Carlo Vercellis

carlo vercellis

Carlo Vercellis is full professor of Computer Science at Politecnico di Milano, where he teaches courses in Optimization, Business Intelligence and Data Mining. He is heading door, the data mining and optimization research group, and the Observatory on Big Data Analytics & Business Intelligence.

His research interests include data mining and machine learning methods, such as support vector machines and nonlinear dimensionality reduction techniques; their application to relational marketing, social networks, biolife science; business intelligence and big data analytics; optimization models and methods, with applications to supply chain and revenue management. In the past he was involved in research on design and analysis of algorithms for combinatorial optimization, project management, transportation models.

He is author of more than 80 scientific writings, among which 6 books and more than 60 papers in high-rank international journals and books. His most recent book is: Business intelligence. Data mining and optimization for decision making. Wiley, 2009. He has been associate editor of several journals. He has coordinated several national and international research programs funded by EEC, CNR, MIUR and private companies. He co-organized several international conferences, and has been invited to discuss his research work in leading research institutions.

Previously, after his graduation in Mathematics at Università degli Studi di Milano, he has been with National Research Council (CNR), Bocconi University and Università degli Studi di Milano.

Contact

School of Management - Politecnico di Milano
Room: 2.03 - Phone: (+39)02-23992784
Homepage at Politecnico di Milano

Publications

Books

  • C. Vercellis. Business intelligence. Data mining and optimization for decision making. Wiley, 2009.
  • C. Vercellis. Ottimizzazione. Teoria, metodi, applicazioni. McGraw-Hill, 2008.
  • G. Felici, C. Vercellis. Mathematical methods for knowledge discovery and data mining. Idea Group, 2007.
  • C. Vercellis. Business intelligence. Modelli matematici e sistemi per le decisioni. McGraw-Hill, 2006.
  • C. Vercellis. Modelli e decisioni. McGraw-Hill, 2006.
  • C.Vercellis. Modelli e decisioni. Strumenti e metodi per le decisioni aziendali. Esculapio, 1997.

Papers in ISI/SCOPUS journals

  • C. Orsenigo, C. Vercellis. Linear versus nonlinear dimensionality reduction for banks' credit rating prediction. Knowledge-Based Systems 47 (2013), pp. 14-22.
  • C. Orsenigo, C. Vercellis. A comparative study of nonlinear manifold learning methods for cancer microarray data classification. Expert Systems with Applications 40 (2013), pp. 2189-2197.
  • C. Orsenigo, C. Vercellis. Landmark Selection for Isometric Feature Mapping Based on Mixed-Integer Optimization. In: Modeling Decisions for Artificial Intelligence. Lecture Notes in Computer Science 8234 (2013), pp. 260-271.
  • C. Orsenigo, C. Vercellis. Dimensionality Reduction via Isomap with Lock-Step and Elastic Measures for Time Series Gene Expression Classification. In: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Lecture Notes in Computer Science 7833 (2013), pp. 92-103.
  • C. Orsenigo, C. Vercellis. Regularization through fuzzy discrete SVM with applications to customer ranking. Journal of Intelligent & Fuzzy Systems 23 (2012), pp. 101-110.
  • C. Orsenigo, C. Vercellis. Kernel ridge regression for out-of-sample mapping in supervised manifold learning. Expert Systems with Applications 39 (2012), pp. 7757-7762.
  • C. Orsenigo, C. Vercellis. An effective double-bounded tree-connected Isomap algorithm for microarray data classification. Pattern Recognition Letters 33 (2012), pp. 9-16.
  • C. Orsenigo, C. Vercellis. Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification. Pattern Recognition (2010), pp. 3787-3794.
  • C. Orsenigo, C. Vercellis. Time series gene expression data classification via L1-norm temporal SVM. In: Pattern Recognition in Bioinformatics. Lecture Notes in Computer Science 6282 (2010), pp. 264-274.
  • C. Orsenigo, C. Vercellis. Multicategory classification via discrete support vector machines. Computational Management Science 6 (2009), 101-114.
  • C. Orsenigo, C. Vercellis. Accurately learning from few examples with a polyhedral classifier. Computational Optimization and Applications 38 (2007), pp. 235-247.
  • C. Orsenigo, C. Vercellis. Evaluating membership functions for fuzzy discrete SVM. In: Applications of fuzzy sets theory. Lecture Notes in Artificial Intelligence 4578 (2007), pp. 187-194.
  • C. Orsenigo, C. Vercellis. Softening the margin in discrete SVM. In: Advances in Data Mining. Lecture Notes in Artificial Intelligence 4597 (2007), pp. 49-62.
  • C. Orsenigo, C. Vercellis. Predicting HIV protease-cleavable peptides by discrete support vector machines. In: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Lecture Notes in Computer Science 4447 (2007), pp.197-206.
  • C. Orsenigo, C. Vercellis. A Bayesian stopping rule for greedy randomized procedures. Journal of Global Optimization 36 (2006), 365-377.
  • C. Orsenigo, C. Vercellis. Discrete support vector decision trees via tabu-search. Computational Statistics and Data Analysis 47 (2004), 311-322.
  • C. Orsenigo, C. Vercellis. Multivariate classification trees based on minimum features discrete support vector machines. IMA Journal of Management Mathematics 14 (2003), 221-234.
  • D. La Torre, C. Vercellis. C1,1 approximations of generalized support vector machines. Journal of Concrete and Applicable Mathematics 1 (2003), 125-134.
  • C. Orsenigo, C. Vercellis. One-against-all multicategory classification via discrete support vector machines. Management Information Systems 7, 2003, 255-264.
  • C. Vercellis. Combining Data Mining and Optimization for Campaign Management. Management Information Systems 6, 2002, 61-71.
  • A. Dumoulin, C. Vercellis. Tactical models for hierarchical capacitated lot-sizing problems with setups and changeovers. International Journal of Production Research 38 (2000), 51-67.
  • F. Fumero, C. Vercellis. Synchronized development of production, inventory and distribution schedules. Transportation Science 33 (1999), 330-340.
  • C. Vercellis. Multi-plant production planning in capacitated self-configuring two-stage serial systems. European Journal of Operational Research 119 (1999), 451-460.
  • F. Fumero, C. Vercellis. Integrating distribution, lot-sizing and machine loading via Lagrangean relaxation. International Journal of Production Economics 49 (1997), 45-54.
  • F. Fumero, C. Vercellis. Capacity management through Lagrangean relaxation: an application to tires production. Production Planning and Control 7 (1996), 604-614.
  • A. Marchetti-Spaccamela, C. Vercellis. Stochastic on-line knapsack problems. Mathematical Programming 68 (1995), 73-104.
  • C. Vercellis. Constrained multi-project planning problems: a Lagrangean decomposition approach. European Journal of Operational Research 78 (1994), 267-275.
  • F. Fumero, C. Vercellis. Capacity analysis in repetitive assemble-to-order manufacturing systems. European Journal of Operational Research 78 (1994), 204-215.
  • F. Stella, C. Vercellis, M. Zaffalon. A GAP formulation for solving production planning problems in Telecom industry. Operations Research 1994, Springer, 1995, 324-328.
  • A.H.G. Rinnooy Kan, L. Stougie, C. Vercellis. A class of generalized greedy algorithms for the multi-knapsack problem. Discrete Applied Mathematics 42 (1993), 279-290.
  • M.G. Speranza, C. Vercellis. Hierarchical models for multi-project planning and scheduling. European Journal of Operational Research 64 (1993), 312-325.
  • M.G. Speranza, C. Vercellis. A hierarchical multiobjective approach to project management. Multiple criteria decision support, Springer, 1991, 191-204.
  • C. Vercellis. Multi-criteria models for capacity analysis and aggregate planning in manufacturing systems. International Journal of Production Economics 23 (1991), 261-272.
  • M. Meanti, A.H.G. Rinnooy Kan, L. Stougie, C. Vercellis. A probabilistic analysis of the multiknapsack value function. Mathematical Programming 46 (1990), 237-248.
  • P.M. Camerini, F. Maffioli, C. Vercellis. Multi-constrained matroidal knapsack problems. Mathematical Programming 45 (1989), 211-231.
  • E. Tosini, C. Vercellis. An interactive approach to crew scheduling problems. Computer-aided transit scheduling, Springer, 1988, 41-53.
  • A. Marchetti Spaccamela, C. Vercellis. Efficient on-line algorithms for knapsack problems. Automata, languages and programming. Springer, 1987, 445-456.
  • B. Betr?, C. Vercellis. Bayesian nonparametric inference and Monte Carlo optimization. Optimization 17 (1986), 681-694.
  • F. Archetti, A. Sciomachen, C. Vercellis. Optimal design of a remote heating network. System modelling and optimization. Springer, 1986, 25-33.
  • C. Vercellis. A probabilistic analysis of the set packing problem. Stochastic programming. Springer, 1986, 272-285.
  • A. Frigessi, C. Vercellis. A probabilistic analysis of Monte Carlo algorithms for a class of counting problems. Stochastic programming. Springer, 1986, 272-285.
  • C. Vercellis. A threshold for multiple edge coverings in random hypergraphs. Annals of Discrete Mathematics 25 (1985), 311-320.
  • A. Frigessi, C. Vercellis. An analysis of Monte Carlo algorithms for counting problems. Calcolo 27 (1985), 413-428.
  • P.M. Camerini, C. Vercellis. The matroidal knapsack: a class of (often) well-solvable problems. Operations Research Letters 3 (1984), 157-162.
  • C. Vercellis. A probabilistic analysis of the set covering problem. Annals of Operations Research 1 (1984), 255-271.
  • F. Maffioli, M.G. Speranza, C. Vercellis. Randomized algorithms: an annotated bibliography. Annals of Operations Research 1 (1984), 331-345.
  • F. Archetti, C. Vercellis. An application of nonlinear programming techniques to the energy-economic optimization of buildings design. Optimization techniques. Springer, 1980, 569-576.

Papers in international books

  • C. Orsenigo, C. Vercellis. Protein folding classification through multicategory discrete SVM In: Mathematical Methods for Knowledge Discovery and Data Mining, G. Felici and C. Vercellis eds., IGI, 2007, 116-129 .
  • C. Orsenigo, C. Vercellis. Rules induction through discrete support vector decision trees. In: Data Mining and Knowledge Discovery. Approaches Based on Rule Induction Techniques, E. Triantaphyllou and G. Felici eds., Springer, 2006, 305-325.
  • F. Fumero, C. Vercellis. Investigating the determinants of sales agents efficiency via data envelopment analysis. Operations Research 2000, Springer, 2000, 471-476.
  • C.G.E. Boender, A.H.G. Rinnooy Kan, C. Vercellis. Stochastic optimization methods. Stochastics in combinatorial optimization. World Scientific, 1987, 94-112.
  • F. Maffioli, M.G. Speranza, C. Vercellis. Randomized algorithms. Combinatorial Optimization. Wiley, 1985, 89-105.
  • F. Archetti, A. Frigessi, C. Vercellis. Variance reduction techniques in Monte Carlo evaluation of the reliability of stochastic networks. Applied Optimization Techniques in Energy Problems. Teubner, 1985, 63-79.
  • F. Archetti, D. Ballabio, C. Vercellis. Cost-benefit analysis of insulation in buildings via nonlinear optimization. Numerical Optimization of Dynamic Systems. North Holland, 1980, 363-375.

Papers in international conference proceedings

  • C. Orsenigo, C. Vercellis. Classification of social networks entities by exploiting relational measures. INFORMS Proc. Artificial Intelligence and Data Mining Workshop, Seattle, 2007.
  • C. Orsenigo, C. Vercellis. Time series classification by discrete support vector machines. INFORMS Proc. Artificial Intelligence and Data Mining Workshop, Pittsburgh, 2006, 1-6.
  • C. Orsenigo, C. Vercellis. Hard separation in discrete support vector machines with relational marketing applications. Proc. 2nd Int. Workshop on Data Mining and Adaptive Modelling Methods for Economics and Management, Pisa, 2004, 111-123.

Top

Back