Ferdinando Cicalese

Foto,  January 12, 2015
Position
Associate Professor
Role
Professore Associato
Academic sector
INF/01 - INFORMATICS
Research sector (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

Office
Ca' Vignal 2,  Floor 1,  Room 1.69
Telephone
+39 045 802 7969
Fax
+39 045 802 7068
E-mail
ferdinando|cicalese*univr|it <== Replace | with . and * with @ to have the right email address.
Personal web page
http://profs.scienze.univr.it/~cicalese

Office Hours

Monday, Hours 11:00 AM - 1:00 PM,   Ca' Vignal 2, floor 1, room 1.69

Curriculum

Mi occupo di progettazione ed analisi di algoritmi, ovvero della caratterizzazione delle migliori strategie possibili per problemi risolvibili mediante strumenti informatici. La mia attività di ricerca si è principalmente concentrata su algoritmi di classificazione basati su logica fuzzy, algoritmi di ricerca fault tolerant e codifica a correzione degli errori, problemi di ottimizzazione di strategie per la valutazione di funzioni Booleane con applicazioni al machine learning, teoria dell'informazione, compressione dati, information retrieval, problemi di ottimizzazione in bioinformatica. 

Modules

Modules running in the period selected: 13.
Click on the module to see the timetable and course details.

Course Name Total credits Online Teacher credits Modules offered by this teacher
Bachelor's degree in Bioinformatics Algorithms (2018/2019)   12    ALGORITMI PER BIOINFORMATICA
Master's degree in Computer Science and Engineering Algorithms (2018/2019)   12    COMPLESSITÀ
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2018/2019)   12    (Algorithm design)
Bachelor's degree in Bioinformatics Algorithms (2017/2018)   12  eLearning ALGORITMI PER BIOINFORMATICA
Master's degree in Computer Science and Engineering Algorithms (2017/2018)   12  eLearning COMPLESSITÀ
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2017/2018)   12  eLearning (Algorithm design)
Bachelor's degree in Bioinformatics Algorithms (2016/2017)   12  eLearning ALGORITMI PER BIOINFORMATICA
Master's degree in Computer Science and Engineering Algorithms (2016/2017)   12  eLearning COMPLESSITÀ
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2016/2017)   12  eLearning (Algorithm design)
Bachelor's degree in Bioinformatics Algorithms (2015/2016)   12  eLearning ALGORITMI PER BIOINFORMATICA
Master's degree in Computer Science and Engineering Algorithms (2015/2016)   12  eLearning COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2014/2015)   12  eLearning ALGORITMI PER BIOINFORMATICA
Master's degree in Computer Science and Engineering Algorithms (2014/2015)   12  eLearning COMPLESSITÀ

Advanced teaching activities
Name Online
PhD course "Computational methods for handling textual data" (31° Ciclo - PhD in Computer Science)
 

Research groups

Bioinformatics and Natural Computing
Algorithmic analysis of biological processes.
Skills
Topic Description Research area
Algorithms for Bioinformatics Our research focuses on design of algorithms for the management and analysis of 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), as well as, information theoretic approaches based on entropic analysis, 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