Computational Intelligence Platform for Evolving and Robust Predictive Systems (INFER, 2010-2014) The Computational Intelligence Platform for Evolving and Robust Predictive Systems (INFER) project (budget 1.55 mln EUR). INFER is a major EU-funded project involving 25 academics from organisations in three different countries. This includes Evonik Industries from Germany, one of the world's leading companies in the process industry; Research and Engineering Centre (REC) from Poland, a highly innovative software engineering company and the Smart Technology Research Centre of Bournemouth University in the UK, an interdisciplinary and integrative centre conducting research in the field of automated intelligent technologies. INFER is a project funded by the European Commission within the Marie Curie Industry and Academia Partnerships & Pathways (IAPP) programme. The project focuses on pervasively adaptive software systems for the development of a modular computational INtelligence software platform For Evolving and Robust predictive systems applicable in various commercial settings and industries. The main innovation of the project is a novel type of environment in which the “fittest” predictive model for whatever purpose will emerge –either autonomously or by user high-level goal-related assistance and feedback. I act as the coordinator of the research and software development activities, I provide consultancy in the area of
computational intelligence to the software development team and I am a task leader for the meta-learning task. IT SOA - Service Oriented Architectures (2008-2013) The strategic goal of the project is to facilitate scientific research into innovative methods and tools for practical application of the Service-Oriented Architecture (SOA) paradigm in the development of modern IT solutions. The project aims to promote the competitiveness of Polish businesses, enhance the national e-Economy and assist in further development of the information society. Physically inspired methods and development of data-driven predictive
systems (fully-funded PhD project, 2007-2010) The research area was artificial intelligence and predictive modelling, with a focus on physical inspirations in machine learning. The main objective of the PhD research project was to explore and investigate some of the similarities between physical world and computational intelligence in order to find inspirations and design a new breed of nature–inspired machine learning techniques. The research intended to bridge the gaps between physics and machine learning and provide more efficient and intelligent means for fuller exploitation of evidence, which is available with varying quality and in varying quantities. |
Research projects |