Ferdinando Cicalese

Foto,  12 gennaio 2015
Qualifica
Professore associato
Ruolo
Professore Associato
Settore disciplinare
INF/01 - INFORMATICA
Settore di Ricerca (ERC)
PE6_6 - Algorithms, distributed, parallel and network algorithms, algorithmic game theory

PE6_11 - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)

PE6_4 - Theoretical computer science, formal methods, and quantum computing

Ufficio
Ca' Vignal 2,  Piano 1,  Stanza 1.69
Telefono
+39 045 802 7969
Fax
+39 045 802 7068
E-mail
ferdinando|cicalese*univr|it <== Sostituire il carattere | con . e il carattere * con @ per avere indirizzo email corretto.
Pagina Web personale
http://profs.scienze.univr.it/~cicalese

Orario di ricevimento

lunedì, Ore 11.00 - 13.00,   Ca' Vignal 2, piano 1, stanza 1.69

Curriculum

Insegnamenti

Insegnamenti attivi nel periodo selezionato: 10.
Clicca sull'insegnamento per vedere orari e dettagli del corso.

Corso Nome Crediti totali Online Crediti del docente Moduli svolti da questo docente
Laurea in Bioinformatica Algoritmi (2017/2018)   12  eLearning ALGORITMI PER BIOINFORMATICA
Laurea magistrale in Ingegneria e scienze informatiche Algoritmi (2017/2018)   12    COMPLESSITÀ
Laurea magistrale in Medical bioinformatics Fundamental algorithms for bioinformatics (2017/2018)   12  eLearning (Algorithm design)
Laurea in Bioinformatica Algoritmi (2016/2017)   12  eLearning ALGORITMI PER BIOINFORMATICA
Laurea magistrale in Ingegneria e scienze informatiche Algoritmi (2016/2017)   12  eLearning COMPLESSITÀ
Laurea magistrale in Medical bioinformatics Fundamental algorithms for bioinformatics (2016/2017)   12  eLearning (Algorithm design)
Laurea in Bioinformatica Algoritmi (2015/2016)   12  eLearning ALGORITMI PER BIOINFORMATICA
Laurea magistrale in Ingegneria e scienze informatiche Algoritmi (2015/2016)   12  eLearning COMPLESSITÀ
Laurea in Bioinformatica Algoritmi (2014/2015)   12  eLearning ALGORITMI PER BIOINFORMATICA
Laurea magistrale in Ingegneria e scienze informatiche Algoritmi (2014/2015)   12  eLearning COMPLESSITÀ

Attività didattiche avanzate
Nome Online
PhD course "Computational methods for handling textual data" (31° Ciclo - Dottorato in Informatica)
PhD course "Computational methods for handling textual data" (30° ciclo - Dottorato in Informatica)
 

Gruppi di ricerca

Bioinformatica e Calcolo Naturale
Analisi algoritmica di processi biologici
Competenze
Argomento Descrizione Area di ricerca
Algorithms and bioinformatics We are working within the intersection of data sciences and life sciences. The focus of our research lies on development and application of algorithms for analyzing large scale biological data (e.g. -omics data of different types). Our research is driven by the idea that modern data sciences can contribute significantly to address important questions in life sciences. Algorithms from the fields of search theory (group testing), active learning, and data mining (particularly in sequences and networks) can be used to support the causal understanding, diagnosis and prognosis of complex diseases. Bioinformatica e informatica medica
Applied computing - Life and medical sciences
Decision tree optimization One of the most studied data mining tasks in the literature is the classification task, consisting of learning a predictive relationship between input values and a desired output. A classification problem can also be viewed as an optimization problem, namely as the problem of building a model that maximizes the predictive accuracy—the number of correct predictions—in the test data (unseen during training). We are interested in the problem of optimizing the construction of decision trees. Decision trees are widely used in data mining and machine learning as comprehensible representation models, given that they can be easily represented in a graphical form and also as a set of classification rules, which can be expressed in natural language in the form of IF-THEN rules. Sistemi intelligenti
Computing methodologies - Machine learning



Organizzazione

Strutture del dipartimento