Personal information
- Name
- Dario Malchiodi
- Birth
- Milano, 8/12/1970
- Citizenship
- Italian
- Tax code
- MLC DRA 70T08 F205 O
Address and coordinates
Dipartimento di Scienze dell'Informazione
Università degli Studi di Milano
Via Comelico 39/41
20135 Milano ITALY
RoomS238
malchiodi[at]dsi[dot]unimi[dot]it
(against spam bots)
web: http://homes.dsi.unimi.it/~malchiod
office:
+39 02 503 16338
fax:
+39 02 503 16276
e-fax:
+39 02 700 30976
(comes directly in my inbox; please contact me before sending stuff to this number)
pgp public key
(updated 2006/05/04)
Social networks:
Academia.edu, Facebook, Twitter, Personal, I use this, Google profiles, LinkedIn, Naymz
Key words
Machine learning, Data quality in learning, Algorithmic inference, Probability and mathematical statistics, Neural networks.
Current position
Since 2011 I am associate professor at the Computer Science Department of the Milan University.
Previous positions
- 2002 > 2011
- Assistant professor at the Computer Science Department of the Milan University.
- 2001 > 2002
- Research assistant at the Computer Science Department of the Milan University, within the Neural Networks Laboratory.
- 2000 > 2001
- Software architect at Inferentia-DNM, with the job of designing statistical and neural architectures for financial forecasts.
- 1997 > 2000
- Statistical analyst at The Continiuity Company S.r.l., within a R&D project on flexible regression models for financial data.
- 1996 > 1997
- Software developer in a division of Olivetti S.p.A..
Education
- 2000
- PhD in Computational Mathematics and Operations Research, Department of Mathematics, Milan University.
- 1996
- Bachelor (cum laude) in Computer Science, Department of Computer Science, Milan University.
- 1994
- Master in Administration of a Unix lab, Centro di formazione B. Vigorelli, Regione Lombardia.
- 1994
- Master in Multimedia programming with Motif and C, Centro di formazione B. Vigorelli, Regione Lombardia.
Research activities
My research activities focus around the treatment of uncertainty in machine learning problems, with the aim of strenghtening the aspects belonging to the fields of computer science and statistics.
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, 2006a] and kernel-based version [Apolloni et al., 2007c]. A first analysis of the performance for these applications has been undertaken both theoretically [Apolloni et al., 2007d] 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., 2009c; Apolloni et al., 2005b].
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., 2008a; Apolloni et al., 2006e] or on local search techniques [Apolloni et al., 2005b].
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., 2008b], while [Apolloni et al., 2007b] 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., 2005d; Apolloni et al., 2002e; Apolloni et al., 2002d; Apolloni and Malchiodi, 2001a; 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., 2007d; Apolloni et al., 2005a; Apolloni and Malchiodi, 2002a; Apolloni and Malchiodi, 2001a], learning confidence regions for regression lines avoiding the typical assumption requiring a Gaussian drift distribution [Apolloni et al., 2005e; Apolloni et al., 2002f], and learning confidence regions for the risk function of re-occurrence distribution times in particular cancer pathologies [Apolloni et al., 2007b; Apolloni et al., 2005f; Apolloni et al., 2002i].
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], and solutions to optimization [Malchiodi, 2006a] and machine learning problems based on Support Vectors [Malchiodi et al., 2009b; Malchiodi et al., 2009a]. The related code has been used in order to build up the simulations in [Apolloni et al., 2007c; Apolloni and Malchiodi, 2006a]. 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., 2006a; 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., 2004b; Apolloni et al., 2004a; Apolloni et al., 2003c; Apolloni et al., 2003b; Apolloni et al., 2003a] 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, 2006a; Apolloni et al., 2005b] 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., 2006a; Apolloni et al., 2005g; Apolloni et al., 2003; Apolloni et al., 2002h; Apolloni et al., 2002b], 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., 2006c; Apolloni et al., 2003d; Apolloni et al., 2002g; Apolloni et al., 2002c], 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., 2006b]. This model is applied in [Apolloni et al., 2007a; Apolloni et al., 2005c] to the biologic field, while [Apolloni et al., 2010a] 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., 2004b; Apolloni et al., 2004a; Apolloni et al., 2003c; Apolloni et al., 2003b; Apolloni et al., 2003a] 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].
Membership to research groups
- PASCAL 2 network of excellence;
- Italian Society for Neural Networks;
- Italian Association of University Professors of Computer Science (GRIN);
- Neural Networks Laboratory at the Computer Science Department, Milan University.
Participation in research projects
- 2005 > 2008
- Network of excellence PASCAL: Pattern Analysis, Statistical Modeling and Computational Learning, finanziata dall'EC;
- 2002 > 2004
- Project Stochastic processes, funded by the Italian Ministry for University and Research
- 2001 > 2003
- IST-FET research project ORESTEIA (mOdular hybRid artEfactS wTh adaptivE funtIonAlity, funded by the European Union under the fifth framework programme)
- 1998 > 2000
- TMR research project PHYSTA (Principled Hybrid Sistems: Theory and Applications, funded by the European Union under the fourth framework programme)
- 2000
- Project Statistical and Neural Methods supporting decisions in finance, funded under the grant Young Researchers at the Milan UniversityProgetto Metodi statistici e neurali di supporto alle decisioni in ambito finanziario, finanziato dal Inferentia-DNM
- 2000
- Project Statistical and Neural Methods for population dynamics, funded under the grant Young Researchers at the Milan University
- 1099
- Project Spatial stochastic processes, funded by the Italian Ministry for University and Research
Publications
Books
[Apolloni et al., 2008] B. Apolloni, W. Pedrycz, S. Bassis and D. Malchiodi, The Puzzle of Granular Computing, Springer, Studies in Computational Intelligence, Vol. 138 (ISBN 978-3-540-79863-7), 2008 [ publisher BIBTEX ]
[Malchiodi, 2007] D. Malchiodi, Fare matematica con Mathematica, Milano: Pearson Addison Wesley (ISBN 978-88-7192-365-9), 2007, in italian [ book-page publisher BIBTEX ]
[Apolloni et al., 2006] B. Apolloni, D. Malchiodi and S. Gaito, Algorithmic Inference in Machine Learning, 2nd Edition, Magill, Adelaide: Advanced Knowledge International, International Series on Advanced Intelligence, Vol. 5 (ISBN 0-9751004-2-4), 2006 [ publisher BIBTEX ]
Papers in international journals
[Apolloni et al., 2010] B. Apolloni, D. Malchiodi and L. Valerio, Relevance regression learning with support vector machines, Nonlinear Analysis 73 (2010), 2855-2867 [ doi> BIBTEX ]
[Apolloni et al., 2010a] B. Apolloni, S. Bassis, S. Gaito, D. Malchiodi and I. Zoppis, Playing monotone games to understand learning behaviors, Theoretical Computer Science 411 - 25 (2010), 2384-2405 [ doi> BIBTEX ]
[Apolloni et al., 2009] B. Apolloni, S. Bassis and D. Malchiodi, Compatible worlds, Nonlinear Analysis: Theory, Methods & Applications 71 - 12 (2009), e2883-e2901 [ doi> BIBTEX ]
[Malchiodi, 2009] D. Malchiodi, An experimental analysis of the impact of accuracy degradation in SVM classification, International Journal of Computational Intelligence Studies 1 - 2 (2009), 163-190 [ doi> BIBTEX ]
[Apolloni et al., 2008a] B. Apolloni, S. Bassis, D. Malchiodi and W. Pedrycz, Interpolating Support Information Granules, Neurocomputing 71 (2008), 2433-2445 [ doi> BIBTEX ]
[Apolloni et al., 2008b] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Bootstrapping Complex Functions, Nonlinear Analysis: Hybrid Systems 2 - 2 (2008), 648-664 [ doi> BIBTEX ]
[Malchiodi, 2008] D. Malchiodi, Embedding Sample Points Uncertainty Measures in Learning Algorithms,
Nonlinear Analysis: Hybrid Systems 2
- 2
(2008),
635-647
[
doi> 
BIBTEX
]
[Apolloni et al., 2007] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Solving complex regression problems via Algorithmic Inference: a new family of bootstrap algorithms, Far East Journal of Theoretical Statistics 22 - 2 (2007), 141-180 [ BIBTEX ]
[Apolloni et al., 2007a] B. Apolloni, S. Bassis, A. Clivio, S. Gaito
and
D. Malchiodi, Modeling individual's aging within a bacterial population using a pi-calculus paradigm,
Natural Computing 6
- 1
(2007),
33-53
[
doi> BIBTEX
]
[Apolloni et al., 2007b] B. Apolloni, S. Bassis, S. Gaito
and
D. Malchiodi, Appreciation of medical treatments by learning underlying functions with good confidence,
Current Pharmaceutical Design 13
- 15
(2007),
1545-1570
[
BIBTEX
]
[Apolloni et al., 2006a] B. Apolloni, A. Brega, D. Malchiodi, G. Palmas
and
A. M. Zanaboni, Learning Rule Representations From Data,
IEEE Transactions on Systems, Man and Cybernetics, Part A 36
- 5
(2006),
1010-1028
[
doi> BIBTEX
]
[Apolloni et al., 2006b] B. Apolloni, S. Bassis, S. Gaito
and
D. Malchiodi, Elementary team strategies in a monotone game,
Nonlinear Analysis 64
- 2
(2006),
310-328
[
doi> BIBTEX
]
[Apolloni et al., 2006c] B. Apolloni, S. Bassis, S. Gaito, D. Malchiodi
and
I. Zoppis, Controlling the losing probability in a monotone game,
Information Sciences 176
- 10
(2006),
1395-1416
[
doi> BIBTEX
]
[Apolloni et al., 2004] B. Apolloni, A. Esposito, D. Malchiodi, C. Orovas, G. Palmas
and
J. G. Taylor, A General Framework for Learning Rules From Data,
IEEE Transactions on Neural Networks 15
- 6
(2004),
1333-1349
[
doi> BIBTEX
]
[Apolloni et al., 2002] B. Apolloni, D. Malchiodi, C. Orovas
and
G. Palmas, From synapses to rules,
Cognitive Systems Research 3
(2002),
167-201
[
doi> BIBTEX
]
[Apolloni and Malchiodi, 2001] B. Apolloni
and
D. Malchiodi, Gaining degrees of freedom in subsymbolic learning,
Theoretical Computer Science 255
(2001),
295-321
[
doi> BIBTEX
]
[Apolloni et al., 1997] B. Apolloni, D. Malchiodi
and
J. G. Taylor, Functional bootstrap: a hardware constrained implementation of on-line bootstrap,
InterStat October
(1997)
[
on-line access BIBTEX
]
Papers in international conference proceedings
[Apolloni et al., 2007c] B. Apolloni, D. Malchiodi and L. Natali, A Modified SVM Classification Algorithm for Data of Variable Quality, in B. Apolloni, R. Howlett and L. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007. Proceedings, Part III, Berlin Heidelberg: Springer-Verlag, Lecture Notes in Artificial Intelligence 4694 (ISBN 978-3-540-74828-1), 131-139, 2007 [ doi> on-line access BIBTEX ]
[Apolloni et al., 2007d] B. Apolloni, S. Bassis and D. Malchiodi, SVM with Random Labels, in B. Apolloni, R. Howlett and L. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007. Proceedings, Part III, Berlin Heidelberg: Springer-Verlag, Lecture Notes in Artificial Intelligence 4694 (ISBN 978-3-540-74828-1), 184-193, 2007 [ doi> on-line access BIBTEX ]
[Apolloni and Malchiodi, 2006a] B. Apolloni and D. Malchiodi, Embedding sample points relevance in SVM linear classification, in V. Torra, Y. Narukawa, A. Valls and J. Domingo-Ferrer (Eds.), MDAI 2006 - Proceedings of 3rd International Conference on Modeling Decisions for Artificial Intelligence, Tarragona: Universitat Rovira I Virgili (ISBN 8400-08416-0), 2006 [ BIBTEX ]
[Apolloni et al., 2006e] B. Apolloni, S. Bassis, D. Malchiodi and W. Pedrycz, Interpolating Support Information Granules, in S. Kollias, A. Stafylopatis, W. Duch and E. Oja (Eds.), Artificial Neural Networks - ICANN 2006 - 16th International Conference, Athens, Greece, September 10-14, 2006, Proceedings, Part II, Berlin/Heidelberg: Springer, Lecture Notes in Computer Science 4132 (ISBN 978-3-540-38871-5), 270-281, 2006 [ doi> on-line access BIBTEX ]
[Malchiodi, 2006] D. Malchiodi, Implementing an XML-RPC client in Mathematica, in B. Autin and Y. Papegay (Eds.), eProceedings of the 8th International Mathematica Symposium, Rocquencourt, France: INRIA (ISBN 2-7261-1289-7), 2006 [ BIBTEX ]
[Apolloni et al., 2005] B. Apolloni, A. Brega
and
D. Malchiodi, BICA: a Boolean Independent Component Analysis Algorithm, in
N. Nedjah, L. Mourelle, M. B. R. Vellasco, A. Abraham
and
M. Köppen
(Eds.),
Proceedings of HIS 2005: Fifth International Conference on Hybrid Intelligent Systems,
IEEE Computer Society (ISBN 0-7695-2457-5),
131-136,
2005
[
doi> BIBTEX
]
[Apolloni et al., 2005a] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Tight Bounds for SVM Classification Error, in M. Zhao and Z. Shi (Eds.), Proceedings - 2005 International Conference on Neural Network & Brain (ICNN&B'05), IEEE Press (ISBN 0-7803-9422-4), 5-8, 2005 [ on-line access BIBTEX ]
[Apolloni et al., 2005f] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Appreciation of medical treatments through confidence intervals, in E. Biganzoli, P. Boracchi, P. Duca and E. Ifeachor (Eds.), Proceedings of the 1t European Workshop on the Assessment of Diagnostic Performance, RCE Edizioni (ISBN 88-8399-084-6), 165-174, 2005 [ BIBTEX ]
[Apolloni et al., 2004a] B. Apolloni, A. Brega, D. Malchiodi and C. Mesiano, Detecting Driving Awareness, in J. Boulicaut, F. Esposito, F. Giannotti and D. Pedreschi (Eds.), Knowledge Discovery in Databases - PKDD 2004. 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 20-24, 2004. Proceedings, Berlin, Heidelberg: Springer, Lecture Notes in Artificial Intelligence 3202 (ISBN 3-540-23108-0), 528-530, 2004, demonstrating paper [ doi> online-access BIBTEX ]
[Apolloni et al., 2004b] B. Apolloni, D. Malchiodi and C. Mesiano, An Attention Monitoring System for High Demanding Operational Tasks, in Proceedings of the 2004 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, IEEE Press (ISBN 0-7803-8381-8), 23-29, 2004, invited paper [ doi> BIBTEX ]
[Apolloni et al., 2003] B. Apolloni, A. Brega, D. Malchiodi, G. Palmas and A. M. Zanaboni, Learning rule representations from boolean data, in O. Kaynak, E. Alpaydin, E. Oja and L. Xu (Eds.), Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings, Springer, Lecture Notes in Computer Science 2714, 875-882, 2003 [ doi> on-line access BIBTEX ]
[Apolloni et al., 2003a] B. Apolloni, S. Bassis, A. Brega, S. Gaito, D. Malchiodi and A. M. Zanaboni, A man-machine human interface for a special device of the pervasive computing world, in A. Kameas and N. Streitz (Eds.), Proceedings of DC Tales: Tales of the Disappearing Computer, Santorini Greece, June 1-4, 2003, CTI Press (ISBN 960-406-461-4), 263-267, 2003 [ BIBTEX ]
[Apolloni et al., 2003b] B. Apolloni, A. Brega, D. Malchiodi, N. Valcamonica and A. M. Zanaboni, A symbolic description of the awareness state in car driving, in A. Kameas and N. Streitz (Eds.), Proceedings of DC Tales: Tales of the Disappearing Computer, Santorini Greece, June 1-4, 2003, CTI Press (ISBN 960-406-461-4), 93-96, 2003 [ BIBTEX ]
[Kasderidis et al., 2003] S. Kasderidis, J. G. Taylor, N. Tsapatoulis and D. Malchiodi, Driving Attention to the Dangerous, in O. Kaynak, E. Alpaydin and E. Oja (Eds.), Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings, Springer, Lecture Notes in Computer Science 2714, 909-916, 2003 [ on-line access BIBTEX ]
[Apolloni and Malchiodi, 2002a] B. Apolloni and D. Malchiodi, Narrowing confidence interval witdh of PAC learning risk function by algorithmic inference, in On-line proceedings of the 7th International Symposium on Artificial Intelligence and Mathematics (Fort Lauderdale, USA, January 2-4 2002), 2002 [ on-line access BIBTEX ]
[Apolloni et al., 2002b] B. Apolloni, D. Malchiodi, C. Orovas and A. M. Zanaboni, Fuzzy Methods for Simplifying a Boolean Formula Inferred from Examples, in L. Wang, S. Halgamuge and X. Yao (Eds.), FSDK'02, Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery: Computational Intelligence for the E-Age, November 18-22, 2002, Orchid Country Club, Singapore, Vol. 2, (ISBN 981-04-7520-9), 554-558, 2002, extended version in [Apolloni et al., 2005g] [ BIBTEX ]
[Apolloni et al., 2002c] B. Apolloni, S. Bassis, D. Malchiodi and S. Gaito, Cooperative games in a stochastic environment, in E. Damiani, R. Howlett, L. Jain and N. Ichalkaranje (Eds.), Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies - KES 2002 (Proceedings of KES'2002: Sixth Internatinal Conference on Knowledge-Based Intelligent Information & Engineering Systems, Crema, Italy, September 18-19, 2002, , Vol. 82, Amsterdam: IOS Press/Ohmsha, Frontiers in Artificial Intelligence and Applications (ISBN 1-58603-280-1), 296-300, 2002 [ BIBTEX ]
[Apolloni and Malchiodi, 2001a] B. Apolloni and D. Malchiodi, Twisting statistics with properties, in A. Morazevich, V. Levashenko, E. Zaitseva and N. Ichalkaranje (Eds.), Proceedings of ICINASTe 2001: Internatinal Conference on Information, Networks and System Technlogies (Minsk, Belarus, October 2-4, 2001), Minsk: BSEU (ISBN 985-426-692-3), 48-56, 2001 [ BIBTEX ]
[Apolloni et al., 2000] B. Apolloni, D. Malchiodi, C. Orovas and G. Palmas, From synapses to rules, in Workshop notes of ECAI 2000: European Conference on Artificial Intelligence - Workshop of connectionist-symbolic integration: representation, paradigm and algorithms (Berlin, Germany, 2000), 2000 [ BIBTEX ]
Papers in national conference proceedings
[Malchiodi et al., 2010] D. Malchiodi, M. Re and G. Valentini, Uso di Mathematica per la classificazione di dati di qualità variabile, in Mathematica Italia User Group Meeting - Atti del Convegno 2010, Adalta (ISBN 978-88-96810-00-2), 2010 [ BIBTEX ]
[Bulgheroni and Malchiodi, 2009] M. Bulgheroni and D. Malchiodi, Mathematica per l'introduzione dei rudimenti della programmazione nelle scuole superiori, in Atti del Mathematica Italia User Group Meeting, Adalta, 2009 [ BIBTEX ]
[Malchiodi et al., 2009a] D. Malchiodi, S. Bassis and L. Valerio, svMathematica: implementazione in Mathematica di algoritmi di machine learning basati su vettori di supporto, in Atti del Mathematica Italia User Group Meeting, Adalta, 2009 [ BIBTEX ]
[Malchiodi et al., 2009c] D. Malchiodi, S. Bassis and L. Valerio, Discovering regression data quality through clustering methods, in B. Apolloni, M. Marinaro and S. Bassis (Eds.), New Directions in Neural Networks, 18th Italian Workshop on Neural Networks: WIRN 2008, 22-24 May 2008, Vietri sul Mare, IOS Press, FAIA-KBIES vol. 193 (ISBN 0922-6389), 76-85, 2009 [ BIBTEX ]
[Malchiodi, 2008a] D. Malchiodi, The head fake, ovvero insegnando è concesso imbrogliare, in Atti del Mathematica Italia User Group Meeting, Adalta, 2008 [ BIBTEX ]
[Apolloni et al., 2005b] B. Apolloni, D. Iannizzi, D. Malchiodi and W. Pedrycz, Granular Regression, in B. Apolloni, M. Marinaro, G. Nicosia and R. Tagliaferri (Eds.), Neural Nets. 16th Italian Workshop on Neural Nets, WIRN 2005 and International Workshop on Natural and Artificial Immune Systems, NAIS 2005. Vietri sul Mare, Italy, June 2005, Springer, Lecture Notes in Computer Science 3931 (ISBN 3-540-33183-2), 2005 [ doi> on-line access BIBTEX ]
[Apolloni et al., 2005c] B. Apolloni, A. Clivio, S. Bassis, S. Gaito and D. Malchiodi, An Evolution Hypothesis of Bacterial Populations, in B. Apolloni, M. Marinaro, G. Nicosia and R. Tagliaferri (Eds.), Neural Nets. 16th Italian Workshop on Neural Nets, WIRN 2005 and International Workshop on Natural and Artificial Immune Systems, NAIS 2005. Vietri sul Mare, Italy, June 2005, Springer, Lecture Notes in Computer Science 3931 (ISBN 3-540-33183-2), 214-230, 2005 [ doi> online-access BIBTEX ]
[Apolloni et al., 2005d] B. Apolloni, S. Bassis, S. Gaito, D. Malchiodi and A. Minora, Computing confidence intervals for the risk ofa SVM classifier through algorithmic inference, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Biological and Artificial Intelligence Environments, Springer, 225-234, 2005 [ online-access BIBTEX ]
[Apolloni et al., 2005e] B. Apolloni, S. Bassis, S. Gaito, D. Iannizzi and D. Malchiodi, Learning continuous functions through a new linear regression method, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Biological and Artificial Intelligence Environments, Springer, 235-243, 2005 [ online-access BIBTEX ]
[Apolloni et al., 2003c] B. Apolloni, S. Bassis, A. Brega, S. Gaito, D. Malchiodi, . and A. M. Zanaboni, Monitoring of car drivng awareness from biosignals, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003, Springer, Lecture Notes in Computer Science 2859 (ISBN 3-540-20227-7), 269-277, 2003 [ doi> on-line access BIBTEX ]
[Apolloni et al., 2003d] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Cooperative games in a stochastic environment, in B. Apolloni, M. Marinaro and R. Tagliaferri (Eds.), Neural Nets: 14th Italian Workshop on Neural Nets, WIRN VIETRI 2003, Vietri sul Mare, Italy, June 4-7, 2003, Springer, Lecture Notes in Computer Science 2859 (ISBN 3-540-20227-7), 25-34, 2003 [ doi> on-line access BIBTEX ]
[Apolloni et al., 2002d] B. Apolloni, D. Malchiodi, S. Gaito and A. M. Zanaboni, Twisting features with properties, in M. Marinaro and R. Tagliaferri (Eds.), Neural Nets WIRN Vietri-01: Proceedings of the 12th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 17-19 May, 2001, Springer, Perspectives in Neural Computing (ISBN 1-85233-505-X), 301-312, 2002 [ BIBTEX ]
Book chapters
[Apolloni et al., 2005g] B. Apolloni, A. Brega, D. Malchiodi, C. Orovas and A. M. Zanaboni, A Fuzzy Method for Learning Simple Boolean Formulas from Examples, in S. Halgamuge and L. Wang (Eds.), Computational Intelligence for Modelling and Prediction, Chapter 26, Springer, Studies in Computational Intelligence, Vol. 2 (ISBN 3-540-26071-4), 367-382, 2005, extended version of [Apolloni et al., 2002b] [ doi> BIBTEX ]
[Apolloni et al., 2002e] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Statistical bases for learning, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 1, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 5-40, 2002 [ BIBTEX ]
[Apolloni et al., 2002f] B. Apolloni, S. Gaito, D. Iannizzi and D. Malchiodi, Learning regression functions, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 3, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 61-73, 2002 [ BIBTEX ]
[Apolloni et al., 2002g] B. Apolloni, S. Bassis, S. Gaito and D. Malchiodi, Cooperative games in a stochastic environment, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 4, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 75-86, 2002 [ BIBTEX ]
[Apolloni et al., 2002h] B. Apolloni, D. Malchiodi, C. Orovas and A. M. Zanaboni, Fuzzy methods for simplifying a Boolean formula inferred from examples, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 7, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 117-128, 2002 [ BIBTEX ]
[Apolloni et al., 2002i] B. Apolloni, S. Gaito and D. Malchiodi, Learning and checking confidence regions for the hazard function of biomedical data, in B. Apolloni and F. Kurfess (Eds.), From synapses to rules. Discovering symbolic rules from neural processed data, Chapter 13, New York: Kluwer Academic/Plenum Publishers (ISBN 0-306-47402), 251-260, 2002 [ BIBTEX ]
Theses
[Malchiodi, 2000] D. Malchiodi, Algorithmic approach to the statistical inference of non-Boolean function classes, Università degli Studi di Milano, 2000, PhD thesis in Computational Mathematics and Operations Research [ BIBTEX ]
[Malchiodi, 1996] D. Malchiodi, Algoritmi di apprendimento per reti neurali non standard, Università degli Studi di Milano, 1996, MSc thesis in Computer Science (in Italian) [ BIBTEX ]
Software
[Malchiodi, 2010a] D. Malchiodi, yaplf: yet another python learning framework, python library, 2010 [ BIBTEX ]
[Malchiodi et al., 2009b] D. Malchiodi, S. Bassis and L. Valerio, svMathematica: a Mathematica package for SV classification and regression, Wolfram Mathematica library, 2009 [ BIBTEX ]
[Malchiodi, 2006a] D. Malchiodi, The Mathematica neosAPI package, Wolfram Mathematica library, 2006 [ BIBTEX ]
[Malchiodi, 2006b] D. Malchiodi, xmlRpc: remotely executing code within Mathematica, Wolfram Mathematica library, 2006 [ BIBTEX ]
[Malchiodi, 2006c] D. Malchiodi, A Mathematica bae64 package, Wolfram Mathematica library, 2006 [ BIBTEX ]
Theses supervised as advisor or co-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, 2011 (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, 2008 (advisor)
- Maria Bulgheroni, Utilizzo di Mathematica come primo approccio alla programmazione, Scuola Interuniversitaria Lombarda di Specializzazione per l'Insegnamento Secondario, 2008 (advisor)
- Paolo Rotta, Frequenze di pattern in parole generate a caso in linguaggi regolari, Laurea in Informatica, Università degli Studi di Milano, 2008 (co-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 (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, 2006 (co-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)
- Antonio Zippo, Implementazione di metodi di inferenza algoritmica in un package di Mathematica, Laurea in Informatica, Università degli Studi di Milano, 2005 (co-advisor)
- Alberto Minora, Algoritmi di apprendimento basati su modelli dinamici per il flusso delle informazioni, Laurea in Informatica, Università degli studi di Milano, 2004 (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, 1998 (co-advisor)
Organization of editorial and scientific activities
Conference organization
- 2009 > 2011
- Member of the scientific committee of MIUGM (Mathematica Italia User Group Meeting)
- 2011
- Member of the program committee of KES2011
- 2010
- Member of the program committee of ECML PKDD 2010 (European Conference on Machine Learning / Principles and Practice of Knowledge Discovery in Databases)
- 2010
- Member of the organizing committee of MIUGM (Mathematica Italia User Group Meeting)
- 2007
- Member of the program committee of WIRN 2007 /KES2007
- 2006
- Collaboration in the organization of CISI2006: Conferenza Italiana sui Sistemi Itelligenti, Ancona, 27-29 settembre 2006
- 2003
- Collaboration in the organization of WIRN2003 (XIV Workshop Italiano Reti Neurali)
Organization of tutorials, workshops and special sessions
- 2007
- Chair of the KES2007/WIRN2007 special session Learning from uncertain data
- 2007
- Chair de la session spéciale Learning from uncertain data, dans KES2007/WIRN2007
- 2006
- Co-chair of the workshop New paradigms in hybrid learning systems, within the International Conference of Hybrid Systems and Applications
- 2005
- Tutorial Statistical bases of Machine Learning, HIS'05: Fifth International Conference on Hybrid Intelligent Systems
- 2004
- Tutorial Statistical approaches used in Machine Learning, 15th European Conference on Machine Learning and 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2004)
- 2004
- Tutorial Statistical approaches used in Machine Learning, 15th International Conference on Algorithmic Learning Theory (ALT2004)
- 2004
- Tutorial Statistical methods for biomedical data processing, XV Workshop Italiano Reti Neurali (WIRN2004)
Membership to editorial boards of international journals
- 2010 > now
- Mathematics and Computers in Simulation
- 2010 > now
- Intelligent decision technologies
- 2008 > now
- International Journal of Computational Intelligence Studies
Reviews for journal and conferences
Journals
- Computers and Operations Research
- IEEE Transactions on Fuzzy Systems
- IEEE Transactions on Neural Networks
- Journal of Fuzzy Optimization and Decision Making
- Mathematics and Computers in Simulation
- Neural Networks
- Neurocomputing
Conferences
- FUN: International Conference on Fun with Algorithms (2010)
- KES: International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (2009, 2008, 2007)
- WCCI: IEEE World Congress on Computational Intelligence (2008)
- SOFSEM: International Conference on Current Trends in Theory and Practice on Computer Science (2008)
- WIRN: Italian Workshop on Neural Networks (2008, 2007, 2005, 2004, 2003, 2002)
- ICTAI: IEEE International Conference on Tools with Artificial Intelligence (2007)
- HIS: International Conference on Hybrid Intelligent Systems (2005)
- IJCNN: IEEE International Joint Conference on Neural Networks (2004, 2003, 2002)
- ICANN/ICONIP: Joint 13th International Concerence on Artificial Neural Networks and 10th International Conference on Information Processing (2003)
- STACS2003: 20th International Symposium on Theoretical Aspects of Computer Science (2003)
Other activities
- 2009
- Design of the web site of the Italian Association of University Professors of Computer Science (GRIN)
- 2006
- Design of the web site of the Italian Society for Neural Networks
Teaching activities
Current activities
I am currently charged of the following courses (taught in italian) at the Milan University:
- F3X-??:
Computer programming 1
(
- 72 hours
- 9 credits
- F3X-34:
Operating systems
(
- 48 hours
- 6 credits
- F4Y-??:
Computer programming 3
(
- 24 hours
- 3 credits
Past activities
Bachelor and Master Courses
- 10/11
- Operating systems
(
- 48 hours
- 6 credits
- 10/11
- Software design and project management
(
- 48 hours
- 6 credits
- 09/10
- Computer Programming 1 (Laboratory)
(
- 48 hours
- 3 credits
- 06/07>09/10
- F88011:
Computer programming 3
(
- 24 hours
- 4 credits
- 02/03>08/09
- F47001:
Computer Programming Laboratory
(
- 48 hours
- 6 credits
- 06/07>08/09
- F743407:
Simulation - theory and techniques
(
- 24 hours
- 3 credits
- 03/04>05/06
- Computer Science
(
- 40 hours
- 3 credits
- 01/02
- Computer Science
(
- 30 hours
Courses and lectures in PhD programs and graduate schools
- 06/07
- Symbolic Processing Laboratory
(
- 20 hours
- 04/05
- Mathematica basics
(
- 10 hours
- 02/03>05/06
- Theoretical bases for learning
(
- 10 hours
- 01/02
- From synapses to rules - discovering symbolic rules from neural processed data
(
- 4 hours
- 01/02
- From synapses to rules
(
- 4 hours
Lectures within university courses
- 04/05
- Exercises for the Probability and Statistics course - Bachelor in Computer Science - Università degli Studi di Milano.
- 00/01>03/04
- Lectures within the Neural Networks course - Bachelor in Computer Science - Università degli Studi di Milano.
- 00/01>03/04
- Lectures within the Probability and Statistics course - Bachelor in Computer Science - Università degli Studi di Milano.
- 98/99
- Exercises for the Probability and Statistics course - Bachelor in Computer Science - Università degli Studi di Milano Bicocca.
Lectures in vocational programs
- 01/02
- From synapses to rules - discovering symbolic rules from neural processed data
(
- 4 hours
- 07/08
- Development of computer systems
(
- 44 hours
- 04/05
- Science communication
(
- 4 hours
- 02/03>03/04
- Intelligent Systems for Symbolic Processing
(
- 6 hours
- 98/99>00/01
- Visual Basic Programming
(
- 120 hours
Other educational activities
- 03/04>04/05
- Organization of the vocational course Intelligent Systems for Symbolic Processing, funded by the FSE project - Università degli Studi di Milano.
- 03/04
- Participation in the project for teaching enhancement - Division of Mathematical, Physical and Natural Sciences - Università degli Studi di Milano.
- 2002
- Organization of the course From Synapses to rules - discovering symbolic rules from neural processed data.
Academic appointments
Evaluation committees
- 2006 > 2008
- Secretary of the committee for the assignment of student specialization grants abroad 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
- 2010 > now
- Coordinator of the committee for prospective students in computer science, Science Division, Milan University
- 2009 > now
- Member of the executive committee of the Computer Science Department, Milan University
- 2008 > 2010
- Member of the committee for prospective students in computer science, Science Division, Milan University
- 2006 > 2007
- Member of the committee for students orientation in computer science
- 2002 > 2005
- Representative of the assistant professors within the Science Division of the Milan University.
Foreign languages skill
Mother tongue: Italian
| Understanding | Speaking | Writing | |||
|---|---|---|---|---|---|
| Listening | Reading | Spoken interaction | Spoken production | ||
| English | C1 (proficient) | C2 (proficient) | C1 (proficient) | C1 (proficient) | C1 (proficient) |
| French | C1 (proficient) | C2 (proficient) | C1 (proficient) | C1 (proficient) | C1 (proficient) |
(according to the Europass language passport)
Computer skills
| Programming and scripting | C (good), Java (very good), PHP (good), Python (good), Objective C (good), Visual Basic (very good), Pascal (basics), Shell scripting (basics), Perl (basics), Cobol (good). |
|---|---|
| Operating systems | Mac OS X (good), Linux (good), Windows (good). |
| Personal productivity | LaTeX (very good), Word (good), Access (very good), Excel (good), PowerPoint (good). |
| Data base management | MySQL (basics). |
| Web | Apache (basics), Javascript (good), CGI scripting (basics), HTML (very good), CSS (good), XSL (very good). |
| Scientific productivity | Wolfram Mathematica (very good), SPSS (good), SNNS (good). |
