Carlotta Orsenigo

carlotta orsenigo

Carlotta Orsenigo is associate professor of Computer Science at Politecnico di Milano, where she teaches courses in Machine Learning, Predictive Analytics, and Optimization. After her graduation in Management Engineering at Politecnico di Milano in 2003, she started working as a research fellow at the Dep. of Management, Economics and Industrial Engineering. In 2005 she became assistant professor at the University of Milan, at the Dep. of Economics, Management and Quantitative Methods, where she was in charge of courses in Mathematics, Financial Mathematics and Business and Marketing Intelligence. From 2007 to 2013 she co-founded and was the scientific research responsible of the Business Intelligence Observatory (now Big Data & Business Analytics) of the School of Management at Politecnico di Milano. From 2015 she is co-director of the International Master in Business Analytics and Big Data at MIP-Politecnico di Milano. In 2018 she founded Aiblooms, a spin-off of Politecnico di Milano devoted to the development of software based on machine learning and artificial intelligence. In the same year, she co-founded the Milan chapter of Women in Machine Learning and Data Science (WiMLDS).
Her research activity has been focused on the development of novel models and methods for machine learning and pattern recognition and their application in several domains, ranging from biolife sciences to marketing and finance. Two major research strands were explored. From one side, the development of classification algorithms in the context of statistical learning theory. From the other, the design of nonlinear dimensionality reduction techniques for high-dimensional data embedding. More recently, research efforts have been focused on two areas. The first is text analytics for sentiment analysis through machine learning and deep learning algorithms, and the use of text classification methods for problems arising in innovation and entrepreneurship studies. The second is the development of novel supervised learning methods in the context of affective computing.

Contact

Dipartimento di Ingegneria Gestionale - Politecnico di Milano
Room: 2.02 - Phone: (+39)02-23993970
Homepage at SOM-Politecnico di Milano

Publications

Papers in ISI/SCOPUS journals

  • K.A. Arano, P. Gloor, C. Orsenigo, C. Vercellis. "Emotions are the Great Captains of our Lives": Measuring Moods through the Power of Physiological and Environmental Sensing. IEEE Transactions on Affective Computing, DOI: 10.1109/TAFFC.2020.3003736.
  • M. Jalayer, C. Orsenigo, C. Vercellis. Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms. Computers in Industry 125 (2021), pp. 103378.
  • E. Lettieri, C. Orsenigo. Predicting soccer consumption: do eSports matter? Empirical insights from a machine learning approach. Sport, Business and Management: An International Journal 10(5) (2020), pp. 523-544.
  • V. Butticè, M.G. Colombo, E. Fumagalli, C. Orsenigo. Green oriented crowdfunding campaigns: Their characteristics and diffusion in different institutional settings. Technological Forecasting and Social Change 141 (2019), pp. 85-97.
  • L. Malandri, F.Z. Xing, C. Orsenigo, C. Vercellis, E. Cambria. Public Mood–Driven Asset Allocation: the Importance of Financial Sentiment in Portfolio Management. Cognitive Computation 10(6) (2018), pp. 1167-1176.
  • C. Orsenigo, C. Vercellis. Anthropogenic influence on global warming for effective cost-benefit analysis: a machine learning perspective. Economia e Politica Industriale 45(3) (2018), pp. 425-442.
  • V. Butticè, C. Orsenigo, M. Wright. The effect of information asymmetries on serial crowdfunding and campaign success. Economia e Politica Industriale 45(2) (2018), pp. 143-173.
  • C. Orsenigo, C. Vercellis, C. Volpetti. Concatenating or Averaging? Hybrid Sentences Representations for Sentiment Analysis. Lecture Notes in Computer Science 11314 (2018), pp. 567-575.
  • C. Orsenigo. Effective MVU via Central Prototypes and Kernel Ridge Regression. In: Modeling Decisions for Artificial Intelligence. Lecture Notes in Artificial Intelligence 9321 (2015), pp. 143-154.
  • C. Orsenigo. An improved set covering problem for Isomap supervised landmark selection. Pattern Recognition Letters 49 (2014), pp. 131-137.
  • 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), pp. 101-114.
  • C. Orsenigo. Gene selection and cancer microarray data classification via mixed-integer optimization. In: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Lecture Notes in Computer Science 4973 (2008), pp. 141-152.
  • 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), pp. 365-377.
  • C. Orsenigo, C. Vercellis. Discrete support vector decision trees via tabu-search. Computational Statistics and Data Analysis 47 (2004), pp. 311-322.
  • C. Orsenigo, C. Vercellis. Multivariate classification trees based on minimum features discrete support vector machines. IMA Journal of Management Mathematics 14 (2003), pp. 221-234.
  • C. Orsenigo, C. Vercellis. One-against-all multicategory classification via discrete support vector machines. Management Information Systems 7 (2003), pp. 255-264.

Papers in international volumes and 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, pp. 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, pp. 305-325.

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.

Talks

International conferences and seminars

  • Invited speaker at the Social Media Days-Italy (Machine Learning seminar), Milan, 2018.
  • Invited speaker at Google Italy for the Google Machine Learning Day, Milan, 2018.
  • 10th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2013), Barcelona, November 2013, "Landmark selection for Isometric feature mapping based on mixed-integer optimization".
  • 5th IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2010), Nijmegen, September 2010, "Time series gene expression data classification via L1-norm temporal SVM". [videolectures]
  • International Conference on Operations Research, Munchen, September 2010, Kernel ridge regression for out-of-sample mapping in supervised manifold learning".
  • XXIV European Conference on Operational Research (EURO 2010), Lisbon, July 2010, "Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification".
  • XXIII European Conference on Operational Research (EURO 2009), Bonn, July 2009, "Cancer microarray data classification by discrete SVM with nonlinear kernels".
  • IX Conference of the Italian Society of Applied and Industrial Mathematics (SIMAI 2008), Roma, September 2008, "Discrete support vector machines: a family of classification methods based on mixed-integer optimization".
  • XXXIX Annual Conference of the Italian Operational Research Society (AIRO 2008), Ischia, September 2008, "Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification".
  • Sixth European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EVOBIO 2008), Naples, March 2008, "Gene selection and cancer microarray data classification via mixed-integer optimization".
  • International Workshop on Fuzzy Logic and Applications (WILF 2007), Lipsia, July 2007, "Fuzzy discrete support vector machines".
  • 8th Industrial Conference on Data Mining (ICDM 2007), Camogli, July 2007, "A new soft margin classifier based on discrete SVM".
  • INFORMS 2nd Artificial Intelligence and Data Mining Workshop (WAID 2007), Seattle, November 2007, "Classification of social networks entities by exploiting relational measures".
  • Fifth European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EVOBIO 2007), Valencia, April 2007, "Predicting HIV protease-cleavable peptides by discrete SVM" - Best Paper Award
  • INFORMS 1st Artificial Intelligence and Data Mining Workshop (WAID 2006), Pittsburgh, November 2006,"Time series classification by discrete support vector machines".
  • XXXVI Annual Conference of the Italian Operational Research Society (AIRO 2005), Camerino, September 2005, "Learning from few examples with MIP-based classifiers".
  • Mathematical methods for learning: advances in data mining and knowledge discovery (MML 2004), Como, June 2004, "One-against-all and round robin classification via discrete support vector machines".
  • 2nd International Workshop on Data Mining and Adaptive Modelling Methods for Economics and Management, Pisa, September 2004, "Hard separation in discrete support vector machines with relational marketing applications".
  • XXXV Annual Conference of the Italian Operational Research Society (AIRO 2004), Lecce, September 2004, "Discrete support vector machines for multicategory classification".
  • XXXIV Annual Conference of the Italian Operational Research Society (AIRO 2003), Venezia, September 2003, "Discrete support vector machines: a new family of classification models".

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