Irene POLI

Qualifica
Senior Researcher
E-mail
irenpoli@unive.it
SSD
STATISTICA [SECS-S/01]
Sito web
https://www.unive.it/persone/irenpoli (scheda personale)
Struttura
Centro Europeo Interuniversitario di Ricerca - European Center for Living Technology
Sito web struttura: https://www.unive.it/eclt
Sede: Ca' Bottacin
Struttura
Dipartimento di Scienze Ambientali, Informatica e Statistica
Sito web struttura: https://www.unive.it/dais
Sede: Campus scientifico via Torino
Research Institute
Research Institute for Complexity

Ricevimento

ricevimento su appuntamento

Didattica anno corrente

Didattica anni precedenti

Attività e competenze di ricerca

Pubblicazioni in evidenza

Slanzi, Debora; Poli, Irene; Jones, Roger D.; Mayer, Gert The combination of expert knowledge and Bayesian networks to identify the relevant factors in diabetic kidney disease (DKD) in STATISTICA APPLICATA, vol. 35, pp. 389-394 (ISSN 2038-5587)
DOI 2025, Articolo su rivista - Scheda ARCA: 10278/5110207


Abebe, Seyum; Poli, Irene; Jones, Roger D.; Slanzi, Debora Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning in MACHINE LEARNING AND KNOWLEDGE EXTRACTION, vol. 6, pp. 1798-1817 (ISSN 2504-4990)
DOI 2024, Articolo su rivista - Scheda ARCA: 10278/5069103


Slanzi, Debora; Silvestri, Claudio; Poli, Irene; Mayer, Gert Exploiting the Potential of Bayesian Networks in Deriving New Insight into Diabetic Kidney Disease (DKD) in Villani, M., Cagnoni, S., Serra, R., Artificial Life and Evolutionary Computation. WIVACE 2023., Springer, vol. 1977, pp. 298-308 (ISBN 9783031574290; 9783031574306) (ISSN 1865-0929)
DOI 2024, Articolo su libro - Scheda ARCA: 10278/5054921


Jones, Roger D; Abebe, Seyum; Distefano, Veronica; Mayer, Gert; Poli, Irene; Silvestri, Claudio; Slanzi, Debora Candidate composite biomarker to inform drug treatments for diabetic kidney disease in FRONTIERS IN MEDICINE, vol. 10, pp. 1271407 (ISSN 2296-858X)
DOI 2023, Articolo su rivista - Scheda ARCA: 10278/5044320


Mameli, Valentina; Slanzi, Debora; Poli, Irene; Green, Darren V S Search for relevant subsets of binary predictors in high dimensional regression for discovering the lead molecule in PHARMACEUTICAL STATISTICS, vol. 20, pp. 898-915 (ISSN 1539-1604)
DOI 2021, Articolo su rivista - Scheda ARCA: 10278/3738547


Tutte le pubblicazioni

Curriculum vitae

Irene Poli is Research Professor of Statistics at the European Centre for Living Technology (ECLT, www.ecltech.org), of which she is a founding member and currently Chair of the Science Board. She leads the ECLT Complexity and Data Analysis group, developing procedures to design informative experiments and to model high dimensional data.  She served as research scientist at the Imperial College of Science and Technology of London (UK), at the Centre for Non-linear Science (CNLS) of the Los Alamos National Laboratory (California University, USA), and at the Santa Fe Institute (NM, USA), and as Professor at the Universities of Bocconi, Bologna, Modena, and Ca’ Foscari. She is Fellow of the New York Academy of Science, the Bernoulli Society, the Italian Statistical Society, and the Royal Statistical Society. She is (or has been) Partner and Coordinator of several large interdisciplinary and international research projects, including Programmable Artificial Cell Evolution,  European Commission’s Directorate of Information Technologies (PACE, EU-FP6, www.istpace.org, 2004-2008); Designing Informative Combinatorial Experiments, Foundation Grant (DICE, 2006-2010); Development of systematic packages for deep energy renovation of residential and tertiart buildings including envelope and systems, European Commission’s  project (iNSPiRe - FP7-NMP-ENV-ENERGY-ICT-EeB, 2012-2016); New pathways for sustainable urban development in China’s medium-sized cities, European Commission's EuropeAid project (MEDIUM-EU-CHINA Research and Innovation Partnership ICI, 2015-2018); Building Lead Optimization Over large Molecular spaces  (BLOOM, Research Agreement with GlaxoSmithKlein, London, UK; 2014- now).  Her current research interests are in: statistical models for high dimensional small data, nonlinear time series, predictive neural networks; machine learning procedures; evolutionary designs for optimization.