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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. Stefano Merler; Cesare Furlanello,
    Machine learning on historic air photographs for mapping risk of unexploded bombs,
  2. Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
    An accelerated procedure for recursive feature ranking on microarray data,
    We describe a new wrapper algorithm for fast feature ranking in classification problems. The E-RFE (Entropy-based Recursive Feature Elimination) method eliminates chunks of uninteresting features according to the entropy of the weights distribution of a SVM classifier. With specific regard to DNA microarray datasets, the method is designed to support computationally intensive model selection in classification problems in which the number of features is much larger than the number of samples. We test E-RFE on synthetic and real data sets, comparing it with other SVM-based methods. The speed-up obtained with E-RFE supports predictive modeling on high dimensional microarray data,
  3. Cesare Furlanello; Maria Serafini; Stefano Merler; Giuseppe Jurman,
    Gene selection and classification by entropy-based recursive feature elimination,
    We analyse E-RFE (Entropy-based Recursive Feature Elimination), a new wrapper algorithm for fast feature ranking in classification problems. The E-RFE method operates the elimination of chunks of uninteresting features according to the entropy of the weights distribution of a SVM classifier. The method is designed to support computationally intensive model selection in classification problems in which the number of features is much largerthan the number of samples. We proofread the elimination procedure on synthetic data sets, and we demonstrate the applicability of E-RFE for the identification of biomedically important genes in predictive classification of microarray data,
  4. Paolo Brunetti; Roberto Flor; Steno Fontanari; Donato Minati; Cesare Furlanello; Gabriele Dallago; Alessandro Sorauf,
    Progettazione e gestion di hotspot IEEE 802.11b con strumenti GIS opensource: WiFiCamp 2003,
    Le tradizionali tecnologie cablate per l'accesso alle reti di calcolatori (LAN, MAN, WAN) sono state negli ultimi anni affiancate dai sistemi wireless, nei quali i dispositivi di calcolo (PC, portatili, PDA, smartphones) possono comunicare tra loro utilizzando il canale radio. Grazie a questi sistemi e' possibile offrire servizi di connettivita' verso le reti e in particolare ad Internet ad utenti che si muovono liberamente su vaste aree. Le aree in cui tali servizi sono offerti (locali pubblici, sale d'attesa di aeroporti, stazioni dei treni, centri per convegni, campus, musei,...) sono comunemente denominate hotspots. In questo lavoro si illustreranno le caratteristiche dei sistemi wireless, con particolare riferimento allo standard IEEE 802.11b, e si descriveranno le problematiche della progettazione, implementazione e valutazione delle prestazioni di hotspot, anche attraverso l'utilizzo di PDA Linux e di software GIS GRASS. In particolare, sara' descritto l'hotspot sperimentato all'interno del progetto WILMA, in occasione del a San Cristoforo di Pergine Valsugana (Trento), grazie al quale e' stato possibile servire un'area di circa 500.000 metri quadrati e raggiungere distanze fino a 1.2Km. Le sperimentazioni effettuate, per la valutazione dell'affidabilita' in condizioni limite del sistema realizzato, sono diffusamente descritte in [4],
  5. 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 -
  6. 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 -
  7. G.R. Hess; S.E. Randolph; P. Arneberg; C. Chmini; Cesare Furlanello; J. Harwood; M. Roberts; J. Swinton,
    Spatial aspects of disease dynamics,
  8. 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-
  9. 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,
  10. 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],