Curriculum vitæ of Dario Malchiodi

Personal information

Dario Malchiodi
Dipartimento di Informatica - Università degli Studi di Milano
Room P102 - Via Comelico 39/41 - 20135 Milano ITALY
Mail: my last name at di dot unimi dot it
Web: http://malchiodi.di.unimi.it
phone: +39 02 503 16338 skype: dariomalchiodi fax: +39 02 503 16276 e-fax: +39 02 700 30976
Social networks: Academia.edu, ResearchGate, coderwall, Codecademy, Facebook, Twitter, Google +, LinkedIn, Naymz, Personal blog, I use this, Klout, Kaggle, Koding
PGP public key (updated 2006/05/04)

Birth: Milano IT, 8/12/1970
Citizenship: Italian
Tax code: MLC DRA 70T08 F205 O

Key words

Machine learning - Data quality in learning - Probability and mathematical statistics

Current position

Since 2011 I am associate professor at the Computer Science Department of the Milan University.

Previous positions

2002 > 2011
Assistant professor at the Computer Science Department of the Milan University.
2001 > 2002
Research assistant at the Computer Science Department of the Milan University, within the Neural Networks Laboratory.
2000 > 2001
Software architect at Inferentia-DNM, with the job of designing statistical and neural architectures for financial forecasts.
1997 > 2000
Statistical analyst at The Continiuity Company S.r.l., within a R&D project on flexible regression models for financial data.
1996 > 1997
Software developer in a division of Olivetti S.p.A..

Education

2000
PhD in Computational Mathematics and Operations Research, Department of Mathematics, Milan University.
1996
Bachelor (cum laude) in Computer Science, Department of Computer Science, Milan University.
1994
Master in Administration of a Unix lab, Centro di formazione B. Vigorelli, Regione Lombardia.
1994
Master in Multimedia programming with Motif and C, Centro di formazione B. Vigorelli, Regione Lombardia.

Research activities of Dario Malchiodi

My research activities focus around the treatment of uncertainty in machine learning problems, with the aim of strenghtening the aspects belonging to the fields of computer science and statistics.

Negative examples selection in bioinformatics

The application of supervised machine learning methods in bioinformatics requires the selection among non-positively labeled data of those representing reliable negative examples, that is excluding entities on which no experiments have been conducted. In [Frasca and Malchiodi, 2017; Frasca and Malchiodi, 2016] such negative selection problem has been tackled using a ranking based on fuzzy membership functions, while [Frasca et al., 2017] proposes an encoding for the available data promoting the negative selection process in the problem of protein functions prediction. Finally, a similar procedure has been proposed in [Frasca et al., 2017] for the problem of gene prioritization.

Data quality-based learning

Machine learning models have as starting point a labeled sample whose elements are processed homogeneously (that is, each element has the same importance). In [Malchiodi, 2008] the general model of data quality-based learning was proposed. In this model it is possible to associate each of the available data items a numerical quantification of its importance with reference to the remaining data. This model was applied to the problem of classification through Support Vector Machines, both in its linear [Apolloni and Malchiodi, 2006] and kernel-based version [Apolloni et al., 2007]. A first analysis of the performance for these applications has been undertaken both theoretically [Apolloni et al., 2007] and experimentally [Malchiodi, 2009]. Some preliminary applications in the bioinformatics field is described in [Malchiodi et al., 2010]. A similar approach has also been applied to the regression problem in [Apolloni et al., 2010; Malchiodi et al., 2009; Apolloni et al., 2005] and to unbalanced learning in [Malchiodi, 2013b].

Design of learning algorithms

Several types of learning algorithms have been designed, implemented and analyzed. In particular, [Malchiodi and Legnani, 2014] proposes an improvement of the support vector-based classification algorithms dealing both with partially labeled data and with uncertain labels, while [Malchiodi and Pedrycz, 2013] introduces a learning algorithm for membership functions of fuzzy sets.

Popularization of informatics culture

The perception of computer science, in society and in primary and secondary education, is often linked almost exclusively to the introduction to specific technological tools rather than to the study and processing of information [Bellettini et al., 2014]. In order to sensitize teachers to a different approach to basic computer science education, a methodology based on interactive laboratories, which is currently being tested, has been proposed in [Bellettini et al., 2012; Bellettini et al., 2013; Bellettini et al., 2014; Bellettini et al., 2014].

Analysis of relations between granular computing and machine learning

The granular computing model, giving information a granular meaning and allowing its analysis and its processing at different abstraction levels, is described in [Apolloni et al., 2008], where its links with machine learning models are analysed. The effects of a fusion of these two models have been studied within the general field of regression, proposing new algorithms based on Support Vector Machines [Apolloni et al., 2008; Apolloni et al., 2006] or on local search techniques [Apolloni et al., 2005].

Bootstrap techniques for regression algorithms

Bootstrap techniques are based on data resampling models with the aim of approximating the distribution of a population. A specialization of this kind of techniques, intially proposed in [Apolloni et al., 2006] and subsequently refined in [Apolloni et al., 2009; Apolloni et al., 2007], gives as output confidence regions for regression curves, avoiding usual assumptions on the distribution of measurement drifts. The use of this technique to solve linear and nonlinear regression problems is shown in [Apolloni et al., 2008], while [Apolloni et al., 2007] describes some applications to the medical field.

Development of inference models for machine learning problems

The task of integrating under a unique theoretical model istances of inference problems from statistics (point and interval estimation of distribution parameters) and computer science (estimation of approximation error in machine learning) is tackled in [Apolloni et al., 2006; Apolloni et al., 2005; Apolloni et al., 2002; Apolloni et al., 2002; Apolloni and Malchiodi, 2001; Malchiodi, 2000], building on previously obtained results on sample complexity [Apolloni and Malchiodi, 2001] and describing the Algorithmic Inference model. This model was used with the aim of estimating the risk in classification problems based on Support Vector Machines [Apolloni et al., 2007; Apolloni et al., 2005; Apolloni and Malchiodi, 2002; Apolloni and Malchiodi, 2001], learning confidence regions for regression lines avoiding the typical assumption requiring a Gaussian drift distribution [Apolloni et al., 2005; Apolloni et al., 2002], and learning confidence regions for the risk function of re-occurrence distribution times in particular cancer pathologies [Apolloni et al., 2007; Apolloni et al., 2005; Apolloni et al., 2002].

Applications of systems for scientific computation

Systems for scientific computation can be used to run simulations and to analyze mathematical problems from an interactive and incremental point of view; To this effect, such systems offer interesting cues in order to design educational activities [Bulgheroni and Malchiodi, 2009; Malchiodi, 2008a]. A commercial version of this kind of systems, thoroughly described in [Malchiodi, 2007], has been extended so as to solve purely computational aspects associated to information encoding [Malchiodi, 2006c], remote procedure invocation [Malchiodi, 2006b; Malchiodi, 2006], production of scientific documentation [Malchiodi, 2011], and solutions to optimization [Malchiodi, 2006a] and machine learning problems based on Support Vectors [Malchiodi et al., 2009; Malchiodi et al., 2009], as well as to perform software validation techniques [Malchiodi, 2013a]. The related code has been used in order to build up the simulations in [Apolloni et al., 2007; Apolloni and Malchiodi, 2006]. Moreover, [Malchiodi, 2010a] describes a library handling machine learning problems within an open source system for scientific computation.

Design of hybrid learning systems

Hybrid learning systems are typically organized coupling sub-symbolic modules (typically based on the neural networks paradigm) with symbolic ones (described in terms of logic circuits). Such a system, having as inputs a set of features describing the available data and extracting their boolean independent components, is described in [Apolloni et al., 2005; Apolloni et al., 2004]. These components, interpreted as truth values, are used in order to infer logical formulas describing in a symbolic ways the relations among original input data [Apolloni et al., 2006; Apolloni et al., 2003; Apolloni et al., 2002; Apolloni et al., 2000]. This system is applied in [Apolloni et al., 2004] to the problem of emotion recognition on the basis of voice signals, while [Apolloni et al., 2004; Apolloni et al., 2004; Apolloni et al., 2003; Apolloni et al., 2003; Apolloni et al., 2003] describes an applications to the monitoring of awareness in car driving in function of biosignals, within the research project IST-2000-26091 ORESTEIA (mOdular hybRid artEfactS wiTh adaptivE functIonAlity, funded between 2001 and 2003 by the EC within the fifth framework programme, under the IST-FET initiative). Moreover, [Apolloni and Malchiodi, 2006; Apolloni et al., 2005] study two hybrid systems obtained through the integration of a fuzzy system for the measurement of quality in available data respectively with a linear Support Vector classifier and with a linear regression model.

Automatic simplification of symbolic descriptions

Whithin computational learning theory, the structural risk minimization principle investigates on the problem of balancing the complexity of a model with its accuracy in describing experimental data. This principle has been applied to classifiers based on logic expressions built in terms of disjuctive and conjunctive boolean normal forms. A simplification algorithm for such forms was developed in [Apolloni et al., 2006; Apolloni et al., 2005; Apolloni et al., 2003; Apolloni et al., 2002; Apolloni et al., 2002], focusing on the stochastic optimization of parameters in fuzzy sets describing the above mentioned forms.

Study of population dynamics

Within this subject the activities have been focused on the problem of modeling conflicting situations through an approach alternative to that of classical game theory. In particular, these conflicts were modeled in terms of approximating the solution to an NP-hard problem [Apolloni et al., 2006; Apolloni et al., 2003; Apolloni et al., 2002; Apolloni et al., 2002], applying the Algorithmic Inference model in order to assign limited computational resources to two players, subsequently extending this technique to team games [Apolloni et al., 2006]. This model is applied in [Apolloni et al., 2007; Apolloni et al., 2005] to the biologic field, while [Apolloni et al., 2010] uses this approach with the aim of correctly dimensioning the running time for learning algorithms based on local error minimization.

Intelligent systems for pervasive and ubiquitous computing

The research project ORESTEIA (mOdular hybRid artEfactS wiTh adaptivE functIonAlity, funded between 2001 and 2003 by the EC within the fifth framework programme, under the IST-FET initiative) was grounded on the design, implementation and analysis of intelligent systems for pervasive and ubiquitous computing. These fields are characterized by highly specialized computers devoted to execute specific tasks. These special computers can be produced so as to significantly reduce their size and cost, consequently being able to immerse them inside an environment. Focusing specifically on the awareness detection problem [Kasderidis et al., 2003], a prototype for the detection of driving awareness on the basis of biosignals [Apolloni et al., 2004; Apolloni et al., 2004; Apolloni et al., 2003; Apolloni et al., 2003; Apolloni et al., 2003] have been developed.

Automatic classification of emotions

Within the progress of reserach project PHYSTA (Principled Hybrid Systems: Theory and Applications, funded between 1998 and 2000 by the EC within the fourth framework programme, within the TMR initiative), the Algorithmic Inference model described in [Apolloni et al., 2006; Malchiodi, 2000] was applied to the problem of automatic classification of emotions on the basis of vocal signals [Apolloni et al., 2004; Apolloni et al., 2002]. The obtained results were presented at an international school on computational learning within the same research project.

Design of hardware-implementable statistics

The availability of hardware circuits able to directly process information with the aim of synthesizing them through estimators allow a remarkable shortening in running times. Their use imply a set of constraints basically linked to the architecture of the circuits themselves. The inference-among-gossips, developed in [Malchiodi, 1996], has been applied within this scope with the aim of obtaining a family of estimators for bernoulli populations directly implementable on pRAM boards [Apolloni et al., 1997]. The same model has been applied in [Apolloni et al., 2013] to the study of information exchange in social networks.

Membership to research groups and research projects

2008 >
ALaDDIn group
2002 >
Italian Association of University Professors of Computer Science (GRIN)
2015 > 2017
Project SMILE (Slow down, Move your body, Improve your diet, Learn for life, and Enjoy school time, funded by the European Commission under the Erasmus+ programme)
2012 > 2015
Project SandS (Social AND Smart, funded by the European Commission under the seventh framework programme)
2012 > 2014
Project VIOPE: Learning computer programming in virtual environment funded by the European Commission under the sixth framework programme.
2008 > 2013
PASCAL 2 network of excellence, funded by the European Commission
2002 > 2013
Italian Society for Neural Networks
1996 > 2011
Neural Networks Laboratory at the Computer Science Department, Milan University
2005 > 2008
Network of excellence PASCAL: Pattern Analysis, Statistical Modeling and Computational Learning, funded by the European Commission
2002 > 2004
Project Stochastic processes, funded by the Italian Ministry for University and Research
2001 > 2003
IST-FET research project ORESTEIA (mOdular hybRid artEfactS wTh adaptivE funtIonAlity, funded by the European Commission under the fifth framework programme)
1998 > 2000
TMR research project PHYSTA (Principled Hybrid Sistems: Theory and Applications, funded by the European Commission under the fourth framework programme)
2000
Project Statistical and Neural Methods supporting decisions in finance, funded under the grant Young Researchers at the Milan UniversityProgetto Metodi statistici e neurali di supporto alle decisioni in ambito finanziario, finanziato dal Inferentia-DNM
2000
Project Statistical and Neural Methods for population dynamics, funded under the grant Young Researchers at the Milan University
1999
Project Spatial stochastic processes, funded by the Italian Ministry for University and Research

Publications of Dario Malchiodi

Books

[Monga et al., 2017] Monga M., Malchiodi D., Morpurgo A. and Torelli M., Turing: la nascita dell'intelligenza artificiale, Corriere della Sera, Grandangolo Scienza, 2017 [BIBTEX]
[Malchiodi, 2015] Malchiodi D., Sistemi operativi – esercizi risolti e commentati, (ISBN 978-88-91091-41-3), 2015 [book page BIBTEX]
[Apolloni et al., 2008] Apolloni B., Pedrycz W., Bassis S. and Malchiodi D., The Puzzle of Granular Computing, Berlin: Springer, Studies in Computational Intelligence, Vol. 138 (ISBN 978-3-540-79863-7), 2008 [publisher BIBTEX]
[Malchiodi, 2007] Malchiodi D., Fare matematica con Mathematica, Milano: Pearson Addison Wesley (ISBN 978-88-7192-365-9), 2007, in italian [book-page publisher BIBTEX]
[Apolloni et al., 2006] Apolloni B., Malchiodi D. and Gaito S., Algorithmic Inference in Machine Learning, 2nd Edition, Magill, Adelaide: Advanced Knowledge International, International Series on Advanced Intelligence, Vol. 5 (ISBN 0-9751004-2-4), 2006 [publisher BIBTEX]

Papers in international journals

[Baraté et al., 2017] Baraté A., Ludovico L. A. and Malchiodi D., Fostering Computational Thinking in Primary School through a LEGO®-based Music Notation, Procedia computer science 112 (2017), 1334–1344, Special issue: KES 2017 - Proceedings of the 21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems [doi> BIBTEX]
[Frasca and Malchiodi, 2017] Frasca M. and Malchiodi D., Exploiting Negative Sample Selection for Prioritizing Candidate Disease Genes, Genomics and Computational Biology 3 - 3 (2017), e47 [doi> BIBTEX]
[Bellettini et al., 2014] Bellettini C., Lonati V., Malchiodi D., Monga M., Morpurgo A., Torelli M. and Zecca L., Informatics Education in Italian Secondary School, ACM Transactions on Computing Education (TOCE) – Special Issue on Computing Education in (K-12) Schools 14 - 2 (2014), 15.1–15.6 [doi> BIBTEX]
[Apolloni et al., 2013] Apolloni B., Malchiodi D. and Taylor J. G., Learning by Gossip: A Principled Information Exchange Model in Social Networks, Cognitive Computation 5 - 3 (2013), 327-339 [doi> BIBTEX]
[Apolloni et al., 2010] Apolloni B., Malchiodi D. and Valerio L., Relevance regression learning with support vector machines, Nonlinear Analysis 73 (2010), 2855-2867 [doi> BIBTEX]
[Apolloni et al., 2010a] Apolloni B., Bassis S., Gaito S., Malchiodi D. and Zoppis I., Playing monotone games to understand learning behaviors, Theoretical Computer Science 411 - 25 (2010), 2384-2405 [doi> BIBTEX]
[Apolloni et al., 2009] Apolloni B., Bassis S. and Malchiodi D., Compatible worlds, Nonlinear Analysis: Theory, Methods & Applications 71 - 12 (2009), e2883-e2901 [doi> BIBTEX]
[Malchiodi, 2009] Malchiodi D., An experimental analysis of the impact of accuracy degradation in SVM classification, International Journal of Computational Intelligence Studies 1 - 2 (2009), 163-190 [doi> BIBTEX]
[Apolloni et al., 2008a] Apolloni B., Bassis S., Malchiodi D. and Pedrycz W., Interpolating Support Information Granules, Neurocomputing 71 (2008), 2433-2445 [doi> BIBTEX]
[Apolloni et al., 2008b] Apolloni B., Bassis S., Gaito S. and Malchiodi D., Bootstrapping Complex Functions, Nonlinear Analysis: Hybrid Systems 2 - 2 (2008), 648-664 [doi> BIBTEX]
[Malchiodi, 2008] Malchiodi D., Embedding Sample Points Uncertainty Measures in Learning Algorithms, Nonlinear Analysis: Hybrid Systems 2 - 2 (2008), 635-647 [doi> BIBTEX]
[Apolloni et al., 2007] Apolloni B., Bassis S., Gaito S. and Malchiodi D., Solving complex regression problems via Algorithmic Inference: a new family of bootstrap algorithms, Far East Journal of Theoretical Statistics 22 - 2 (2007), 141-180 [BIBTEX]
[Apolloni et al., 2007a] Apolloni B., Bassis S., Clivio A., Gaito S. and Malchiodi D., Modeling individual's aging within a bacterial population using a pi-calculus paradigm, Natural Computing 6 - 1 (2007), 33-53 [doi> BIBTEX]
[Apolloni et al., 2007b] Apolloni B., Bassis S., Gaito S. and Malchiodi D., Appreciation of medical treatments by learning underlying functions with good confidence, Current Pharmaceutical Design 13 - 15 (2007), 1545-1570 [ BIBTEX]
[Apolloni et al., 2006a] Apolloni B., Brega A., Malchiodi D., Palmas G. and Zanaboni A. M., Learning Rule Representations From Data, IEEE Transactions on Systems, Man and Cybernetics, Part A 36 - 5 (2006), 1010-1028 [doi> BIBTEX]
[Apolloni et al., 2006b] Apolloni B., Bassis S., Gaito S. and Malchiodi D., Elementary team strategies in a monotone game, Nonlinear Analysis 64 - 2 (2006), 310-328 [doi> BIBTEX]
[Apolloni et al., 2006c] Apolloni B., Bassis S., Gaito S., Malchiodi D. and Zoppis I., Controlling the losing probability in a monotone game, Information Sciences 176 - 10 (2006), 1395-1416 [doi> BIBTEX]
[Apolloni et al., 2004] Apolloni B., Esposito A., Malchiodi D., Orovas C., Palmas G. and Taylor J. G., A General Framework for Learning Rules From Data, IEEE Transactions on Neural Networks 15 - 6 (2004), 1333-1349 [doi> BIBTEX]
[Apolloni et al., 2002] Apolloni B., Malchiodi D., Orovas C. and Palmas G., From synapses to rules, Cognitive Systems Research 3 (2002), 167-201 [doi> BIBTEX]
[Apolloni and Malchiodi, 2001] Apolloni B. and Malchiodi D., Gaining degrees of freedom in subsymbolic learning, Theoretical Computer Science 255 (2001), 295-321 [doi> BIBTEX]
[Apolloni et al., 1997] Apolloni B., Malchiodi D. and Taylor J. G., Functional bootstrap: a hardware constrained implementation of on-line bootstrap, InterStat October (1997) [on-line access BIBTEX]

Papers in international conference proceedings

[Lonati et al., 2017] Lonati V., Malchiodi D., Monga M. and Morpurgo A., How presentation affects the difficulty of computational thinking taksk: an IRT analysis, in Proceedings of 17th Koli Calling International Conference on Computing Education Research, ACM, 2017, in press [BIBTEX]
[Calcagni et al., 2017] Calcagni A., Lonati V., Monga M., Morpurgo A. and Malchiodi D., Promoting computational thinking skills: would you use this Bebras task?, in The 10th International Conference on Informatics in Schools, 2017, in press [BIBTEX]
[Ludovico et al., 2017] Ludovico L. A., Malchiodi D. and Zecca L., A Multimodal LEGO®-based Learning Activity Mixing Musical Notation and Computer Programming, in 19th ACM International Conference on Multimodal Interaction, ACM, 2017, in press [BIBTEX]
[Frasca et al., 2017] Frasca M., Fontaine J. Fred, Valentini G., Mesiti M., Notaro M., Malchiodi D. and Andrade-Navarro M., Disease-Genes must Guide Data Source Integration in the Gene Prioritization Process, in Proceedings of CIBB 2017 - 14th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2017, in press [BIBTEX]
[Lonati et al., 2017a] Lonati V., Malchiodi D., Monga M. and Morpurgo A., Learning Greedy Strategies at Secondary Schools: An Active Approach, in A. Sforza and C. Sterle (Eds.), Optimization and Decision Science Methodologies and Applications, Springer, Proceedings in Mathematics & Statistics (ISBN 978-3319673973), 2017, in press [doi> BIBTEX]
[Lonati et al., 2017b] Lonati V., Malchiodi D., Monga M. and Morpurgo A., Bebras as a teaching resource, in ITiCSE '17 Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education, ACM (ISBN 9781450347044), 366–366, 2017 [BIBTEX]
[Lonati et al., 2017c] Lonati V., Malchiodi D., Monga M. and Morpurgo A., Nothing to fear but fear itself: introducing recursion in lower secondary schools, in Proceedings of Fifth International Conference on Learning and Teaching in Computing and Engineering, LATICE 2017, 2017, in press [BIBTEX]
[Frasca et al., 2017b] Frasca M., Lipreri F. and Malchiodi D., Analysis of Informative Features for Negative Selection in Protein Function Prediction, in I. Rojas and F. Ortuño (Eds.), Bioinformatics and Biomedical Engineering 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part II, Vol. 10209, 2017 [doi> BIBTEX]
[Baratè et al., 2017] Baratè A., Formica A., Ludovico L. A. and Malchiodi D., Fostering Computational Thinking in Secondary School through Music: An Educational Experience based on Google Blockly, in P. Escudeiro, G. Costagliola, S. Zvacek, J. Uhomoibhi and B. M. McLaren (Eds.), Proceedings of the 9th International Conference on Computer Supported Education, SCITEPRESS (ISBN 978-989-758-240-0), 117–124, 2017 [BIBTEX]
[Lonati et al., 2016] Lonati V., Malchiodi D., Monga M., Morpurgo A. and Previtali M., A playful tool to introduce lower secondary school pupils to recursive thinking, in Proceedings of 9th International Conference on Informatics in Schools: Situation, Evolution, and Perspectives, ISSEP 2016, 51-52, 2016 [BIBTEX]
[Bellettini et al., 2015a] Bellettini C., Lonati V., Malchiodi D., Monga M., Morpurgo A. and Torelli M., How Challenging are Bebras Tasks? An IRT analysis based on the performance of Italian students, in ITiCSE '15 Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education, New York: ACM (ISBN 9781450334402), 27-32, 2015 [BIBTEX]
[Lonati et al., 2015] Lonati V., Malchiodi D., Monga M. and Morpurgo A., Is coding the way to go?, in A. Brodnik and J. Vahrenhold (Eds.), Informatics in Schools. Curricula, Competences, and Competitions, Springer International Publishing (ISBN 9783319253954), 165-174, 2015 [BIBTEX]
[Paterson et al., 2015] Paterson J., Karhu M., Cazzola W., Illina I., Law R., Malchiodi D., Maximiano M. and Silva C., Experience of an International Collaborative Project with First Year Programming Students, in Proceedings of the IEEE 39th Annual Computer Software and Applications Conference (COMPSAC'15), 829–834, 2015 [BIBTEX]
[Bellettini et al., 2014a] Bellettini C., Lonati V., Malchiodi D., Monga M., Morpurgo A., Torelli M. and Zecca L., Extracurricular Activities for Improving the Perception of Informatics in Secondary Schools, in Y. Gülbahar and E. Karataş (Eds.), Informatics in Schools. Teaching and Learning Perspectives – 7th International Conference on Informatics in Schools: Situation, Evolution, and Perspectives, ISSEP 2014, Istanbul, Turkey, September 22-25, 2014. Proceedings, Vol. 8730, Springer International Publishing, Lecture Notes in Computer Science (ISBN 978-3-319-09958-3), 161–172, 2014 [doi> BIBTEX]
[Bellettini et al., 2014b] Bellettini C., Lonati V., Malchiodi D., Monga M., Morpurgo A. and Torelli M., Teaching Informatics for Fun and Profit, in A. Raschi, A. Di Fabio and L. Sebastiani (Eds.), Proceedings of the International Workshop on Science Education and Guidance in Schools: The Way Forward, Edizioni ETS (ISBN 978-88-903469-2-7), 125–128, 2014 [ BIBTEX]
[Malchiodi and Pedrycz, 2013] Malchiodi D. and Pedrycz W., Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering, in F. Masulli, G. Pasi and R. Yager (Eds.), Fuzzy Logic and Applications. 10th International Workshop, WILF 2013, Genoa, Italy, November 19–22, 2013. Proceedings., Vol. 8256, Springer International Publishing, Switzerland, Lecture Notes on Artificial Intelligence (ISBN 978-3-319-03199-6), 52–59, 2013 [doi> BIBTEX]
[Bellettini et al., 2013] Bellettini C., Lonati V., Malchiodi D., Monga M., Morpurgo A. and Torelli M., What you see is what you have in mind: constructing mental models for formatted text processing, in I. Diethelm, J. Arndt, M. Dünnebier and J. (Eds.), Informatics in Schools: Local Proceedings of the 6th International Conference ISSEP 2013 - Selected Papers, Vol. 6, Universitätsverlag Potsdam, Commentarii informaticae didacticae (ISBN 978-3-86956-222-3), 139-147, 2013 [ BIBTEX]
[Bellettini et al., 2012] Bellettini C., Lonati V., Malchiodi D., Monga M., Morpurgo A. and Torelli M., Exploring the processing of formatted texts by a kynesthetic approach, in WiPSCE'12 Proceedings of the 7th Workshop in Primary and Secondary Computing Education , ACM (ISBN 9781450317870), 143-144, 2012 [ BIBTEX]
[Apolloni et al., 2007c] Apolloni B., Malchiodi D. and Natali L., A Modified SVM Classification Algorithm for Data of Variable Quality, in B. Apolloni, R. Howlett and L. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007. Proceedings, Part III, Berlin Heidelberg: Springer-Verlag, Lecture Notes in Artificial Intelligence 4694 (ISBN 978-3-540-74828-1), 131-139, 2007 [doi> on-line access BIBTEX]
[Apolloni et al., 2007d] Apolloni B., Bassis S. and Malchiodi D., SVM with Random Labels, in B. Apolloni, R. Howlett and L. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007. Proceedings, Part III, Berlin Heidelberg: Springer-Verlag, Lecture Notes in Artificial Intelligence 4694 (ISBN 978-3-540-74828-1), 184-193, 2007 [doi> on-line access BIBTEX]
[Apolloni and Malchiodi, 2006a] Apolloni B. and Malchiodi D., Embedding sample points relevance in SVM linear classification, in V. Torra, Y. Narukawa, A. Valls and J. Domingo-Ferrer (Eds.), MDAI 2006 - Proceedings of 3rd International Conference on Modeling Decisions for Artificial Intelligence, Tarragona: Universitat Rovira I Virgili (ISBN 8400-08416-0), 2006 [ BIBTEX]
[Apolloni et al., 2006e] Apolloni B., Bassis S., Malchiodi D. and Pedrycz W., Interpolating Support Information Granules, in S. Kollias, A. Stafylopatis, W. Duch and E. Oja (Eds.), Artificial Neural Networks - ICANN 2006 - 16th International Conference, Athens, Greece, September 10-14, 2006, Proceedings, Part II, Berlin/Heidelberg: Springer, Lecture Notes in Computer Science 4132 (ISBN 978-3-540-38871-5), 270-281, 2006 [doi> on-line access BIBTEX]
[Malchiodi, 2006] Malchiodi D., Implementing an XML-RPC client in Mathematica, in B. Autin and Y. Papegay (Eds.), eProceedings of the 8th International Mathematica Symposium, Rocquencourt, France: INRIA (ISBN 2-7261-1289-7), 2006 [ BIBTEX]
[Apolloni et al., 2005] Apolloni B., Brega A. and Malchiodi D., BICA: a Boolean Independent Component Analysis Algorithm, in N. Nedjah, L. Mourelle, M. B. R. Vellasco, A. Abraham and M. Köppen (Eds.), Proceedings of HIS 2005: Fifth International Conference on Hybrid Intelligent Systems, IEEE Computer Society (ISBN 0-7695-2457-5), 131-136, 2005 [doi> BIBTEX]
[Apolloni et al., 2005a] Apolloni B., Bassis S., Gaito S. and Malchiodi D., Tight Bounds for SVM Classification Error, in M. Zhao and Z. Shi (Eds.), Proceedings - 2005 International Conference on Neural Network & Brain (ICNN&B'05), IEEE Press (ISBN 0-7803-9422-4), 5-8, 2005 [on-line access BIBTEX]
[Apolloni et al., 2005f] Apolloni B., Bassis S., Gaito S. and Malchiodi D., Appreciation of medical treatments through confidence intervals, in E. Biganzoli, P. Boracchi, P. Duca and E. Ifeachor (Eds.), Proceedings of the 1t European Workshop on the Assessment of Diagnostic Performance, RCE Edizioni (ISBN 88-8399-084-6), 165-174, 2005 [ BIBTEX]
[Apolloni et al., 2004a] Apolloni B., Brega A., Malchiodi D. and Mesiano C., Detecting Driving Awareness, in J. Boulicaut, F. Esposito, F. Giannotti and D. Pedreschi (Eds.), Knowledge Discovery in Databases - PKDD 2004. 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 20-24, 2004. Proceedings, Berlin, Heidelberg: Springer, Lecture Notes in Artificial Intelligence 3202 (ISBN 3-540-23108-0), 528-530, 2004, demonstrating paper [doi> online-access BIBTEX]
[Apolloni et al., 2004b] Apolloni B., Malchiodi D. and Mesiano C., An Attention Monitoring System for High Demanding Operational Tasks, in Proceedings of the 2004 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, IEEE Press (ISBN 0-7803-8381-8), 23-29, 2004, invited paper [doi> BIBTEX]
[Apolloni et al., 2003] Apolloni B., Brega A., Malchiodi D., Palmas G. and Zanaboni A. M., Learning rule representations from boolean data, in O. Kaynak, E. Alpaydin, E. Oja and L. Xu (Eds.), Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings, Springer, Lecture Notes in Computer Science 2714, 875-882, 2003 [doi> on-line access BIBTEX]
[Apolloni et al., 2003a] Apolloni B., Bassis S., Brega A., Gaito S., Malchiodi D. and Zanaboni A. M., A man-machine human interface for a special device of the pervasive computing world, in A. Kameas and N. Streitz (Eds.), Proceedings of DC Tales: Tales of the Disappearing Computer, Santorini Greece, June 1-4, 2003, CTI Press (ISBN 960-406-461-4), 263-267, 2003 [ BIBTEX]
[Apolloni et al., 2003b] Apolloni B., Brega A., Malchiodi D., Valcamonica N. and Zanaboni A. M., A symbolic description of the awareness state in car driving, in A. Kameas and N. Streitz (Eds.), Proceedings of DC Tales: Tales of the Disappearing Computer, Santorini Greece, June 1-4, 2003, CTI Press (ISBN 960-406-461-4), 93-96, 2003 [ BIBTEX]
[Kasderidis et al., 2003] Kasderidis S., Taylor J. G., Tsapatoulis N. and Malchiodi D., Driving Attention to the Dangerous, in O. Kaynak, E. Alpaydin and E. Oja (Eds.), Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings, Springer, Lecture Notes in Computer Science 2714, 909-916, 2003 [on-line access BIBTEX]
[Apolloni and Malchiodi, 2002a] Apolloni B. and Malchiodi D., Narrowing confidence interval witdh of PAC learning risk function by algorithmic inference, in On-line proceedings of the 7th International Symposium on Artificial Intelligence and Mathematics (Fort Lauderdale, USA, January 2-4 2002), 2002 [on-line access BIBTEX]
[Apolloni et al., 2002b] Apolloni B., Malchiodi D., Orovas C. and Zanaboni A. M., Fuzzy Methods for Simplifying a Boolean Formula Inferred from Examples, in L. Wang, S. Halgamuge and X. Yao (Eds.), FSDK'02, Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery: Computational Intelligence for the E-Age, November 18-22, 2002, Orchid Country Club, Singapore, Vol. 2, (ISBN 981-04-7520-9), 554-558, 2002, extended version in [Apolloni et al., 2005] [ BIBTEX]
[Apolloni et al., 2002c] Apolloni B., Bassis S., Malchiodi D. and Gaito S., Cooperative games in a stochastic environment, in E. Damiani, R. Howlett, L. Jain and N. Ichalkaranje (Eds.), Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies - KES 2002 (Proceedings of KES'2002: Sixth Internatinal Conference on Knowledge-Based Intelligent Information & Engineering Systems, Crema, Italy, September 18-19, 2002, Vol. 82, Amsterdam: IOS Press/Ohmsha, Frontiers in Artificial Intelligence and Applications (ISBN 1-58603-280-1), 296-300, 2002 [ BIBTEX]
[Apolloni and Malchiodi, 2001a] Apolloni B. and Malchiodi D., Twisting statistics with properties, in A. Morazevich, V. Levashenko, E. Zaitseva and N. Ichalkaranje (Eds.), Proceedings of ICINASTe 2001: Internatinal Conference on Information, Networks and System Technlogies (Minsk, Belarus, October 2-4, 2001), Minsk: BSEU (ISBN 985-426-692-3), 48-56, 2001 [ BIBTEX]
[Apolloni et al., 2000] Apolloni B., Malchiodi D., Orovas C. and Palmas G., From synapses to rules, in Workshop notes of ECAI 2000: European Conference on Artificial Intelligence - Workshop of connectionist-symbolic integration: representation, paradigm and algorithms (Berlin, Germany, 2000), 2000 [ BIBTEX]

Papers in national conference proceedings

[Frasca and Malchiodi, 2016] Frasca M. and Malchiodi D., Selection of Negative Examples for Node Label Prediction through Fuzzy Clustering Techniques, in S. Bassis, A. Esposito, F. C. Morabito and E. Pasero (Eds.), Advances in Neural Networks: Computational Intelligence for ICT, Springer International Publishing (ISBN 978-3-319-33747-0), 67-76, 2016 [doi> BIBTEX]
[Malchiodi and Legnani, 2014] Malchiodi D. and Legnani T., Avoiding the Cluster Hypothesis in SV Classification of Partially Labeled Data, in S. Bassis, A. Esposito and F. C. Morabito (Eds.), Recent Advances of Neural Networks Models and Applications. Proceedings of the 23nd Workshop of the Italian Neural Networks Society (SIREN), May 23-25, Vietri sul Mare, Salerno, Italy, Vol. 26, Springer, Smart Innovation, Systems and Technologies (ISBN 978-3-319-04128-5), 33-40, 2014 [ BIBTEX]
[Malchiodi, 2013a] Malchiodi D., MUT: un framework di test automatico per Wolfram Mathematica, in Mathematica Italia User Group Meeting 2013 - Atti del Convegno, Adalta (ISBN 978-88-96810-03-3), 2013 [ BIBTEX]
[Malchiodi, 2013b] Malchiodi D., An interpretation of the boundary movement method for imbalanced dataset classification based on data quality, in B. Apolloni, S. Bassis, A. Esposito and F. C. Morabito (Eds.), Neural Nets and Surroundings. 22nd Italian Workshop on Neural Nets, WIRN 2012, May 17-19 2012, Vietri sul Mare, Salerno, Italy, Springer, Smart Innovation, Systems and Technologies 19 (ISBN 978-3-642-35466-3), 21-27, 2013 [doi> BIBTEX]
[Malchiodi, 2011] Malchiodi D., Scrivi anche tu un libro con Mathematica!, in Mathematica Italia User Group Meeting 2011 - Atti del Convegno, Adalta (ISBN 9788896810026), 2011 [ BIBTEX]
[Malchiodi et al., 2010] Malchiodi D., Re M. and Valentini G., Uso di Mathematica per la classificazione di dati di qualità variabile, in Mathematica Italia User Group Meeting - Atti del Convegno 2010, Adalta (ISBN 978-88-96810-00-2), 2010 [ BIBTEX]
[Bulgheroni and Malchiodi, 2009] Bulgheroni M. and Malchiodi D., Mathematica per l'introduzione dei rudimenti della programmazione nelle scuole superiori, in Atti del Mathematica Italia User Group Meeting, Adalta, 2009 [ BIBTEX]
[Malchiodi et al., 2009a] Malchiodi D., Bassis S. and Valerio L., svMathematica: implementazione in Mathematica di algoritmi di machine learning basati su vettori di supporto, in Atti del Mathematica Italia User Group Meeting, Adalta, 2009 [ BIBTEX]
[Malchiodi et al., 2009c] Malchiodi D., Bassis S. and Valerio L., Discovering regression data quality through clustering methods, in B. Apolloni, M. Marinaro and S. Bassis (Eds.), New Directions in Neural Networks, 18th Italian Workshop on Neural Networks: WIRN 2008, 22-24 May 2008, Vietri sul Mare, IOS Press, FAIA-KBIES vol. 193 (ISBN 0922-6389), 76-85, 2009 [ BIBTEX]
[Malchiodi, 2008a] Malchiodi D., The head fake, ovvero insegnando è concesso imbrogliare, in Atti del Mathematica Italia User Group Meeting, Adalta, 2008 [ BIBTEX]
[Apolloni et al., 2005b] Apolloni B., Iannizzi D., Malchiodi D. and Pedrycz W., Granular Regression, in B. Apolloni, M. Marinaro, G. Nicosia and R. Tagliaferri (Eds.), Neural Nets. 16th Italian Workshop on Neural Nets, WIRN 2005 and International Workshop on Natural and Artificial Immune Systems, NAIS 2005. Vietri sul Mare, Italy, June 2005, Springer, Lecture Notes in Computer Science 3931 (ISBN 3-540-33183-2), 2005 [doi> on-line access BIBTEX]
[Apolloni et al., 2005c] Apolloni B., Clivio A., Bassis S., Gaito S. and Malchiodi D., An Evolution Hypothesis of Bacterial Populations, in B. Apolloni, M. Marinaro, G. Nicosia and R. Tagliaferri (Eds.), Neural Nets. 16th Italian Workshop on Neural Nets, WIRN 2005 and International Workshop on Natural and Artificial Immune Systems, NAIS 2005. Vietri sul Mare, Italy, June 2005, Springer, Lecture Notes in Computer Science 3931 (ISBN 3-540-33183-2), 214-230, 2005 [doi> online-access BIBTEX]
[Apolloni et al., 2005d] Apolloni B., Bassis S., Gaito S., Malchiodi D. and Minora A., Computing confidence intervals for the risk ofa SVM classifier through algorithmic inference, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Biological and Artificial Intelligence Environments, Springer, 225-234, 2005 [online-access BIBTEX]
[Apolloni et al., 2005e] Apolloni B., Bassis S., Gaito S., Iannizzi D. and Malchiodi D., Learning continuous functions through a new linear regression method, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Biological and Artificial Intelligence Environments, Springer, 235-243, 2005 [online-access BIBTEX]
[Apolloni et al., 2003c] Apolloni B., Bassis S., Brega A., Gaito S., Malchiodi D., Valcamonica N. and Zanaboni A. M., Monitoring of car drivng awareness from biosignals, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003, Springer, Lecture Notes in Computer Science 2859 (ISBN 3-540-20227-7), 269-277, 2003 [doi> on-line access BIBTEX]
[Apolloni et al., 2003d] Apolloni B., Bassis S., Gaito S. and Malchiodi D., Cooperative games in a stochastic environment, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003, Springer, Lecture Notes in Computer Science 2859 (ISBN 3-540-20227-7), 25-34, 2003 [doi> on-line access BIBTEX]
[Apolloni et al., 2002d] Apolloni B., Malchiodi D., Gaito S. and Zanaboni A. M., Twisting features with properties, in M. Marinaro and R. Tagliaferri (Eds.), Neural Nets WIRN Vietri-01: Proceedings of the 12th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 17-19 May, 2001, Springer, Perspectives in Neural Computing (ISBN 1-85233-505-X), 301-312, 2002 [ BIBTEX]

Book chapters

[Bellettini et al., 2015] Bellettini C., Lonati V., Malchiodi D., Monga M., Morpurgo A. and Pedersini F., La formazione degli insegnanti della classe 42/A – Informatica: l'esperienza dell'Università degli Studi di Milano, in and A. Labella (Ed.), E questo tutti chiamano Informatica, Chapter 4, Sapienza Università Editrice (ISBN 978-88-98533-63-3), 53–76, 2015 [BIBTEX]
[Apolloni et al., 2005g] Apolloni B., Brega A., Malchiodi D., Orovas C. and Zanaboni A. M., A Fuzzy Method for Learning Simple Boolean Formulas from Examples, in S. Halgamuge and L. Wang (Eds.), Computational Intelligence for Modelling and Prediction, Chapter 26, Springer, Studies in Computational Intelligence, Vol. 2 (ISBN 3-540-26071-4), 367-382, 2005, extended version of [Apolloni et al., 2002] [doi> BIBTEX]
[Apolloni et al., 2002e] Apolloni B., Bassis S., Gaito S. and Malchiodi D., Statistical bases for learning, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 1, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 5-40, 2002 [BIBTEX]
[Apolloni et al., 2002f] Apolloni B., Gaito S., Iannizzi D. and Malchiodi D., Learning regression functions, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 3, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 61-73, 2002 [BIBTEX]
[Apolloni et al., 2002g] Apolloni B., Bassis S., Gaito S. and Malchiodi D., Cooperative games in a stochastic environment, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 4, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 75-86, 2002 [BIBTEX]
[Apolloni et al., 2002h] Apolloni B., Malchiodi D., Orovas C. and Zanaboni A. M., Fuzzy methods for simplifying a Boolean formula inferred from examples, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 7, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 117-128, 2002 [BIBTEX]
[Apolloni et al., 2002i] Apolloni B., Gaito S. and Malchiodi D., Learning and checking confidence regions for the hazard function of biomedical data, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 13, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 251-260, 2002 [BIBTEX]

Theses

[Malchiodi, 2000] Malchiodi D., Algorithmic approach to the statistical inference of non-Boolean function classes, Università degli Studi di Milano, 2000, PhD thesis in Computational Mathematics and Operations Research [ BIBTEX]
[Malchiodi, 1996] Malchiodi D., Algoritmi di apprendimento per reti neurali non standard, Università degli Studi di Milano, 1996, MSc thesis in Computer Science (in Italian) [ BIBTEX]

Software

[Malchiodi, 2010a] Malchiodi D., yaplf: yet another python learning framework, python library, 2010 [Home GitHub BIBTEX]
[Malchiodi et al., 2009b] Malchiodi D., Bassis S. and Valerio L., svMathematica: a Mathematica package for SV classification and regression, Wolfram Mathematica library, 2009 [Home GitHub BIBTEX]
[Malchiodi, 2006a] Malchiodi D., The Mathematica neosAPI package, Wolfram Mathematica library, 2006 [Home GitHub BIBTEX]
[Malchiodi, 2006b] Malchiodi D., xmlRpc: remotely executing code within Mathematica, Wolfram Mathematica library, 2006 [Home GitHub BIBTEX]
[Malchiodi, 2006c] Malchiodi D., A Mathematica bae64 package, Wolfram Mathematica library, 2006 [Home GitHub BIBTEX]

Other publications

[Lissoni et al., 2015] Lissoni A., Lonati V., Malchiodi D., Monga M., Morpurgo A., Repetto L. and Torelli M., VII Kangourou dell'informatica 2014-2015, Edizioni Kangourou Italia (ISBN 978-88-89249-41-3), 2015 [BIBTEX]
[Lissoni et al., 2014] Lissoni A., Lonati V., Malchiodi D., Monga M., Morpurgo A., Repetto L. and Torelli M., VI Kangourou dell'Informatica 2013--2014, Edizioni Kangourou Italia (ISBN 9788889249376), 2014 [BIBTEX]
[Lissoni et al., 2013] Lissoni A., Lonati V., Malchiodi D., Monga M., Morpurgo A. and Torelli M., V Kangourou dell'Informatica 2012--2013. Testi, soluzioni e commenti, Edizioni Kangourou Italia (ISBN 978-88-89249-34-5), 2013 [BIBTEX]
[Lissoni et al., 2012] Lissoni A., Lonati V., Malchiodi D., Monga M., Morpurgo A. and Torelli M., Kangourou dell'Informatica 2012, Edizioni Kangourou Italia (ISBN 9788889249307), 2012 [BIBTEX]

Theses supervised as advisor or co-advisor

Organization of editorial and scientific activities

Conference organization

2017
Member of the local organizing committee of 2017 Bebras international workshop
2017
Member of the local organizing committee of 21st Century Strategies to Tackle Early School Leaving
2012
Member of the local organizing committee of IAD 2012 (Italian Agile Day 2012)
2011
Member of the organizing committee of INFOCULT 2011
2011
Member of the program committee of KES2011
2010
Member of the program committee of ECML PKDD 2010 (European Conference on Machine Learning / Principles and Practice of Knowledge Discovery in Databases)
2010
Member of the organizing committee of MIUGM (Mathematica Italia User Group Meeting)
2007
Member of the program committee of WIRN 2007/KES2007
2006
Collaboration in the organization of CISI2006: Conferenza Italiana sui Sistemi Itelligenti, Ancona, 27-29 settembre 2006
2003
Collaboration in the organization of WIRN2003 (XIV Workshop Italiano Reti Neurali)

Organization of tutorials, workshops and special sessions

2013
Speaker in the panel "Computational Intelligence Methods for Big Data Analysis", organized during WILF2013
2007
Chair of the KES2007/WIRN2007 special session Learning from uncertain data
2006
Co-chair of the workshop New paradigms in hybrid learning systems, within the International Conference of Hybrid Systems and Applications
2005
Tutorial Statistical bases of Machine Learning, HIS'05: Fifth International Conference on Hybrid Intelligent Systems
2004
Tutorial Statistical approaches used in Machine Learning, 15th European Conference on Machine Learning and 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2004)
2004
Tutorial Statistical approaches used in Machine Learning, 15th International Conference on Algorithmic Learning Theory (ALT2004)
2004
Tutorial Statistical methods for biomedical data processing, XV Workshop Italiano Reti Neurali (WIRN2004)

Membership to editorial boards of international journals

2010 >
Mathematics and Computers in Simulation
2010 >
Intelligent decision technologies
2008 >
International Journal of Computational Intelligence Studies

Reviews for journals, conferences, and projects

Journals

Conferences

Projects

2014
SIR 2014 (Scientific Independence of young Researchers) – Italian Ministry for education, university and research (reviewer and rapporteur)

Other activities

2009
Design of the Web site of the Italian Association of University Professors of Computer Science, GRIN.
2006
Design of the web site of the Italian Society for Neural Networks

Teaching activities

Current activities

I am currently charged of the following courses (taught in italian) at the Milan University:

15/16 > 17/18
F1X-97: Statistics and data analytics, BSc in Computer science, Università degli Studi di Milano (48 hours up to 2016/17 (jointly with the degrees of Computer science for digital communication and of Computer science and music), 60 hours since 2017/18)
12/13 > 17/18
F94-80: Big scale analytics, MSci in Computer Science, Università degli Studi di Milano
14/15 > 17/18
F94-124: Informatics didactics, MSci in Computer Science, Università degli Studi di Milano

Past activities

Bachelor and Master Courses

15/16
Big data, Ma in Public and corporate communication, Università degli Studi di Milano (in English)
06/07 > 16/17
F94-12: Simulation - theory and techniques, MSci in Computer Science, Università degli Studi di Milano (annual editions until 2008/09 and biennal editions since 2012/13)
11/12 > 15/16
F4Y-72: Computer programming 3, MSc in Mathematics, Università degli Studi di Milano (biennal editions)
10/11 > 14/15
F3X-34: Operating systems, BSc in Computer science for Digital Communication (and BSc in Computer Science and Music), Università degli Studi di Milano
11/12
F3X-36: Computer programming 1, BSc in Computer Science and Music, Università degli Studi di Milano
10/11
F1Y-35: Software design and project management, MSci in Computer Science and Communication, Università degli Studi di Milano
09/10
F2X-54: Computer Programming 1 (Laboratory), BSc in Digital Communication, Università degli Studi di Milano
06/07 > 09/10
F88011: Computer programming 3, MSci in Applied Mathematics, Università degli Studi di Milano
02/03 > 08/09
F47001: Computer Programming Laboratory, Bachelor in Digital Communication, Università degli Studi di Milano
03/04 > 05/06
Computer Science, Bachelor in Speech Pathology, Università degli Studi di Milano
01/02
Computer Science, Bachelor in Speech Pathology, Università degli Studi di Milano

Courses and lectures in PhD programs and graduate schools

16/17
Multidimensional data analysis, PhD in Computer Science, Università degli Studi di Milano
15/16
Big data: analysis and technologies, PhD in Computer Science, Università degli Studi di Milano
14/15
Teaching 1, Master in Computer Science teaching, Università degli Studi di Milano
13/14
Cmputer science teaching, Master in Computer Science teaching, Università degli Studi di Milano
12/13
A4205: Teaching strategies for operating systems and networks laboratories, Master in Computer Science teaching, Università degli Studi di Milano
06/07
Symbolic Processing Laboratory, Postgraduate School for Teachers, Università degli Studi di Milano
02/03 > 05/06
Theoretical bases for learning, Master in Cognitive Science, Université Victor Segalen Bordeaux 2 (course taught in English and funded by the PROG-ERASMUS project)
04/05
Mathematica basics, PhD in Computer Science, Università degli Studi di Milano
01/02
From synapses to rules - discovering symbolic rules from neural processed data, International School on Neural Networks "E. R. Caianiello", 5th course (taught in English)
01/02
From synapses to rules, TMR-EC International School on Computational Learning (taught in English and funded within the IV EC framework programme)

Lectures within university courses

04/05
Exercises for the Probability and Statistics course, Bachelor in Computer Science, Università degli Studi di Milano
00/01 > 03/04
Lectures within the Neural Networks course, Bachelor in Computer Science, Università degli Studi di Milano
00/01 > 03/04
Lectures within the Probability and Statistics course, Bachelor in Computer Science, Università degli Studi di Milano
98/99
Exercises for the Probability and Statistics course, Bachelor in Computer Science, Università degli Studi di Milano Bicocca

Lectures in vocational programs

07/08
Development of computer systems, SIAM
04/05
Science communication, Università degli Studi di Milano
02/03 > 03/04
Intelligent Systems for Symbolic Processing, Università degli Studi di Milano
01/02
From synapses to rules - discovering symbolic rules from neural processed data, International School on Neural Networks "E. R. Caianiello", 5th course (taught in English)
98/99 > 00/01
Visual Basic Programming, CIAM

Other educational activities

03/04 > 04/05
Organization of the vocational course Intelligent Systems for Symbolic Processing, funded by the FSE project, Università degli Studi di Milano
03/04
Participation in the project for teaching enhancement, Università degli Studi di Milano
2002
Organization of the course From Synapses to rules - discovering symbolic rules from neural processed data, International School on Neural Networks "E. R. Caianiello", 5th course

Academic appointments

Evaluation committees

2006 > 2008
Secretary of the committee for the assignment of abroad student specialization grants for the Computer Science Area, Division of Sciences, University of Milan.
2014
Chair of the committee for admission to the Master in Computer Science teaching
2007
Secretary of the committee for the assignment of an assistant professor position in the Computer Science field at the Law division of "Naples Parthenope" University.
2007
Member of the committee for the renewal of a research associate position in the Computer Science field at the Computer Science Department of the Milan University.
2006
Secretary of the committee for the assignment of a research associate position in Computer Science at the Computer Science Department of the Milan University.
2005
Secretary of the committee for the assignment of a research associate position in Computer Science at the Computer Science Department of the Milan University.
2002
Member of the committee for the assignment of a research associate position in Computer Science at the Computer Science Department of the Milan University.
2002
Member of the committee for the assignment of a technical position at the Computer Science Department of Milan University.
2002
Member of the committee for the assignment of a technical position at the Computer Science Department of Milan University.

Other committees and representative units

2013 > now
Member of the workgroup for perspective students and vocational orientation of the Academic Senate of the Milan University.
2013 > now
Coordinator of the commission for perspective students and vocational orientation of the Science and Technology division of the Milan University.
2013 > now
Member of the committee of the Masters in Mathematics, Physics and Computer science Teaching, Milan University.
2012 > now
Delegated by the Director of the Computer science Department of the Milan University to the activities related to cultural promotion and counseling for prospective students.
2012 > now
Member of the executive committee of the Computer Science Department, Milan University.
2010 > 2012
Coordinator of the committee for prospective students in computer science, Science Division, Milan University
2009 > 2012
Member of the executive committee of the Computer Science Department, Milan University
2008 > 2010
Member of the committee for prospective students in computer science, Science Division, Milan University
2006 > 2007
Member of the committee for students orientation in computer science
2002 > 2005
Representative of the assistant professors within the Science Division of the Milan University.

Foreign languages skill

Mother tongue: Italian

Understanding Speaking Writing
Listening Reading Spoken interaction Spoken production
English C1 (proficient) C2 (proficient) C1 (proficient) C1 (proficient) C1 (proficient)
French C1 (proficient) C2 (proficient) C1 (proficient) C1 (proficient) C1 (proficient)
Spanish A2 (basic) B1 (independent) A2 (basic) A2 (basic) A1 (basic)

(according to the Europass language passport)