Curriculum vitæ of Dario Malchiodi – full

This CV contains a full list of the activities carried out during my academic career, and you probably don't want to read it. A compact and short version, easier to read, are available.

Personal information

Dario Malchiodi
Dipartimento di Informatica Università degli Studi di Milano
Room 5015 – Via Celoria 18 – 20133 Milano ITALY
Mail: first . last at unimi . it
Web: http://malchiodi.di.unimi.it
phone: +39 02 503 16338 – skype: dariomalchiodi

Social networks:
dariomalchiodi @dariomalchiodi Dario Malchiodi 0000-0002-7574-697XScopus ID: 6507119064 Dario_Malchiodi @dariomalchiodi

PGP key
470D 8811 C4B3 787A (keybase)

Current position

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

Previous positions

2002 > 2011
Assistant professor at the Computer Science Department of the University of Milan.
2001 > 2002
Research assistant at the Computer Science Department of the University of Milan, 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, University of Milan.
1996
MSc (cum laude) in Computer Science, University of Milan.
1994
Specialization in Unix lab Administration, Regione Lombardia.
1994
Specialization in Multimedia programming with Motif and C, Regione Lombardia.

Research activities

Data-driven induction of fuzzy sets

A learning algorithm for fuzzy sets processing data labeled with their membership degrees has been proposed in [Malchiodi and Pedrycz, 2013; Malchiodi, 2019a] . Such algorithm has been applied to axiom mining within semantic Web [Malchiodi and Tettamanzi, 2018] and to negative examples selection in bioinformatics [Frasca and Malchiodi, 2017; Frasca and Malchiodi, 2016] . This approach has been extended in [Cermenati et al., 2020] to the simultaneous induction of several fuzzy sets, and in [Malchiodi and Zanaboni, 2019] to shadowed sets.

Compression of machine learning models

Knowledge induced via machine learning techniques is often encoded and stored in a distributed fashion withen models learnt from data. Thus it might be difficult to give a qualitative interpretation of the obtained results. Moreover, this typically turns out in bandwidth and storage capacity issues when resources are limited. A possible solution to these problems consists in reducing the amount of space necessary in order to store the above mentioned models after they have been trained. Some compression techniques for neural networks obtained via deep learning is currently under investigation within the research project Multicriteria Data Structures and Algorithms: from compressed to learned indexes, and beyond, funded by the Italian Ministry of Education and Research under the PRIN initiative.

Mining of knowledge bases in semantic Web

Searching potential axioms within a set of formulas is a particularly demanding problem from a computational viewpoint. The solution of inducing such axioms starting from formulas labeled via a precomputed fitness measure, obtained through processing of a knowledge base from the semabtic Web field, has been studied using learning algorithms for fuzzy sets [Malchiodi and Tettamanzi, 2018] and kernel-based regression techniques [Malchiodi et al., 2018] .

Negative example 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 membership functions to fuzzy sets, while [Frasca et al., 2017; Boldi et al., 2018] propose 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., 2019] for the problem of gene prioritization.

Application of ML in veterinary and forensics

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. The latter approach has been extended in [Malchiodi and Zanaboni, 2019] to shadowed sets.

Popularization of informatics culture

Concerning tertiary-level teaching, two publications have been produced: a manual for a software for automatic computations and a exercise textbook on operating systems [Malchiodi, 2007; Malchiodi, 2015] . Within a wider audience, [Monga et al., 2017] is centered around Alan Turing, and [Malchiodi, 2019a] describes possible future evolutions of fuzzy-based technologies.

Training of computing teachers

The algomotorial approach has been introduced in [Bellettini et al., 2014] with the aim of teaching computing as the science studying the automatic elaboration of information, in contrast with the trend of tying computing to the working knowledge of specific technological tools [Lonati et al., 2015; Bellettini et al., 2014] . The proposed approach has been evaluated in the realm of teaching habilitation [Bellettini et al., 2015] , with special focus to a constructivist perspective [Bellettini et al., 2018; Bellettini et al., 2018] . Furthermore, the relation within teaching and computational thinking competitions was studied in [Lonati et al., 2017] , evaluating the impact of the presentation of questions on the latter efficacy [Lonati et al., 2017] .

Teaching computer programming

Starting from an analysis of computing education in Italian schools [Bellettini et al., 2014] and a criticism to the common identification of computer programming with the use of a language in order to encode an algorithm [Lonati et al., 2015] , the field of computer programming teaching has been studied from the viewpoint of its introduction via projects and specific tools [Bulgheroni and Malchiodi, 2009; Paterson et al., 2015] , of an interdisciplinary approach with musical subjects [Ludovico et al., 2017; Baraté et al., 2017; Baratè et al., 2017] , also considering advanced aspects of the discipline [Lonati et al., 2016; Lonati et al., 2017] . Finally, [Monga et al., 2018; Lodi et al., 2019] analyses a constructionist approach to computer programming.

Computational thinking challenges

Within the organization of non-competitive challenges on computational thinking at the national level [Lissoni et al., 2012; Lissoni et al., 2013; Lissoni et al., 2014; Lissoni et al., 2015] and the evaluation of their results [Bellettini et al., 2015; Lonati et al., 2017] , an analysis of the possibility to exploit this tools as a resource for learning in primary and secondary schools has been carried out [Lonati et al., 2017; Calcagni et al., 2017; Morpurgo et al., 2018] .

Informal learning of computing

The algomotorial approach introduced in [Bellettini et al., 2014; Bellettini et al., 2014] has been applied to the introduction of core concepts of computing, such as information representation [Bellettini et al., 2012; Bellettini et al., 2013; Baraté et al., 2017] , basics of computer programming [Baratè et al., 2017] , as well as recursive and greedy strategies [Lonati et al., 2016; Lonati et al., 2017; Lonati et al., 2017] .

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 projects

2019 > 2021
Multicriteria Data Structures and Algorithms: from compressed to learned indexes, and beyond (Italian Ministry of education and research, PRIN) – member
2016 > 2018
Fostering a correct view of informatics (University of Milan, PSR) – coordinator
2015 > 2017
SMILE: Slow down, Move your body, Improve your diet, Learn for life, and Enjoy school time (European Commission, Erasmus+) – unit coordinator
2015
Teaching advanced informatics concepts to high school students (University of Milan, PSR) – coordinator
2012 > 2015
SandS: Social AND Smart (European Commission, Erasmus Programme) – member
2012 > 2014
VIOPE: Learning computer programming in virtual environment (European Commission, 6th Framework Programme) – member
2008 > 2013
PASCAL2: Pattern Analysis, Statistical Modelling and Computational Learning (European Commission, 7th Framework Programme) – member
2005 > 2008
PASCAL: Pattern Analysis, Statistical Modelling and Computational Learning (European Commission, 6th Framework Programme) – member
2002 > 2004
Processi stocastici (Italian Ministry of education and research, PRIN) – member
2001 > 2003
ORESTEIA: mOdular hybRid artEfactS wiTh adaptivE funtIonAlity (European Commission, 5th Framework Programme) – member
1998 > 2000
PHYSTA: Principled Hybrid Sistems: Theory and Applications (European Commission, 4th Framework Programme) – member
2000
Metodi statistici e neurali di supporto alle decisioni in ambito finanziario (Inferentia-DNM) – member
2000
Metodi statistico-neurali per lo studio di popolazioni (Università degli Studi di Milano) – member
1999
Processi stocastici con natura spaziale (Italian Ministry of education and research, PRIN) – member

Membership to academic associations

2016 >
Visiting scientist at INRIA/Université de la Côte d'Azur within the WIMMICS project
2019 >
Data science research centre, University of Milano
2002 >
GRIN: Italian Association of Computer Science University Professors
2008 > 2019
ALaDDIn laboratory
2002 > 2013
Italian Society for Neural Networks
1996 > 2011
Neural Networks Laboratory, Computer Science Department, University of Milan

Awards

2018
CSEDU 2018 best poster award (Carlo Bellettini, Fabrizio Carimati, Violetta Lonati, Riccardo Macoratti, Dario Malchiodi, Mattia Monga and Anna Morpurgo, A Platform for the Italian Bebras)
2016
Informatics Europe Best Practices in Education Award (ALaDDIn laboratory)

Publications

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
Malchiodi, 2015
Malchiodi D.Sistemi operativi – esercizi risolti e commentati, (ISBN 978-88-91091-41-3), 2015
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
Malchiodi, 2007
Malchiodi D.Fare matematica con Mathematica, Milano: Pearson Addison Wesley (ISBN 978-88-7192-365-9), 2007, in italian
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

Papers in international journals

Lodi et al., 2019
Lodi M., Malchiodi D., Monga M., Morpurgo A. and Spieler B.Constructionist Attempts at Supporting the Learning of Computer Programming: A Survey, Olympiads in Informatics 13 (2019), 99—121
Boldi et al., 2018
Boldi P., Frasca M. and Malchiodi D.Evaluating the impact of topological protein features on the negative examples selection, BMC Bioinformatics 19 - 14 (2018), 417.115–417.126
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
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
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
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
Apolloni et al., 2010
Apolloni B., Malchiodi D. and Valerio L.Relevance regression learning with support vector machines, Nonlinear Analysis 73 (2010), 2855-2867
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
Apolloni et al., 2009
Apolloni B., Bassis S. and Malchiodi D.Compatible worlds, Nonlinear Analysis: Theory, Methods & Applications 71 - 12 (2009), e2883-e2901
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
Apolloni et al., 2008a
Apolloni B., Bassis S., Malchiodi D. and Pedrycz W.Interpolating Support Information Granules, Neurocomputing 71 (2008), 2433-2445
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
Malchiodi, 2008
Malchiodi D.Embedding Sample Points Uncertainty Measures in Learning Algorithms, Nonlinear Analysis: Hybrid Systems 2 - 2 (2008), 635-647
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
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
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
Apolloni et al., 2006a
Apolloni B., Brega A., Malchiodi D., Palmas G. and Zanaboni A.Learning Rule Representations From Data, IEEE Transactions on Systems, Man and Cybernetics, Part A 36 - 5 (2006), 1010-1028
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
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
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
Apolloni et al., 2002
Apolloni B., Malchiodi D., Orovas C. and Palmas G.From synapses to rules, Cognitive Systems Research 3 (2002), 167-201
Apolloni and Malchiodi, 2001
Apolloni B. and Malchiodi D.Gaining degrees of freedom in subsymbolic learning, Theoretical Computer Science 255 (2001), 295-321
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)

Papers in international conference proceedings

Malchiodi and Zanaboni, 2019
Malchiodi D. and Zanaboni A.Data-Driven Induction of Shadowed Sets Based on Grade of Fuzziness, in R. Fullér, S. Giove and F. Masulli (Eds.), Fuzzy Logic and Applications — 12th International Workshop, WILF 2018 Genoa, Italy, September 6–7, 2018 — Revised Selected Papers, Cham: Springer Nature Switzerland AG, Lecture Notes in Artificial Intelligence 11291 (ISBN 978-3-030-12543-1/978-3-030-12544-8), 17—28, 2019
Malchiodi, 2019a
Malchiodi D.Some Thoughts About Appealing Directions for the Future of Fuzzy Theory and Technologies Along the Path Traced by Lotfi Zadeh, in R. Fullér, S. Giove and F. Masulli (Eds.), Fuzzy Logic and Applications — 12th International Workshop, WILF 2018 Genoa, Italy, September 6–7, 2018 — Revised Selected Papers, Cham: Springer Nature Switzerland AG, Lecture Notes in Artificial Intelligence 11291 (ISBN 978-3-030-12543-1/978-3-030-12544-8), 240—243, 2019
Bellettini et al., 2018
Bellettini C., Lonati V., Malchiodi D., Monga M. and Morpurgo A.Informatics and Computational Thinking: A Teacher Professional Development Proposal Based on Social-Constructivism, in Informatics in Schools. Fundamentals of Computer Science and Software Engineering., Springer, Lecture Notes in Computer Science 11169 (ISBN 9783030027490), 194–205, 2018
Malchiodi et al., 2018
Malchiodi D., da Costa Pereira C. and Tettamanzi A. G. B.Predicting the Possibilistic Score of OWL Axioms through Support Vector Regression, in D. Ciucci, G. Pasi and B. Vantaggi (Eds.), Scalable Uncertainty Management. SUM 2018, Cham: Springer, Lecture Notes in Artificial Intelligence 11142 (ISBN 978-3-030-00460-6/978-3-030-00461-3), 2018
Monga et al., 2018
Monga M., Lodi M., Malchiodi D., Morpurgo A. and Spieler B.Learning to Program in a Constructionist Way, in V. Dagienė and E. Jasutė (Eds.), Constructionism 2018: Computational Thinking and Educational Innovation: conference proceedings, Vilnius University (ISBN 9786099576015), 906–929, 2018
Cermenati et al., 2020
Cermenati L., Malchiodi D. and Zanaboni A.Simultaneous Learning of Fuzzy Sets, in A. Esposito, M. Faundez-Zanuy, M. Morabito and E. Pasero (Eds.), Neural Approaches to Dynamics of Signal Exchanges, Singapore: Springer, Smart Innovation, Systems and Technologies, 167-175, 2020
Bellettini et al., 2018a
Bellettini C., Lonati V., Malchiodi D., Monga M. and Morpurgo A.Informatica e pensiero computazionale: una proposta costruttivista per gli insegnanti, in G. Adorni, M. Cicognani, F. Koceva and G. Mastronardi (Eds.), Didamatica 2018: Didattica Informatica, AICA (ISBN 978889809147-8), 201–210, 2018
Morpurgo et al., 2018
Morpurgo A., Monga M., Malchiodi D., Macoratti R., Lonati V., Carimati F. and Bellettini C.A Platform for the Italian Bebras, in Proceedings of 10th International Conference on Computer Supported Education, SCITEPRESS (ISBN 978-989-758-291-2), 350–357, 2018
Malchiodi and Tettamanzi, 2018
Malchiodi D. and Tettamanzi A. G. B.Predicting the Possibilistic Score of OWL Axioms through Modified Support Vector Clustering, in H. Haddad, R. L. Wainwright and R. Chbeir (Eds.), SAC'18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, ACM (ISBN 9781450351911), 1984–1991, 2018
Lonati et al., 2017
Lonati V., Malchiodi D., Monga M. and Morpurgo A.How presentation affects the difficulty of computational thinking tasks: an IRT analysis, in Proceedings of 17th Koli Calling International Conference on Computing Education Research, ACM (ISBN 9781450353014), 60–69, 2017
Calcagni et al., 2017
Calcagni A., Lonati V., Malchiodi D., Monga M. and Morpurgo A.Promoting Computational Thinking Skills: Would You Use this Bebras Task?, in V. Dagienė and H. Hellas (Eds.), Informatics in Schools: Focus on Learning Programming, Springer, Lecture Notes in Computer Science (ISBN 978-3-319-71482-0), 102–113, 2017
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 MIE 2017 Proceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education, ACM (ISBN 978-1-4503-5557-5), 44–48, 2017
Frasca et al., 2019
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 M. Bartoletti, A. Barla, A. Bracciali, G. W. Klau, L. Peterson, A. Policriti and R. Tagliaferri (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017, Cham: Springer, Lecture Notes in Computer Science 10834 / Lecture Notes in Bioinformatics 10834 (ISBN 978-3-030-14159-2/978-3-030-14160-8), 60—69, 2019
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), 223–231, 2017
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
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 International Conference on Learning and Teaching in Computing and Engineering (LATICE), 2017, IEEE (ISBN 9781538608920), 91–98, 2017
Frasca et al., 2017a
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Malchiodi, 2008a
Malchiodi D.The head fake, ovvero insegnando è concesso imbrogliare, in Atti del Mathematica Italia User Group Meeting, Adalta, 2008
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Apolloni et al., 2003
Apolloni B., Brega A., Malchiodi D., Palmas G. and Zanaboni A.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
Apolloni et al., 2003a
Apolloni B., Bassis S., Brega A., Gaito S., Malchiodi D. and Zanaboni A.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
Apolloni et al., 2003b
Apolloni B., Brega A., Malchiodi D., Valcamonica N. and Zanaboni A.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
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
Apolloni et al., 2003c
Apolloni B., Bassis S., Brega A., Gaito S., Malchiodi D., Valcamonica N. and Zanaboni A.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
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
Apolloni and Malchiodi, 2002a
Apolloni B. and Malchiodi D.Narrowing confidence interval width 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
Apolloni et al., 2002b
Apolloni B., Malchiodi D., Orovas C. and Zanaboni A.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]
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
Apolloni et al., 2002d
Apolloni B., Malchiodi D., Gaito S. and Zanaboni A.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
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
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

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
Apolloni et al., 2005g
Apolloni B., Brega A., Malchiodi D., Orovas C. and Zanaboni A.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]
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
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
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
Apolloni et al., 2002h
Apolloni B., Malchiodi D., Orovas C. and Zanaboni A.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
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

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
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)

Software

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

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
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
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
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

Organization of editorial and scientific activities

Conference organization

2017 > 2020
Member of the program committee of DSIR: International Conference on Data Science and Institutional Research
2017
Member of the local organizing committee of the 2017 Bebras international workshop
2017
Member of the local organizing committee of 21st Century Strategies to Tackle Early School Leaving
2009 > 2015
Member of the scientific committee of the Mathematica Italia User Group Meeting
2012
Member of the local organizing committee of 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 the 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
2003
Collaboration in the organization of WIRN2003 (XIV Workshop Italiano Reti Neurali)

Tutorials, workshops, panels and special sessions

2018
Speaker in the round table Zadeh and the Future of Fuzzy Logic, organized during WILF2018
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, at the International Conference on Hybrid Systems and Applications
2005
Tutorial Statistical bases of Machine Learning at HIS'05: Fifth International Conference on Hybrid Intelligent Systems
2004
Tutorial Statistical approaches used in Machine Learning at the 15th European Conference on Machine Learning and 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
2004
Tutorial Statistical approaches used in Machine Learning at the 15th International Conference on Algorithmic Learning Theory
2004
Tutorial Statistical methods for biomedical data processing at the XV Workshop Italiano Reti Neurali (WIRN2004)

Membership to editorial boards of international journals

2008 >
International Journal of Computational Intelligence Studies
2010 > 2018
Mathematics and Computers in Simulation
2010 > 2014
Intelligent decision technologies

Reviews for journals, conferences, and projects

Journals

Conferences

Projects

2019
Research funding scheme B 2019/20 – University of Mauritius (reviewer)
2019
Bando straordinario per progetti interdipartimentali (SEED) – University of Milan (reviewer)
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 Computer Science University Professors, GRIN.
2006
Design of the Web site of the Italian Society for Neural Networks

Teaching activities

Current activities

2019-20
F94-156: Algorithms for massive datasets, MSc in Computer science, Università degli Studi di Milano (48 hours, 6 credits) – in English
2019-20
R18-68: Architectural patterns for distributed machine learning applications, PhD in Computer Science, Università degli Studi di Milano (4 hours) – in English
2014-15 > 2019-20
F94-124: Computing education, MSc in Computer science, Università degli Studi di Milano (16 hours, 2 credits)
2019-20
M335: Computer programming for data analysis, DUT in Statistics and business intelligence, Université de la Côte d'Azur (20 hours) – in French
2015-16 > 2019-20
F1X-97: Statistics and data analytics, BSc in Computer science, Università degli Studi di Milano (60 hours, 6 credits) – 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

Past activities

Bachelor and Master Courses

2012-13 > 2018-19
F94-80: Big scale analytics, MSc in Computer science, Università degli Studi di Milano (48 hours, 6 credits)
2018-19
M4103C: Data bases II, DUT in Statistics and business intelligence, Université de la Côte d'Azur (38 hours) – in French
2018-19
M4101: Data mining, DUT in Statistics and business intelligence, Université de la Côte d'Azur (18 hours) – in French
2006-07 > 2016-17
F94-12: Simulation, MSc in Computer science, Università degli Studi di Milano (24 hours, 3 credits) – annual editions until 2008/09 and biennal editions since 2012/13
2015-16
B62-59: Big data and digital methods, Ma in Public and corporate communication, Università degli Studi di Milano (40 hours, 3 credits) – in English
2011-12 > 2015-16
F4Y-72: Computer programming 3, MSc in Mathematics, Università degli Studi di Milano (21 hours, 3 credits) – biennal editions
2010-11 > 2014-15
F3X-34: Operating systems, BSc in Computer science and music, Università degli Studi di Milano (48 hours, 6 credits) – from 2011/12 till 2013/14 jointly with the degree in Digital communication, in 2014/15 jointly with the degree in Computer science for digital communication
2011-12
F3X-36: Computer programming 1, BSc in Computer science and music, Università degli Studi di Milano (72 hours, 9 credits)
2010-11
F1Y-35: Software design, MSc in Computer science for communication, Università degli Studi di Milano (48 hours, 6 credits)
2003-04 > 2009-10
F2X-54: Laboratory of computer programming 1, BSc in Computer science and music, Università degli Studi di Milano (48 hours, 3 credits)
2006-07 > 2009-10
F88011: Computing systems 2, MSc in Mathematics, Università degli Studi di Milano (24 hours, 4 credits)
2002-03 > 2005-06
Theoretical bases for learning, MSc in Cognitive sciences, Université Victor Segalen Bordeaux 2 (10 hours) – course held in 2002/03 and in 2005/06
2003-04 > 2004-05
Computer Science, Bachelor in Professional education, Università degli Studi di Milano (40 hours, 3 credits)
2003-04 > 2004-05
Computer Science, Bachelor in Speech and language therapy, Università degli Studi di Milano (30 hours)

Courses and lectures in PhD programs and graduate schools

2018-19
M39-16: Computer programming for bioinformatics and data science, Specialization course in Bioinformatics and functional genomics, Università degli Studi di Milano (12 hours) – in English
2018-19
M39-11: Algorithms and data organization in bioinformatics, Specialization course in Bioinformatics and functional genomics, Università degli Studi di Milano (10 hours) – in English
2018-19
M39-14: Data integration and visualization, Specialization course in Bioinformatics and functional genomics, Università degli Studi di Milano (2 hours) – in English
2017-18
R18-40: Analysis of multidimensional data, PhD in Computer Science, Università degli Studi di Milano (10 hours) – in English
2016-17 > 2017-18
Data science seminars, Master in Computer Science (EIT Digital data science), Université de la Côte d'Azur (6 hours) – in English
2017-18
M40-2: Elements of R and python, Specialization in Data science for economics, business and finance, Università degli Studi di Milano (10 hours)
2017-18
91A-4: Computer science applied to clinical studies, Specialization course in Management of clinical studies in oncology and hematology-oncology, Università degli Studi di Milano (12 hours)
2017-18
M40-10: Parallel and distributed computing, Specialization in Data science for economics, business and finance, Università degli Studi di Milano (20 hours)
2015-16
R18-15: Big data analytics and technologies, PhD in Computer Science, Università degli Studi di Milano (6 hours) – in English
2014-15
A42-4: Computer programming education, Specialization course for Computing education, Università degli Studi di Milano (18 hours)
2013-14
P42-5: Computing education, Specialization course for Computing education, Università degli Studi di Milano (16 hours)
2012-13
A4205: Teaching strategies for operating systems and networks laboratories, Specialization course for Computing education, Università degli Studi di Milano (14 hours)
2006-07
Symbolic processing laboratory, Specialization course for high-school teachers, Università degli Studi di Milano (20 hours)
2004-05
Mathematica basics, PhD in Computer Science, Università degli Studi di Milano (10 hours)
2001-02
From synapses to rules - discovering symbolic rules from neural processed data , International School on Neural Networks "E. R. Caianiello", 6th course (4 hours) – taught in English
2001-02
From synapses to rules - discovering symbolic rules from neural processed data , TMR-EC International School on Computational Learning (4 hours) – taught in English and funded within the IV EC framework programme

Lectures within university courses

2004/05
Exercises for the Probability and Statistics course, BSc in Computer science, Università degli Studi di Milano (20 hours)
2000/01 > 2003/04
Lectures within the Neural Networks course, MSc in Computer science, Università degli Studi di Milano (4 hours)
2000/01 > 2003/04
Lectures within the Probability and Statistics course, BSc in Computer science, Università degli Studi di Milano
1998/99
Exercises for the Probability and Statistics course, BSc in Computer science, Università degli Studi di Milano Bicocca

Lectures in vocational programs

2007/08
Development of computer systems, Società Italiana Arti e Mestieri (44 hours)
2004/05
Science communication, Università degli Studi di Milano (4 hours)
2002/03 > 2003/04
Intelligent Systems for Symbolic Processing, Università degli Studi di Milano (6 hours)
1999/00 > 2000/01
Visual Basic Programming, CIAM (120 hours)

Other educational activities

2003/04 > 2004/05
Organization of the vocational course Intelligent Systems for Symbolic Processing, funded by the FSE project , 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", 6th course

Theses supervised as advisor or co-advisor

Academic appointments

Evaluation committees

2017
Member of the PhD defense jury of the École doctorale des sciences et technologies de l'information et de la communication, Université de la Côte d'Azur.
2015
Member of the PhD defense jury of the École doctorale des sciences et technologies de l'information et de la communication, Université de Nice – Sophia Antipolis.
2014
Chair of the committee for admission to the specialization in Computer Science teaching
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.
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

2020
Member of the selection committee for access to the 2nd-level specialization course in Bioinformatics and functional genomics of the University of Milan.
2017 > 2018
Member of the steering committee of the specialization course in Data science for economics, business and finance of the University of Milan.
2017
Member of the steering committee of the specialization course in Management of clinical studies in oncology and hematology-oncology of the University of Milan.
2013 > 2017
Coordinator of the commission for prospective students and vocational orientation of the Science and Technology division of the Milan University.
2012 > 2017
Deputy Director of the Computer science Department of the Milan University for activities related to cultural promotion and counseling for prospective students.
2012 > 2017
Member of the executive committee of the Computer Science Department, Milan University.
2010 > 2017
Coordinator of the committee for prospective students in computer science, Science Division, Milan University
2013 > 2015
Member of the committee of the Specialization in Mathematics, Physics and Computer science Teaching, Milan University.
2013 > 2014
Member of the workgroup for prospective students and vocational orientation of the Academic Senate of the 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 prospective students 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 C2 (proficient) C2 (proficient) C2 (proficient) C2 (proficient) C2 (proficient)
French C2 (proficient) C2 (proficient) C2 (proficient) C2 (proficient) C2 (proficient)
Spanish A2 (basic) B1 (independent) A2 (basic) A2 (basic) A1 (basic)