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Cesare Furlanello

Head of Unit
  • Phone: +39 0461 314580
  • FBK Povo
Short bio

Cesare Furlanello received his degree in Mathematics at the University of Padua, Italy, in 1986. He is at Fondazione Bruno Kessler (Centre for Scientific and Technological Research of Trento) since 1987, now a Senior Researcher. He is currently leader of the MPBA Project (previously the ITC-IRST Neural Networks for Complex Data Analysis Project, since 1995).

In general terms, he is a data scientist, with main research interests in the interdisciplinary applications of machine learning methods to biomedical and environmental data. He is active in the field of bioinformatics, developing methods and software solutions to find patterns in very high throughput molecular data (such as Next Generation Sequencing and microarrays). He also have years of experience with machine learning and data analysis for geoinformatics, aiming at creating a bridge (geo-bioinformatics) between molecular profiles and spatial data structures. He has designed and managed many collaborative studies with life science researchers, in which math and software infrastructures are integrated to discover patterns in high-throughput datasets. He was first Project manager at IRST for the National Bioelectronics Project (1991-94), and he is currently PI and project manager of research projects in which Predictive Models are applied to Biomedical and Environmental data, for a total of 58 funded projects since 1988. These studies combine statistical machine learning methods with new sw infrastructures for data collection, management and distribution of the resulting models: Predictive Health Platforms and Geoinformatics platforms are thus the final outcome. The most recent research is directed to applications in functional genomics, including the development of computational pipelines and a focus to the problems of scientific reproducibility.

Basic and applied studies have been developed at the MPBA group with colleagues in other institutions on molecular oncology, vector-borne disease mapping, wildlife epidemiology, traffic safety, landscape risk analysis. CF has actively contributed to computational aspects, supporting the development of open source geoinformatics (GIS GRASS, WebGIS) and high performance machine learning (mlpy). Since 2002, he has contributed to the development of predictive classification models and gene selection procedures for molecular diagnostics, in collaboration with national and international centres of excellence in molecular oncology. He is a bioinformatics PI collaborator of international consortia such as the SEQC/MAQC FDA initiative and the FANTOM5 project led by the RIKEN OMICS centre. He has been a PI for AIRC with the IFOM-FIRC institute. He is also a collaborator PI in several projects of the Mach Foundation (FEM) for computational biology (metagenomics) and environmental mapping (climate change and plant genomics)

Several of the systems realized in experimental studies are now data platforms in use as infrastructures by public agencies: IET, MITRIS (Trentino and Friuli-VG), UXB-TN (Trentino), FaunaTN and FaunaBL (Trentino and Belluno) are the largest. The spinoff company MPA Solutions is mantaining these systems and developing WebGIS technologies with predictive modeling functions.

CF was Scientific secretary of the GNCB-CNR school on Neural Networks for Signal Processing (Trento 1989) and organizer of other workshops on Applications of Machine Learning and Neural Networks. In September 2008, he was Local Conference Chair of the MGED11 International Workshop of the MGED Society (in its Advisory Board since 2007) and he is now in the Board of Directors of the FGED society. Lecturer on Neural Network and Statistics at Master School of Advanced Information Science of Salerno University. Chairman of Session Theory 1 at IEEE NNSP-95 Cambridge MA, 1995. Member of the Scientific Board of the Multiple Classifier Systems series of conferences. Invited participant in the Machine Learning and Neural Networks Program of the Newton Institute of Mathematical Science, Cambridge UK, 1998. Member of the Italian Neural Network Society (serving in its Scientific Board 1991-2005), of the International Association for Pattern Recognition.

Invited lectures (a selection): NATO-ASI school Learning with Ensemble models (Vietri 2002), the ECEM/EAML Conference (Bled 2004), at the Int. BCB-Workshop on Machine Learning in Bioinformatics (Oct. 2005, Berlin), Int. School "The analysis of patterns" (Nov. 2005, Erice), "Predictive modeling on spatio-temporal patterns" (April 2007, Univ. Bristol), and "Signature Stability Analysis" (Nov 2007, Silver Springs, FDA).

He has been supervisor of 30 graduate or postgraduate theses for the universities of Trento (Mathematics and Engineering), Milano, Bologna, and Torino, supervisor of Leonardo graduate placements, tutor of 8 ASI-CONAE fellows in 2003-2012. Currently a supervisor of internships for Master thesis in Mathematics, Information and Telecommunication Engineering for the University of Trento, as well as a tutor for post-doc fellowships. Courses held: 1998-2003: Lecturer on COMPUTATIONAL STATISTICS AND PREDICTIVE MODELS, Math MsC, Trento University, and 2004-06: Lecturer on "Statistical Machine Learning", a course for the International Graduate School in ICT, Trento University. He is currently a member of the PhD School in Biomolecular sciences of UniTN.

He is a founder of the WEBVALLEY project, the FBK summer course for dissemination of interdisciplinary scientific research. Since 2001, CF is responsible for the WebValley Scientific program, and a resident tutor for all the 12 editions of this event. Developing the culture of data with open source platforms (web scripting, geodatabases, webGIS, tools for data visualization, statistical analysis decision making) based on a challenging project is the theme of 3 fast-paced weeks, in which about 20 high schools students team up with senior and junior researchers. In 2012, for this activity CF has been listed as "one of the 50 persons that are changing the world" by Wired, Italian edition (at #42, as in the H Guide).

  1. Annamaria Rizzoli; Stefano Merler; Cesare Furlanello; C. Chemini; C. Genchi,
    Geographical Information Systems and Bootstrap Aggregation (Bagging) of Tree-Based Classifiers for Lyme Disease Risk Assessment in Trentino, Italian Alps,
    , pp. 485 -
  2. Cesare Furlanello; Stefano Merler; Stefano Menegon; S. Mancuso; G. Bertiato,
    New WEBGIS technologies for geolocation of epidemiological data: an application for the surveillance of the risk of Lyme borreliosis disease,
    , pp. 241 -
  3. G.R. Hess; S.E. Randolph; P. Arneberg; C. Chmini; Cesare Furlanello; J. Harwood; M. Roberts; J. Swinton,
    Spatial aspects of disease dynamics,
  4. Bruno Caprile; Cesare Furlanello; Stefano Merler,
    Highlighting Hard Patterns via Adaboost Weights Evolution,
    Proceedings of the Third International Workshop on Multiple Classifier Systems [MCS 2002],
    , pp. 72-
  5. Cesare Furlanello; Stefano Merler; Giuseppe Jurman; Maria Serafini,
    Gene selection and classification with support vector machines applied to microarray data,
    Primo Workshop Nazionale sulla Bioinformatica,
  6. Stefano Merler; Cesare Furlanello; Annamaria Rizzoli; C. Chemini,
    Mapping tick borne diseases risk in Trentino, Italian Alps,
    Book of abstracts of the 4th International Conference on Ticks and Tick-borne Pathogens [TTP 4],
  7. Stefano Menegon; Steno Fontanari; Radim Blazek; Markus Neteler; Stefano Merler; Cesare Furlanello,
    Wildlife management and landscape analysis in the grass gis,
    Proceedings of the Open Source Free Software GIS - GRASS users conference 2002,
  8. Cesare Furlanello; Stefano Merler; Giuseppe Jurman; Maria Serafini,
    Entropy-based gene ranking without selection bias for the predictive classification of microarray data,
    We describe a ranking method for the identification of biomedically important genes for diagnosis and therapy, and provide an experimental schema for the development of predictive classification models on microarray data. This combined approach is designed to correct the selection bias problem (too optimistic prediction errors estimated by rules tested on samples previously used in the feature selection process). Our E--RFE ranking algorithm is a wrapper based on the entropy of the distribution of weights obtained from a SVM classifier. In order to control and speed-up the ranking process, E--RFE eliminates chunks of uninteresting genes until the remaining distribution stabilizes into higher entropy levels, then proceeding at shorter elimination steps as in the original recursive feature elimination RFE method. To control the selection bias, we use an external stratified partition resampling scheme and an internal K-fold cross-validation for the E--RFE feature ranking at each run. This double intensive model selection and error estimation process is made viable by a speed-up factor of 1/100 of E-RFE with respect to RFE, without a decrease of classification accuracy. The experimental scheme supports the identification of gene candidates with the highest contribute to predictive accuracy,
  9. Marco Visentin; Cesare Furlanello,
    Time Series Boosting for the Automatic Selection of panels in Marketing and Financial Studies,
  10. Cesare Furlanello; Stefano Merler; Stefano Menegon,
    Metodi informatici WebGIS per l'analisi e la sorveglianza epidemiologica delle infezioni trasmesse da zecche,