www.unive.it/persone/irenpoli (personal record)
|Research team||Science of complex economic, human and natural systems|
Coordinatrice del team Science of complex economic, human and natural systems
Irene Poli is Professor of Statistics at Ca' Foscari University of Venice, Department of Environmental Science, Informatics and Statistics.
She served as research scientist at the Imperial College of Science and Technology of London (UK) (1982-84), at the Centre for Non-linear Science (CNLS) of the Los Alamos National Laboratory (California University, USA) (1988-90), and at the Santa Fe Institute (NM, USA) (1991-92).
She is Fellow of the New York Academy of Science, of the Bernoulli Society, of the Italian Statistical Society, and of the Royal Statistical Society. She has been member of the Scientific Committee of CIVEN (a University network devoted to the research in the field of bio-nanotechnologies - www.civen.org), and of the Doctoral School of Statistics in Padua.
She is currently Chair of the Science Board of the European Centre for Living Technology (ECLT, www.ecltech.org), and member of the Academic Senate of the Venice University.
She has been Coordinator and Partner in several large interdisciplinary and international research projects, including Programmable Artificial Cell Evolution, Founded by the European Commission’s – Directorate of Information Technologies - (PACE, www.istpace.org, 2004-2008); Designing Informative Combinatorial Experiments, at ECLT, Foundation Grant (DICE, 2006-2009)); Development of systematic packages for deep energy renovation of residential and tertiart buildings including envelope and systems, (iNSPiRe - Collaborative project - FP7-2012-NMP-ENV-ENERGY-ICT-EeB, 2012-2016); New pathways for sustainable urban development in China’s medium-sized cities, (MEDIUM-EuropeAid EU-CHINA Research and Innovation Partnership ICI+/2014/348-005, 2015-2018).
Her current research interests are in: high dimensional data analysis; statistical modelling for large data sets; nonlinear time series models; predictive neural networks; evolutionary computation in data analysis; dynamical complex systems.