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 – 0000-0002-7574-697X – Scopus 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 [Marinò et al., 2021] . Their implementation is described in [Marinò et al., 2021] .

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] . The dependency of the problem on the used learning algorithm and on the dimensionality reduction technique employed in order to encode axioms as numerical vectors has been investigated in [Malchiodi et al., 2020] .

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.

ML-based COVID-19 risk prediction

[Casiraghi et al., 2020] and [Esposito et al., 2021] describe the application of machine learning techniques to the problem of predicting the severity of COVID-19 in patients entering EDs.

Application of ML in veterinary and forensics

Some machine learning and statistical data analysis techniques have been adapted in order to deal with problems in the veterinary and forensic fields. In particular, [Galizzi et al., 2021] and [Bagardi et al., 2021] describe the application of statistical methods in order to classify the incidence of cardiovascular factors in the death of dogs undergoing specific therapy, while [Casali et al., 2021] discusses a pilot study on the application of classification algorithms to predict the type of vehicle involved in a pedestrian hit.

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 and [Condorelli and Malchiodi, 2022] describes the joint design of a Master course on Big Data Architectures done with an Industrial partner.

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

Research funds management

2018: Piano sostegno alla ricerca, Università degli Studi di Milano
2017: Osservatorio Milano Duomo
2017: Computer science department, Università degli Studi di Milano
2017: Social Thingum
2017: Piano sostegno alla ricerca, Università degli Studi di Milano
2016: Consorzio Sardegna Ricerche
2016: SMILE project, European Commission
2016: Piano sostegno alla ricerca, Università degli Studi di Milano
2015: Piano sostegno alla ricerca, Università degli Studi di Milano
2010: Centro Orientamento Scuola e Professioni, Università degli Studi di MIlano

Membership to academic associations and research groups

2022 >: Web Algorithmics Laboratory, Computer Science Department, University of Milan
2021 >: CINI Laboratory on Big Data
2020 >: CINI Laboratory on Artificial Intelligence and Intelligent Systems (AIIS)
2020 >: NIH National COVID Cohort Collaborative (N3C)
2020 >: COVID-19 International Research Team
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 Mattia, Malchiodi Dario, Morpurgo Anna and Torelli MauroTuring: la nascita dell'intelligenza artificiale, Corriere della Sera, Grandangolo Scienza, 2017
Malchiodi, 2015: Malchiodi DarioSistemi operativi – esercizi risolti e commentati, (ISBN 978-88-91091-41-3), 2015
Apolloni et al., 2008: Apolloni Bruno, Pedrycz Witold, Bassis Simone and Malchiodi DarioThe Puzzle of Granular Computing, Berlin: Springer, Studies in Computational Intelligence, Vol. 138 (ISBN 978-3-540-79863-7), 2008
Malchiodi, 2007: Malchiodi DarioFare matematica con Mathematica, Milano: Pearson Addison Wesley (ISBN 978-88-7192-365-9), 2007, in italian
Apolloni et al., 2006: Apolloni Bruno, Malchiodi Dario and Gaito SabrinaAlgorithmic 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

Blandino et al., 2024: Blandino Alberto, Malchiodi Dario, Zanaboni Anna M., Casali Michelangelo, Spada Claudio and Di Francesco CarlottaFatal fall from a height: is it possible to apply artificial intelligence techniques for height estimation?, International Journal of Legal Medicine (2024), in press
Malchiodi et al., 2024: Malchiodi Dario, Raimondi Davide, Fumagalli Giacomo, Giancarlo Raffaele and Frasca MarcoThe role of classifiers and data complexity in learned Bloom filters: insights and recommendations, Journal of Big Data 11 - 45 (2024)
Valentini et al., 2023: Valentini Giorgio, Malchiodi Dario, Gliozzo Jessica, Mesiti Marco, Soto-Gomez Mauricio, Cabri Alberto, Reese Justin, Casiraghi Elena and Robinson Peter N.The promises of large language models for protein design and modeling, Frontiers in Bioinformatics 3 (2023), 1304099
Marinò et al., 2023: Marinò Giosuè C., Furia Flavio, Malchiodi Dario and Frasca MarcoEfficient and Compact Representations of Deep Neural Networks via Entropy Coding, IEEE Access 11 (2023), 106103—106125
Ruschioni et al., 2023: Ruschioni Giulia, Malchiodi Dario, Zanaboni Anna M. and Bonizzoni LetiziaSupervised learning algorithms as a tool for archaeology: classification of ceramic samples described by chemical element concentrations, Journal of Archaeological Science: Reports 49 (2023), 103995
Marinò et al., 2023a: Marinò Giosuè C., Petrini Alessandro, Malchiodi Dario and Frasca MarcoDeep neural networks compression: a comparative survey and choice recommendations, Neurocomputing 520 (2023), 152—170
Condorelli and Malchiodi, 2022: Condorelli Andrea and Malchiodi DarioDesigning a Master Course on Architectures for Big Data: A Collaboration Between University and Industry, Informatics in Education 4 (2022), 635—653
Casali et al., 2021: Casali Michelangelo, Malchiodi Dario, Spada Claudio, Zanaboni Anna M., Cotroneo Rosy, Furci Domenico, Sommariva Andrea, Genovese Umberto and Blandino AlbertoA pilot study for investigating the feasibility of supervised machine learning approaches for the classification of pedestrians struck by vehicles, Journal of Forensics and Legal Medicine 84 (2021), 102256
Bagardi et al., 2021: Bagardi Mara, Locatelli Chiara, Zanaboni Anna M., Galizzi Alberto, Malchiodi Dario and Brambilla Paola G.Multiple retrospective analysis of survival and evaluation of cardiac death predictors in a population of dogs affected by degenerative mitral valve disease in ACVIM class C treated with different therapeutic protocols, Polish Journal of Veterinary Sciences 24 - 1 (2021), 109—118
Esposito et al., 2021: Esposito Andrea A., Casiraghi Elena, Chiaraviglio Francesca, Scarabelli Alice, Stellato Elvira, Plensich Guido, Lastella Giulia, Di Meglio Letizia, Fusco Stefano, Avola Emanuele, Jachetti Alessandro, Giannitto Caterina, Malchiodi Dario, Frasca Marco, Beheshti Afshin, Robinson Peter N., Valentini Giorgio, Forzenigo Laura and Carrafiello GianpaoloArtificial Intelligence in Predicting Clinical Outcome in COVID-19 Patients from Clinical, Biochemical, and a Qualitative Chest X-Ray Scoring System, Reports in Medical Imaging 14 (2021), 27—39
Galizzi et al., 2021: Galizzi Alberto, Bagardi Mara, Stranieri Angelica, Zanaboni Anna M., Malchiodi Dario, Borromeo Vitaliano, Brambilla Paola G. and Locatelli ChiaraFactors affecting the urinary aldosterone-to-creatinine ratio in healthy dogs and dogs with naturally occurring myxomatous mitral valve disease, BMC Veterinary Research 17 - 1 (2021), 1—14
Casiraghi et al., 2020: Casiraghi Elena, Malchiodi Dario, Trucco Gabriella, Frasca Marco, Cappelletti Luca, Fontana Tommaso, Esposito Andrea A., Avola Emanuele, Jachetti Alessandro, Reese Justin, Rizzi Alessandro, Robinson Peter N. and Valentini GiorgioExplainable machine learning for early assessment of COVID-19 risk prediction in emergency departments, IEEE Access 8 (2020), 196299—196325
Lodi et al., 2019: Lodi Michael, Malchiodi Dario, Monga Mattia, Morpurgo Anna and Spieler BernadetteConstructionist Attempts at Supporting the Learning of Computer Programming: A Survey, Olympiads in Informatics 13 (2019), 99—121
Boldi et al., 2018: Boldi Paolo, Frasca Marco and Malchiodi DarioEvaluating 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 Adriano, Ludovico Luca A. and Malchiodi DarioFostering 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 Marco and Malchiodi DarioExploiting Negative Sample Selection for Prioritizing Candidate Disease Genes, Genomics and Computational Biology 3 - 3 (2017), e47
Bellettini et al., 2014: Bellettini Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna, Torelli Mauro and Zecca LuisaInformatics 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 Bruno, Malchiodi Dario and Taylor John G.Learning by Gossip: A Principled Information Exchange Model in Social Networks, Cognitive Computation 5 - 3 (2013), 327-339
Apolloni et al., 2010: Apolloni Bruno, Malchiodi Dario and Valerio LorenzoRelevance regression learning with support vector machines, Nonlinear Analysis 73 (2010), 2855-2867
Apolloni et al., 2010a: Apolloni Bruno, Bassis Simone, Gaito Sabrina, Malchiodi Dario and Zoppis ItaloPlaying monotone games to understand learning behaviors, Theoretical Computer Science 411 - 25 (2010), 2384-2405
Apolloni et al., 2009: Apolloni Bruno, Bassis Simone and Malchiodi DarioCompatible worlds, Nonlinear Analysis: Theory, Methods & Applications 71 - 12 (2009), e2883-e2901
Malchiodi, 2009: Malchiodi DarioAn 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 Bruno, Bassis Simone, Malchiodi Dario and Pedrycz WitoldInterpolating Support Information Granules, Neurocomputing 71 (2008), 2433-2445
Apolloni et al., 2008b: Apolloni Bruno, Bassis Simone, Gaito Sabrina and Malchiodi DarioBootstrapping Complex Functions, Nonlinear Analysis: Hybrid Systems 2 - 2 (2008), 648-664
Malchiodi, 2008: Malchiodi DarioEmbedding Sample Points Uncertainty Measures in Learning Algorithms, Nonlinear Analysis: Hybrid Systems 2 - 2 (2008), 635-647
Apolloni et al., 2007: Apolloni Bruno, Bassis Simone, Gaito Sabrina and Malchiodi DarioSolving 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 Bruno, Bassis Simone, Clivio Alberto, Gaito Sabrina and Malchiodi DarioModeling individual's aging within a bacterial population using a pi-calculus paradigm, Natural Computing 6 - 1 (2007), 33-53
Apolloni et al., 2007b: Apolloni Bruno, Bassis Simone, Gaito Sabrina and Malchiodi DarioAppreciation of medical treatments by learning underlying functions with good confidence, Current Pharmaceutical Design 13 - 15 (2007), 1545-1570
Apolloni et al., 2006a: Apolloni Bruno, Brega Andrea, Malchiodi Dario, Palmas Giorgio and Zanaboni Anna MariaLearning Rule Representations From Data, IEEE Transactions on Systems, Man and Cybernetics, Part A 36 - 5 (2006), 1010-1028
Apolloni et al., 2006b: Apolloni Bruno, Bassis Simone, Gaito Sabrina and Malchiodi DarioElementary team strategies in a monotone game, Nonlinear Analysis 64 - 2 (2006), 310-328
Apolloni et al., 2006c: Apolloni Bruno, Bassis Simone, Gaito Sabrina, Malchiodi Dario and Zoppis ItaloControlling the losing probability in a monotone game, Information Sciences 176 - 10 (2006), 1395-1416
Apolloni et al., 2004: Apolloni Bruno, Esposito Anna, Malchiodi Dario, Orovas Christos, Palmas Giorgio and Taylor John G.A General Framework for Learning Rules From Data, IEEE Transactions on Neural Networks 15 - 6 (2004), 1333-1349
Apolloni et al., 2002: Apolloni Bruno, Malchiodi Dario, Orovas Christos and Palmas GiorgioFrom synapses to rules, Cognitive Systems Research 3 (2002), 167-201
Apolloni and Malchiodi, 2001: Apolloni Bruno and Malchiodi DarioGaining degrees of freedom in subsymbolic learning, Theoretical Computer Science 255 (2001), 295-321
Apolloni et al., 1997: Apolloni Bruno, Malchiodi Dario and Taylor John G.Functional bootstrap: a hardware constrained implementation of on-line bootstrap, InterStat October (1997)

Papers in international conference proceedings

Paravisi et al., 2024: Paravisi Mattia, Visconti Andrea and Malchiodi DarioSecurity Analysis of Cryptographic Algorithms: Hints from Machine Learning, in L. Iliadis, I. Maglogiannis, A. Papaleonidas, E. Pimenidis and C. Jayne (Eds.), Engineering Applications of Neural Networks. EANN 2024., Vol. 2141, Cham: Springer, Communications in Computer and Information Science, 569–580, 2024
Frasson and Malchiodi, 2024: Frasson Marco and Malchiodi DarioSupport Vector Based Anomaly Detection in Federated Learning, in L. Iliadis, I. Maglogiannis, A. Papaleonidas, E. Pimenidis and C. Jayne (Eds.), Engineering Applications of Neural Networks. EANN 2024., Vol. 2141, Cham: Springer, Communications in Computer and Information Science, 274–287, 2024
Nicolini et al., 2024: Nicolini Marco, Malchiodi Dario, Cabri Alberto, Cavalleri Emanuele, Mesiti Marco, Paccanaro Alberto, Robinson Peter N., Reese Justin, Casiraghi Elena and Valentini GiorgioFine-Tuning of Conditional Transformers Improves the Generalization of Functionally Characterized Proteins, in BIOSTEC 2024 - 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Proceedings, Vol. 1, SCITEPRESS (ISBN 978-989-758-688-0), 561-568, 2024
Gliozzo et al., 2024: Gliozzo Jessica, Marinò Giosuè, Bonometti Arturo, Frasca Marco and Malchiodi DarioResource-Limited Automated Ki67 Index Estimation in Breast Cancer, in Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications (ICBRA '23), New York, NY, USA: ACM, 165–172, 2024
Malchiodi et al., 2023: Malchiodi Dario, Raimondi Davide, Fumagalli Giacomo, Giancarlo Raffaele and Frasca MarcoA Critical Analysis of Classifier Selection in Learned Bloom Filters: the Essentials, in L. Iliadis, I. Maglogiannis, S. Alonso Castro, C. Jayne and E. Pimenidis (Eds.), Engineering Application of Neural Networks — 24th International Conference — EAAAI/EANN 2023 — León, Spain, June 14—17, 2023 —Proceedings, Springer Nature, Communications in Computer and Information Science 1826, 47—61, 2023
Zanaboni et al., 2022: Zanaboni Anna M., Malchiodi Dario, Bonizzoni Letizia and Ruschioni GiuliaClassification of Pottery Fragments Described by Concentration of Chemical Elements, in P. L. Mazzeo, E. Frontoni, S. Sclaroff and C. Distante (Eds.), Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022., Vol. 13373, Cham: Springer, Lecture Notes in Computer Science (ISBN 978-3-031-13320-6), 141—151, 2022
Fumagalli et al., 2022: Fumagalli Giacomo, Raimondi Davide, Giancarlo Raffaele, Malchiodi Dario and Frasca MarcoOn the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters, in Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods — ICPRAM, SciTePress (ISBN 978-989-758-549-4), 675—682, 2022
Marinò et al., 2021: Marinò Giosuè C., Ghidoli Gregorio, Frasca Marco and Malchiodi DarioReproducing the sparse Huffman Address Map compression for deep neural networks, in B. Kerautret, M. Colom, A. Krähenbühl, Adrien, D. Lopresti, P. Monasse and H. Talbot (Eds.), Reproducible Research in Pattern Recognition, Cham: Springer International Publishing, Lecture Notes in Computer Science 12636, 161—166, 2021
Marinò et al., 2021a: Marinò Giosué C., Ghidoli Gregorio, Frasca Marco and Malchiodi DarioCompression strategies and space-conscious representations for deep neural networks, in 2020 25th International Conference on Pattern Recognition (ICPR), IEEE, 9835—9842, 2021
Malchiodi et al., 2020: Malchiodi Dario, da Costa Pereira Célia and Tettamanzi Andrea G.Classifying Candidate Axioms via Dimensionality Reduction Techniques, in V. Torra, Y. Narukawa, J. Nin and N. Agell (Eds.), Modeling Decisions for Artificial Intelligence. 17th International Conference, MDAI 2020 Sant Cugat, Spain, September 2–4, 2020 Proceedings, Cham, Switzerland: Springer, Lecture Notes in Computer Sciencce 12256, 179—191, 2020
Malchiodi and Zanaboni, 2019: Malchiodi Dario and Zanaboni Anna MariaData-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 DarioSome 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 Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia and Morpurgo AnnaInformatics 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 Dario, da Costa Pereira Célia and Tettamanzi Andrea G.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 Mattia, Lodi Michael, Malchiodi Dario, Morpurgo Anna and Spieler BernadetteLearning 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 Luca, Malchiodi Dario and Zanaboni Anna MariaSimultaneous Learning of Fuzzy Sets, in A. Esposito, M. Faundez-Zanuy, M. Morabito and E. Pasero (Eds.), Neural Approaches to Dynamics of Signal Exchanges, Vol. 151, Singapore: Springer, Smart Innovation, Systems and Technologies, 167-175, 2020
Bellettini et al., 2018a: Bellettini Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia and Morpurgo AnnaInformatica 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 Anna, Monga Mattia, Malchiodi Dario, Macoratti Roberto, Lonati Violetta, Carimati Fabio and Bellettini CarloA 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 Dario and Tettamanzi Andrea G.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 Violetta, Malchiodi Dario, Monga Mattia and Morpurgo AnnaHow 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 Annalisa, Lonati Violetta, Malchiodi Dario, Monga Mattia and Morpurgo AnnaPromoting 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 Luca A., Malchiodi Dario and Zecca LuisaA 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 Marco, Fontaine Jean F., Valentini Giorgio, Mesiti Marco, Notaro Marco, Malchiodi Dario and Andrade-Navarro MiguelDisease-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 Violetta, Malchiodi Dario, Monga Mattia and Morpurgo AnnaLearning 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 Violetta, Malchiodi Dario, Monga Mattia and Morpurgo AnnaBebras 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 Violetta, Malchiodi Dario, Monga Mattia and Morpurgo AnnaNothing 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 Marco, Lipreri Fabio and Malchiodi DarioAnalysis 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 Adriano, Formica Andrea, Ludovico Luca A. and Malchiodi DarioFostering 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 Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna and Previtali MauroA 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 Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna and Torelli MauroHow 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 Violetta, Malchiodi Dario, Monga Mattia and Morpurgo AnnaIs 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 Marco and Malchiodi DarioSelection 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 James, Karhu Markku, Cazzola Walter, Illina Irina, Law Robert, Malchiodi Dario, Maximiano Marisa and Silva CatarinaExperience 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 Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna, Torelli Mauro and Zecca LuisaExtracurricular 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 Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna and Torelli MauroTeaching 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 Dario and Pedrycz WitoldLearning 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 Dario and Legnani TommasoAvoiding 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 Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna and Torelli MauroWhat 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 DarioMUT: 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 Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna and Torelli MauroExploring 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 DarioAn 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 DarioScrivi 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 Dario, Re Matteo and Valentini GiorgioUso 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 Maria and Malchiodi DarioMathematica 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 Dario, Bassis Simone and Valerio LorenzosvMathematica: 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 Dario, Bassis Simone and Valerio LorenzoDiscovering 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 DarioThe head fake, ovvero insegnando è concesso imbrogliare, in Atti del Mathematica Italia User Group Meeting, Adalta, 2008
Apolloni et al., 2007c: Apolloni Bruno, Malchiodi Dario and Natali LucaA 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 Bruno, Bassis Simone and Malchiodi DarioSVM 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 Bruno and Malchiodi DarioEmbedding 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 Bruno, Bassis Simone, Malchiodi Dario and Pedrycz WitoldInterpolating 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 DarioImplementing 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 Bruno, Brega Andrea and Malchiodi DarioBICA: 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 Bruno, Bassis Simone, Gaito Sabrina and Malchiodi DarioTight 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 Bruno, Iannizzi Domenico, Malchiodi Dario and Pedrycz WitoldGranular 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 Bruno, Clivio Alberto, Bassis Simone, Gaito Sabrina and Malchiodi DarioAn 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 Bruno, Bassis Simone, Gaito Sabrina, Malchiodi Dario and Minora AlbertoComputing 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 Bruno, Bassis Simone, Gaito Sabrina, Iannizzi Domenico and Malchiodi DarioLearning 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 Bruno, Bassis Simone, Gaito Sabrina and Malchiodi DarioAppreciation 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 Bruno, Brega Andrea, Malchiodi Dario and Mesiano CristianDetecting 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 Bruno, Malchiodi Dario and Mesiano CristianAn 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 Bruno, Brega Andrea, Malchiodi Dario, Palmas Giorgio and Zanaboni Anna MariaLearning 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 Bruno, Bassis Simone, Brega Andrea, Gaito Sabrina, Malchiodi Dario and Zanaboni Anna MariaA 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 Bruno, Brega Andrea, Malchiodi Dario, Valcamonica Norberto and Zanaboni Anna MariaA 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 Stathis, Taylor John G., Tsapatoulis Nicolas and Malchiodi DarioDriving 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 Bruno, Bassis Simone, Brega Andrea, Gaito Sabrina, Malchiodi Dario, Valcamonica Norberto and Zanaboni Anna MariaMonitoring of car driving 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 Bruno, Bassis Simone, Gaito Sabrina and Malchiodi DarioCooperative 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 Bruno and Malchiodi DarioNarrowing 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 Bruno, Malchiodi Dario, Orovas Christos and Zanaboni Anna MariaFuzzy 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 Bruno, Bassis Simone, Malchiodi Dario and Gaito SabrinaCooperative 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 Bruno, Malchiodi Dario, Gaito Sabrina and Zanaboni Anna MariaTwisting 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 Bruno and Malchiodi DarioTwisting 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 Bruno, Malchiodi Dario, Orovas Christos and Palmas GiorgioFrom 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., 2020: Bellettini Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia and Morpurgo AnnaAlgomotricità: manipolare i fondamenti dell'Informatica, in and E. Nardelli (Ed.), Coding e oltre: l'informatica nella scuola, Chapter , Liscianilibri (ISBN 978-8892810426), 2020
Bellettini et al., 2015: Bellettini Carlo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna and Pedersini FedericoLa 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 Bruno, Brega Andrea, Malchiodi Dario, Orovas Christos and Zanaboni Anna MariaA 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 Bruno, Bassis Simone, Gaito Sabrina and Malchiodi DarioStatistical 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 Bruno, Gaito Sabrina, Iannizzi Domenico and Malchiodi DarioLearning 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 Bruno, Bassis Simone, Gaito Sabrina and Malchiodi DarioCooperative 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 Bruno, Malchiodi Dario, Orovas Christos and Zanaboni Anna MariaFuzzy 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 Bruno, Gaito Sabrina and Malchiodi DarioLearning 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 DarioAlgorithmic 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 DarioAlgoritmi di apprendimento per reti neurali non standard, Università degli Studi di Milano, 1996, MSc thesis in Computer Science (in Italian)

Software

Malchiodi, 2010a: Malchiodi Darioyaplf: yet another python learning framework, python library, 2010
Malchiodi et al., 2009b: Malchiodi Dario, Bassis Simone and Valerio LorenzosvMathematica: a Mathematica package for SV classification and regression, Wolfram Mathematica library, 2009
Malchiodi, 2006a: Malchiodi DarioThe Mathematica neosAPI package, Wolfram Mathematica library, 2006
Malchiodi, 2006b: Malchiodi DarioxmlRpc: remotely executing code within Mathematica, Wolfram Mathematica library, 2006
Malchiodi, 2006c: Malchiodi DarioA Mathematica bae64 package, Wolfram Mathematica library, 2006

Other publications

Lissoni et al., 2015: Lissoni Angelo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna, Repetto Lorenzo and Torelli MauroVII Kangourou dell'informatica 2014-2015, Edizioni Kangourou Italia (ISBN 978-88-89249-41-3), 2015
Lissoni et al., 2014: Lissoni Angelo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna, Repetto Lorenzo and Torelli MauroVI Kangourou dell'Informatica 2013--2014, Edizioni Kangourou Italia (ISBN 9788889249376), 2014
Lissoni et al., 2013: Lissoni Angelo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna and Torelli MauroV Kangourou dell'Informatica 2012--2013. Testi, soluzioni e commenti, Edizioni Kangourou Italia (ISBN 978-88-89249-34-5), 2013
Lissoni et al., 2012: Lissoni Angelo, Lonati Violetta, Malchiodi Dario, Monga Mattia, Morpurgo Anna and Torelli MauroKangourou dell'Informatica 2012, Edizioni Kangourou Italia (ISBN 9788889249307), 2012

Organization of editorial and scientific activities

Conference organization

2017 > 2023: 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
2008: Member of the technical committee of WCCI2008>
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

2023: Chair of the EANN2023 session CLASSIFICATION / SECURITY / ETHOLOGY
2018: Speaker in the round table Zadeh and the Future of Fuzzy Logic, organized during WILF2018
2018: Chair of the IWBBIO2017 session Computational systems for modelling biological processes
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 IDA 2005: Sixth International Symposium on Intelligent Data Analysis
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

CSTST: International Conference on Soft Computing as Transdisciplinary Science and Technology (2008)
DSIR: International Conference on Data Science and Institutional Research ( 2017 , 2018 , 2019 , 2021 , 2022 , 2023 )
FUN: International Conference on Fun with Algorithms (2010)
HIS: International Conference on Hybrid Intelligent Systems (2005)
ICANN/ICONIP: Joint 13th International Concerence on Artificial Neural Networks and 10th International Conference on Information Processing (2003)
ICTAI: IEEE International Conference on Tools with Artificial Intelligence (2007)
IJCNN: IEEE International Joint Conference on Neural Networks ( 2004 , 2003 , 2002)
IPMU: Information Processing and Management of Uncertainty in Knowledge-Based Systems (2022)
ISSEP: International Conference on informatics in Schools. Situation, Evolution and Perspectives and Automation (2019)
IWBBIO: International Work-Conference on Bioinformatics and Biomedical Engineering and Automation (2017)
KES: International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (2013, 2009, 2008, 2007)
NAIS05: International Workshop on Natural and Artificial Immune Systems ( 2005 )
SOFSEM: International Conference on Current Trends in Theory and Practice on Computer Science (2008)
STACS2003: 20th International Symposium on Theoretical Aspects of Computer Science ( 2003 )
The Web Conference (2022)
WCCI: IEEE World Congress on Computational Intelligence ( 2008 )
WCICA: IEEE World Congress on Intelligent Control and Automation (2016)
WIRN: Italian Workshop on Neural Networks (2014, 2013, 2008, 2007, 2005, 2004, 2003, 2002)

Projects

2022: PhD program – Università degli Studi di Palermo (reviewer)
2021: Research funding scheme A 2020/21 – University of Mauritius (reviewer)
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

2020-21 > 2023-24: Algorithms for massive data (DSE), MSc in Data science and economics, Università degli Studi di Milano (20 hours, 3 credits) – in English
2019-20 > 2023-24: F94-156: Algorithms for massive datasets, MSc in Computer science, Università degli Studi di Milano (48 hours, 6 credits) – in English
2015-16 > 2023-24: 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

2023-24: R18-120: Deep learning in bioinformatics, PhD in Computer Science, Università degli Studi di Milano (4 hours) – in English
2023-24: R18-124: Efficacy and efficiency evaluation of machine learning models, PhD in Computer Science, Università degli Studi di Milano (10 hours) – in English
2019-20 > 2021-22: M335: Computer programming for data analysis, DUT in Statistics and business intelligence, Université de la Côte d'Azur (20 hours) – in French
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)
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
2017-18: R18-40: Analysis of multidimensional data, PhD in Computer Science, Università degli Studi di Milano (10 hours) – in English
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: R18-15: Big data analytics and technologies, PhD in Computer Science, Università degli Studi di Milano (6 hours) – in English
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
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)
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

Matteo Rusconi, Estensione e ingegnerizzazione di algoritmi di apprendimento per insiemi fuzzy tramite tecniche basate su vettori di supporto, Laurea magistrale in Informatica, Università degli Studi di Milano, 2023/24 (Advisor)
Andrea Yachaya, Analisi della correlazione tra eventi all'interno di un SOC, Laurea in Informatica, Università degli Studi di Milano, 2022/23 (Advisor)
Antonio Belotti, Algoritmi di classificazione basati su vettori di supporto per la produzione di modelli succinti, Laurea magistrale in Informatica, Università degli Studi di Milano, 2022/23 (Advisor)
Beatrice Gheli, Integrazione di dati clinici e omici per predizioni in ambito medico, Laurea in Informatica, Università degli Studi di Milano, 2022/23 (Co-advisor)
Claudio Garanzini, Tecniche di data engineering e algoritmi di apprendimento in ambito veterinario, Laurea in Informatica, Università degli Studi di Milano, 2022/23 (Advisor)
Davide Raimondi, Studio e progettazione di un'architettura orientata ai servizi per la creazione di una Data Mesh, Laurea magistrale in Informatica, Università degli Studi di Milano, 2022/23 (Advisor)
Elena Valentina Serbu, Pipeline Reengineering and Optimization -- Rethinking ETL using Pyspark, Laurea magistrale in Data Science for Economics, Università degli Studi di Milano, 2022/23 (Advisor)
Elia Covino, Analisi comparativa di Filtri di Bloom appresi, Laurea in Informatica per la Comunicazione Digitale, Università degli Studi di Milano, 2022/23 (Advisor)
Gianmarco Lodi, Development of a Manufacturing-focused Cloud Data platform using Snowflake, Laurea magistrale in Informatica, Università degli Studi di Milano, 2022/23 (Advisor)
Lorenzo Polli, Data-driven techniques as a support for customer needs understanding and in-store product placement: the Iper La Grande I case study, Laurea magistrale in Data Science for Economics, Università degli Studi di Milano, 2022/23 (Advisor)
Loris Bartesaghi, Design and development of a music recommendation system based on Markov Chains, Laurea magistrale in Data Science for Economics, Università degli Studi di Milano, 2022/23 (Co-advisor)
Luca Bertoletti, E-commerce insights: analyzing customer reviews through LDA topic modeling and association rules, Laurea magistrale in Data Science for Economics, Università degli Studi di Milano, 2022/23 (Advisor)
Marco Lassandro, Tecniche di regressione per problemi di medicina forense, Laurea magistrale in Informatica, Università degli Studi di Milano, 2022/23 (Advisor)
Massimo Frasson, Support Vector Anomaly Detection in Federated Learning, Laurea magistrale in Informatica, Università degli Studi di Milano, 2022/23 (Advisor)
Mattia Paravisi, Security analysis of cryptographic algorithms: hints from machine learning, Laurea magistrale in Informatica, Università degli Studi di Milano, 2022/23 (Co-advisor)
Nicola Manca, Evaluating online and batch machine learning models in production, Laurea magistrale in Informatica, Università degli Studi di Milano, 2022/23 (Advisor)
Sara Caiello, Uso di tecniche di machine learning per dati sbilanciati in ambito veterinario, Laurea in Informatica, Università degli Studi di Milano, 2022/23 (Co-advisor)
Stefano De Filippis, The importance of marketing channels: attribution models and forecasts, Laurea magistrale in Data Science for Economics, Università degli Studi di Milano, 2022/23 (Advisor)
William Biondi, Recommendation system for the fashion industry: a semantic approach, Laurea magistrale in Data Science for Economics, Università degli Studi di Milano, 2022/23 (Advisor)
Alesia Sommariva, Online machine learning models for a scalable streaming machine learning platform, Laurea magistrale in Informatica, Università degli Studi di Milano, 2021/22 (Advisor)
Alessandro Beranti, Metodi per la classificazione automatica di pazienti con long-COVID, Laurea magistrale in Informatica, Università degli Studi di Milano, 2021/22 (Co-advisor)
Alessandro Di Gioacchino, Tecniche di one-class classification per la classificazione di reperti archeologici, Laurea in Informatica, Università degli Studi di Milano, 2021/22 (Co-advisor)
Ali Rafiei, Towards computer vision-based natural language processing, Laurea magistrale in Informatica, Università degli Studi di Milano, 2021/22 (Advisor)
Francesca Fazio, Predizione della mortalità in ambito veterinario tramite algoritmi di apprendimento supervisionato, Laurea in Informatica, Università degli Studi di Milano, 2021/22 (Co-advisor)
Gabriele Garavelli, Implementazione e analisi di estensioni partizionate di Filtri di Bloom appresi, Laurea in Informatica, Università degli Studi di Milano, 2021/22 (Advisor)
Giulio Lodi, Software architecture for deployment of machine learning models in distributed and reactive environments, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2021/22 (Advisor)
Ivan Mazzon, Classificazione multitask di etichette in gradi mediante reti di Hopfield, Laurea in Informatica, Università degli Studi di Milano, 2021/22 (Co-advisor)
Jaspreet Kaur, Bringing natural language processing in the legal domain: state of the art and evolution of Q&A, Laurea magistrale in Informatica, Università degli Studi di Milano, 2021/22 (Advisor)
Laura Luperto, Metodi ensable per l'analisi di dati omici, Laurea in Informatica, Università degli Studi di Milano, 2021/22 (Advisor)
Lucia Anna Mellini, Scelta della popolazione iniziale nel problema di compressione di grafi tramite algoritmi genetici, Laurea in Informatica, Università degli Studi di Milano, 2021/22 (Advisor)
Ruben Popper, Image segmentation and fashion item recognition for luxury brands, Laurea magistrale in Data Science and Economics, Università degli Studi di Milano, 2021/22 (Advisor)
Alessia Cecere, Apprendimento di insiemi fuzzy nell'ambito del Web semantico, Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Advisor)
Andrea Marconi, Tecniche di machine learning per la rilevazione di comportamenti anomali in ambito finanziario e anti-riciclaggio, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2020/21 (Advisor)
Carlos Jordanco Menendez Terrones, Parallelizzazione di algoritmi genetici per la compressione di grafi, Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Co-advisor)
Davide Gavio, Towards semantic powered insight engines, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2020/21 (Advisor)
Davide Raimondi, Tecniche supervisionate per l'apprendimento di filtri di Bloom, Laurea in Sicurezza dei sistemi e delle reti informatiche, Università degli Studi di Milano, 2020/21 (Co-advisor)
Eleonora Di Pierro, Induzione di insiemi fuzzy per la predizione della mortalità in ambito veterinario, Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Advisor)
Giacomo Fumagalli, Sulla sinergia tra apprendimento automatico e filtri di Bloom, Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Advisor)
Giacomo Intagliata, Implementazione e confronto di algoritmi di ottimizzazione per l'apprendimento di insiemi fuzzy, Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Advisor)
Ivan Lamperti, Data pipeline monitoring through machine learning, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2020/21 (Advisor)
Laura Elena Ciurca, Data science for cybersecurity: a machine learning approach for detecting brute force attacks, Laurea Magistrale in Data Sciencei and Economics, Università degli Studi di Milano, 2020/21 (Co-advisor)
Luca Fumagalli, Metodi di deep learning per la predizione di siti di missplicing nel genoma umano, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2020/21 (Co-advisor)
Marco Cavallari, Modelli di previsione del rischio di mortalità per pazienti COVID Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Co-advisor)
Marco Zuccolo, Migrazione di un sistema di integrazione dati da IBM DB2 mainframe a Microsoft SQL server in cloud, Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Advisor)
Pietro Scuttari, Tecniche di machine learning per la classificazione di reperti archeologici, Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Co-advisor)
Samuel Cestola, Induzione di regole per la classificazione in ambito oncologico, Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Advisor)
Samuele Beliusse, Conversione di random forest in alberi associativi, Laurea in Informatica, Università degli Studi di Milano, 2020/21 (Co-advisor)
Alessandra Saitta, Applicazione di algoritmi di apprendimento per la riqualificazione delle aree dismesse nelle regioni italiane, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2019/2020 (Advisor)
Alessia Lombarda, Confronto tra approcci basati su clustering e SVM nell'induzione di insiemi fuzzy, Laurea in Informatica, Università degli Studi di Milano, 2019/2020 (Co-advisor)
Andrea Adami, Design and implementation of recommender systems for luxury brands, Laurea Magistrale in Data Science for Economics, Università degli Studi di Milano, 2019/2020 (Advisor)
Andrea Ferretti, Applying online deep learning in a streaming data platform, Laurea magistrale in Informatica, Università degli Studi di Milano, 2019/2020 (Advisor)
Cristian Dall'Ozzo, Compressione di grafi tramite algoritmi genetici, Laurea in Informatica, Università degli Studi di Milano, 2019/2020 (Co-advisor)
Gabriele Cerizza, Induzione di shadowed set tramite tecniche di one-class clustering, Laurea in Informatica, Università degli Studi di Milano, 2019/2020 (Advisor)
Giovanni Laganà, Rilevazione di fake news basata sull'induzione di insiemi fuzzy, Laurea magistrale in Informatica, Università degli Studi di Milano, 2019/2020 (Advisor)
Gregorio Ghidoli, Compressione di reti neurali mediante quantizzazione probabilistica, Laurea in Informatica, Università degli Studi di Milano, 2019/2020 (Co-advisor)
Isabella Cadisco, Confronto tra algoritmi per l'induzione di insiemi fuzzy, Laurea in Informatica per la comunicazione digitale, Università degli Studi di Milano, 2019/2020 (Co-advisor)
Manuel Dileo, Induzione di insiemi fuzzy in ambito medico-legale, Laurea in Informatica, Università degli Studi di Milano, 2019/2020 (Advisor)
Marco Ghezzi, Apprendimento dell'indice PGM tramite reti neurali, Laurea in Informatica, Università degli Studi di Milano, 2019/2020 (Co-advisor)
Mario Petruccelli, Uso di reti neurali per la classificazione di dati in problemi di medicina legale, Laurea in Informatica, Università degli Studi di Milano, 2019/2020 (Advisor)
Mauro Mastrapasqua, Analisi della mobilità nel Comune di Milano su dati di servizi di navigazione, Laurea in Sicurezza dei sistemi e delle reti informatiche, Università degli Studi di Milano, 2019/2020 (Co-advisor)
Rita Folisi, Confronto tra approcci basati su nearest neighbour e reti neurali nell'induzione di insiemi fuzzy, Laurea in Informatica, Università degli Studi di Milano, 2019/2020 (Advisor)
Simone Quadrelli, Forecasting the occupation of parking slots at Malpensa Airport: a comparison of parametric and machine learning approaches, Laurea magistrale in Data science for economics, Università degli Studi di Milano, 2019/2020 (Co-advisor)
Stefano Dell'oca, Studio dell'effetto di metodi di data augmentation per la classificazione di dati medici, Laurea in Informatica per la comunicazione digitale, Università degli Studi di Milano, 2019/2020 (Co-advisor)
Alessio Petralia, Induzione di fuzzy set: una rassegna, Laurea in Informatica per la comunicazione digitale, Università degli Studi di Milano, 2018/2019 (Co-advisor)
Alessandro Beranti, Analisi di dati per problemi di medicina legale, Laurea in Informatica, Università degli Studi di Milano, 2018/19 (Advisor)
Filippo Vajana, Progettazione e implementazione di misure di affidabilità per problemi di deep learning, Laurea magistrale in Informatica, Università degli Studi di Milano, 2018/19 (Advisor)
Giosuè Marinò, Compressione di reti neurali multistrato mediante tecniche di binarizzazione, Laurea in Informatica, Università degli Studi di Milano, 2018/19 (Advisor)
Leonardo Medici, Analisi e perfezionamento di modelli per la valutazione dell'empatia, Laurea magistrale in Informatica, Università degli Studi di Milano, 2018/19 (Advisor)
Marco Riva, Making a Time-Series Database "smart": human and machine communication towards conversational analytics, Laurea in Informatica, Università degli Studi di Milano, 2018/19 (Advisor)
Paolo Galli, Apprendimento e compressione di strutture dati indicizzate, Laurea in Informatica, Università degli Studi di Milano, 2018/19 (Advisor)
Salvatore Caramazza, Analisi di dati tramite induzione di insiemi fuzzy, Laurea in Informatica, Università degli Studi di Milano, 2018/19 (Advisor)
Tommaso Amadori, Riconoscimento di volti tramite insiemi fuzzy, Laurea in Informatica, Università degli Studi di Milano, 2018/19 (Advisor)
Moris Doratiotto, Algoritmi di compressione per reti neurali, Laurea in Informatica, Università degli Studi di Milano, 2017/2018 (Advisor)
Ettore Tancredi Galante, Deep Neural Networks for Learned Indexes Data Structures, Laurea in Informatica, Università degli Studi di Milano, 2017/18 (Co-advisor)
Daniele Tria, Delivering online Machine Learning in real-time predictive systems relying on open source distributed streaming processors, Laurea magistrale in Informatica, Università degli Studi di Milano, 2017/18 (Advisor)
Elvis Nava, SV-based regression techniques for survival analysis: a case study on veterinary data, Laurea in Informatica, Università degli Studi di Milano, 2017/18 (Advisor)
Francesco Cuccio, Progettazione e realizzazione di un webservice restful per la classificazione di brevi testi in real time mediante l'utilizzo di algoritmi di supervised learning, Diploma universitario in Informatica, Università degli Studi di Milano, 2017/18 (Advisor)
Giovanni Milani, Progettazione e sviluppo di un'applicazione Web per la raccolta e l'analisi di dati a fini commerciali tramite tecnologia NFC, Laurea triennale in Informatica, Università degli Studi di Milano, 2017/18 (Advisor)
Giuseppe Crinò, Progettazione di reti neurali robuste rispetto alla tecnica di adversarial examples, Laurea in Informatica, Università degli Studi di Milano, 2017/18 (Advisor)
Luca Cermenati, Apprendimento simultaneo di fuzzy set, Laurea magistrale in Informatica, Università degli Studi di Milano, 2017/18 (Advisor)
Simone Quadrelli, Graph-based semisupervised learning algorithms in bioinformatics, Laurea triennale in Informatica, Università degli Studi di Milano, 2017/18 (Co-advisor)
Ameni Bouaziz, Méthodes d'apprentissage interactif pour la classification des messages courts, École Doctorale Sciences et Technologies de l'Information et de la Communication, Université de Nice - Sophia Antipolis, 2017 (examinateur)
Andrea Cerruti, Interfacciamento di sistemi informativi in ambito real estate, Laurea in Comunicazione Digitale, Università degli Studi di Milano, 2016/17 (Advisor)
Federica Previ, Utilizzo di tecniche di apprendimento automatico per l'analisi di dati in ambito veterinario, Laurea in Informatica, Università degli Studi di Milano, 2016/17 (Co-advisor)
Francesco Frontera, Implementazione di una libreria per la valutazione di modelli di Machine Learning in real-time streaming, Laurea in Informatica musicale, Università degli Studi di Milano, 2016/17 (Advisor)
Leonardo Medici, Utilizzo di support vector machine per l’analisi di dati in ambito veterinario, Laurea in Informatica, Università degli Studi di Milano, 2016/17 (Advisor)
Simone Colombo, Progettazione di un framework multi-linguaggio per la gestione di esercitazioni basato su notebook, Laurea in Comunicazione Digitale, Università degli Studi di Milano, 2016/17 (Advisor)
Andrea Formica, Progettazione e realizzazione di percorsi didattici di informatica musicale per studenti delle scuole secondarie, Laurea in Informatica Musicale, Università degli Studi di Milano, 2015/16 (Advisor)
Cristiano Aitis, Analisi finanziarie anti-riciclaggio basate su algoritmi di apprendimento bayesiani, Laurea Magistrale in Informatica, 2015/16 (Advisor)
Dario Filippini, Progettazione e implementazione di linguaggi di programmazione e metodologie didattiche attive per l'insegnamento della notazione musicale nelle scuole primarie, Laurea in Informatica Musicale, Università degli Studi di Milano, 2015/16 (Co-advisor)
Davide Melchiorre, Progettazione e realizzazione di un framework per la gestione di esercitazioni basato su jupyter, Laurea in Comunicazione Digitale, Università degli Studi di Milano, 2015/16 (Advisor)
Edoardo Trotta, A deep learning framework for rhythmical patterns generation, Laurea in Informatica Musicale, Università degli Studi di Milano, 2015/16 (Advisor)
Enrico Taglietti, Progettazione e sviluppo di software per la fidelizzazione di clienti in ambito bancario, Laurea in Comunicazione digitale, Università degli Studi di Milano, 2015/16 (Advisor)
Fabio Lipreri, Tecniche di analisi esplorativa per la ricerca di pattern in reti biomolecolari, Laurea in Informatica, Università degli Studi di Milano, 2015/16 (Advisor)
Germana Natalia La Rocca, Sviluppo di un percorso didattico sui sistemi concorrenti per la scuola, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2015/16 (Co-advisor)
Luca Favalli, Algoritmi di apprendimento basati su macchine di Boltzmann ristrette, Laurea in Informatica, Università degli Studi di Milano, 2015/16 (Advisor)
Marco Forlivesi, Ottimizzazione blackbox di funzioni computazionalmente onerose, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2015/16 (Co-advisor)
Marco Gibelli, Progettazione e implementazione di software per laboratori di informatica interattivi per studenti delle scuole secondarie, Laurea in Comunicazione Digitale, Università degli Studi di Milano, 2015/16 (Advisor)
Michelangelo Alcini, Analisi statistica di dataset imprecisi finalizzata al confronto tra metodi per l'identificazione di outlier e per l'attribuzione di dati mancanti, Laurea in Comunicazione digitale, Università degli Studi di Milano, 2015/16 (Co-advisor)
Paolo Merola, Analisi di tecniche di classificazione monoclasse applicate a problemi di apprendimento di funzioni di membership fuzzy, Laurea in Matematica, Università degli Studi di Milano, 2015/16 (Advisor)
Simone Scarano, Gestione della coerenza dell'informazione a priori negli algoritmi di label propagation gerarchici, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2015/16 (Advisor)
Valentina Arrigoni, Unconventional numerical representations for accelerated convolutional neural networks, Laurea Magistrale in Matematica, Università degli Studi di Milano, 2015/16 (Advisor)
Somsack Inthasone, Biodiversity Knowledge Extraction Techniques, École Doctorale Sciences et Technologies de l'Information et de la Communication, Université de Nice - Sophia Antipolis, 2015 (rapporteur)
Alessio Spini, Progettazione e sviluppo di un tool per il reverse engineering di un'architettura SW automotive a partire dall'analisi automatica di codice sorgente, Laurea in Informatica Muusicale, Università degli Studi di Milano, 2014/15 (Advisor)
Daniele Grassi, Realizzazione di un motore di ricerca per i quesiti Bebras, Laurea in Comunicazione Digitale, Università degli Studi di Milano, 2014/15 (Advisor)
Fabio Ramaglia, Sviluppo di applicazioni in ambito turistico basate su big data, Laurea in Comunicazione Digitale, Università degli Studi di Milano, 2014/15 (Advisor)
Manuel Previtali, Progettazione e sviluppo di un percorso didattico sulla ricorsione come approccio alla soluzione per il problem solving e di un'architettura software di supporto, Laurea Magistrale in Informatica per la Comunicazione, Università degli Studi di Milano, 2014/15 (Co-advisor)
Roberto Mapelli, Studio e realizzazione di un prototipo funzionale per piattaforma di monitoraggio di giardini/orti casalinghi, Laurea in Informatica Musicale, Università degli Studi di Milano, 2014/15 (Advisor)
Leonardo Andrea Calvi, Analisi dell'attendibilità di siti Web per previsioni meteorologiche, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2013/14 (Advisor)
Maria Haddad, Progettazione di attività in ambito informatico per le scuole secondarie, Laurea in Informatica, Università degli Studi di Milano, 2013/14 (Co-advisor)
Paolo Riccobaldi, Detection and classification of graphical patterns through computational algebraic topology algorithms: an application to musical notation, Laurea in Informatica Musicale, Università degli Studi di Milano, 2013/14 (Advisor)
Simone Robutti, Classificazione di dati con etichette incerte tramite metodi basati su vettori di supporto, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2013/14 (Advisor)
Andrea Venturelli, Progettazione e realizzazione di un ambiente online per la fruizione di gare informatiche rivolte a studenti degli istituti secondari di primo e di secondo grado, Laurea Magistrale in Informatica per la Comunicazione, Università degli Studi di Milano, 2012/13 (Advisor)
Cristiano Aitis, Analisi della qualità di cifre manoscritte utilizzando metodi di clustering, Laurea in Informatica musicale, Università degli Studi di Milano, 2012/13 (Advisor)
Matteo Borriero, Sviluppo di un'applicazione Web per la gestione dei fascicoli personli del «Centro Diagnostico Polispecialistico», Laurea in Informatica, Università degli Studi di Milano, 2012/13 (Co-advisor)
Omar Negri, Aggregazione e visualizzazione interattiva di dati provenienti da reti sociali, Laurea Magistrale in Tecnologie dell'Informazione e della Comunicazione, Università degli Studi di Milano, 2012/13 (Advisor)
Simone Bettini, «Messaggi cifrati»: progettazione di un'attività didattica in ambito informatico per la scuola secondaria secondo un approccio costruttivista, Laurea in Comunicazione digitale, Università degli Studi di Milano, 2012/13 (Co-advisor)
Christian Fraccola, Tirocinio Formativo Attivo, Classe di abilitazione A042 – Informatica, Università degli Studi di Milano, 2011/12 (Advisor)
Tommaso Legnani, Utilizzo delle macchine a vettori di supporto nell'apprendimento semi-supervisionato, Laurea Magistrale in Matematica, Università degli Studi di Milano, 2011/12 (Advisor)
Andrea Galasso, Progettazione di uno strumento software a supporto dell'analisi dei testi strutturati in percorsi didattici per la scuola secondario di primo grado, Laurea in Informatica, Università degli Studi di Milano, 2010/11 (Co-advisor)
Emanuele Galiano, Progettazione di uno strumento software a supporto dell'analisi di immagini in percorsi didattici per la scuola secondaria di primo grado, Laurea in Informatica, Università degli Studi di Milano, 2010/11 (Co-advisor)
Riccardo Mena, Progettazione e sviluppo di software per automazione di test relativi ad apparecchiature avioniche, Laurea in Comunicazione Digitale, Università degli Studi di Milano, 2008/09 (Advisor)
Maria Bulgheroni, Utilizzo di Mathematica come primo approccio alla programmazione, Scuola Interuniversitaria Lombarda di Specializzazione per l'Insegnamento Secondario, 2008 (Advisor)
Giuseppe Lopes, Algoritmi di controllo del traffico di rete mediante utilizzo di tecniche di granular computing, Laurea in Scienze dell'Informazione, Università degli Studi di Milano, 2007/08 (Co-advisor)
Lorenzo Valerio, Progettazione e analisi di algoritmi di regressione per dati di qualità variabile, Laurea Magistrale in Tecnologie dell'informazione e della comunicazione, Università degli Studi di Milano, 2006/07 (Advisor)
Luca Natali, Progettazione e analisi di algoritmi di apprendimento per SVM basati su misure di rilevanza, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2006/07 (Advisor)
Paolo Rotta, Frequenze di pattern in parole generate a caso in linguaggi regolari, Laurea in Informatica, Università degli Studi di Milano, 2006/07 (Co-advisor)
Simone Mattia Tuveri, Sviluppo di sistemi di interrogazione di database tramite tecnologie J2EE, Laurea in Comunicazione Ditigale, Università degli Studi di Milano, 2006/07 (Advisor)
Jean Coravu, A graphical editor for UML diagrams for Java language and a Java code generator for these diagrams, University of Craiova, Romania, 2006 (Co-advisor)
Hannes Perathoner, Development of a framework for the design of hypermedia and web applications based on J2EE, Laurea in Comunicazione Digitale, Università degli Studi di Milano/Universidad Carlos III de Madrid, 2005/06 (Co-advisor)
Antonio Zippo, Implementazione di metodi di inferenza algoritmica in un package di Mathematica, Laurea in Informatica, Università degli Studi di Milano, 2004/05 (Advisor)
Stefano Pilia, Un progetto di e-Democracy e e-Government. Il caso Sardegna, Laurea Magistrale in Informatica, Università degli Studi di Milano, 2004/05 (Advisor)
Alberto Minora, Algoritmi di apprendimento basati su modelli dinamici per il flusso delle informazioni, Laurea in Informatica, Università degli studi di Milano, 2003/04 (Co-advisor)
Marco Testa, Modelli di apprendimento di algoritmi approssimati per problemi di ottimizzazione combinatoria, Laurea in Scienze dell'Informazione, Università degli Studi di Milano, 1997/98 (Co-advisor)

Academic appointments

Evaluation committees

2023: Member of the committee for the assignment of a technical position at the Department of Agricultural and Environmental Sciences - Production, Territory, Agroenergy of the Milan University.
2023: Member of the Committee for the assignment of the AIFOS award «Il punto sulla ricerca in materia di salute e sicurezza in Italia: analisi analitica (data analytics) degli elaborati della biblioteca tesi sicurezza AIFOS».
2020: Member of the admission committee for the competitive PhD position in «Applications of artificial intelligence to study the interaction between genetic and environmental factors underlying human diseases», multi-annual work-programme in genomics and bioinformatics, University of Milano and Ispra Joint Research Center of the European Commission.
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

2021 > 9999: Member of the Erasmus committee of the Computer Science Department, Milan University.
2022: Coordinator of the selection committee for access of foreign students to the master in Computer science of the University of Milan.
2020 > 2021: Member of the selection committee for access of foreign students to the master in Computer science of the University of Milan.
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

	Level	Certification
English	C2 (proficient)
French	C2 (proficient)
Spanish	B1 (independent)	DELE 09/2024