (Dynamic Data Analytics through automatically Constructed Machine Learning Pipelines) Project, which was officially kicked off on 9 February 2018, with a meeting of all consortium members at LIACS, Leiden University. The project aims at developing a platform for dynamic data analytics based on techniques for automatically constructing machine learning pipelines for a broad range of real-world challenges, including dynamic management of energy stored in fleets of electric cars and prediction of Parkinson’s disease; it received one million euros in funding from the Netherlands Organisation for Scientific Research (NWO) and private partners.
The people involved in the project include Prof. Dr. Thomas Bäck (PI and adjunct member of ADA), Prof. Dr. Holger hoos (co-PI and head of ADA) and Can Wang (PhD student and member of ADA) from LIACS, Leiden University, as well as colleagues from Delft University of Technology, Eindhoven University of Technology, Honda Research Institute Europe GmbH and the Leiden University Medical Centre (LUMC).
The project will develop algorithm configuration approaches for composing, configuring, and parameterising data analytics pipelines from scratch – thereby automatically generating the best solutions for a broad range of data analytics tasks. To demonstrate the approach, two complementary practical application tasks have been selected: The early detection and treatment optimization for Parkinson’s disease, and the cost-effective and environmentally optimised management of energy for private households with electric vehicles. The first case deals with video recordings and slow dynamics over time (analyzing the progression of the disease over a series of diagnostic observations), while the second addresses numerical data with fast dynamics and the need for optimal decision making in real time. Our group is focussed on the second application, in cooperation with Honda Research Institute Europe GmbH, as well as on foundational work together with TU Eindhoven on the automatic construction of data analytics pipelines (an application of AutoML), while work at TU Delft and is focussed on the Parkinson application.