Ferdinando Cicalese

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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 3:00 PM - 5:00 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: 26.
Click on the module to see the timetable and course details.

Course Name Total credits Online Teacher credits Modules offered by this teacher
Master's degree in Computer Science and Engineering Algorithmic Game Theory (2021/2022)   6    6 
Bachelor's degree in Bioinformatics Informational Methods (2021/2022)   6  eLearning 6 
PhD in Computer Science Introduzione al Machine Teaching: Teoria e Applicazioni (2021/2022)   3    3 
Master's degree in Computer Science and Engineering Problems solving: Algorithms and Complexity (2021/2022)   12    6  (Teoria)
Master's degree in Computer Science and Engineering Algorithmic Game Theory (2020/2021)   6  eLearning 6 
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2020/2021)   12  eLearning 3  (Algorithm design)
Bachelor's degree in Bioinformatics Informational Methods (2020/2021)   6  eLearning 3 
PhD in Computer Science Introduzione al Machine Teaching: Teoria e Applicazioni (2020/2021)   2    2 
Master's degree in Computer Science and Engineering Problems solving: Algorithms and Complexity (2020/2021)   12  eLearning 6 
Master's degree in Computer Science and Engineering Algorithms (2019/2020)   12  eLearning 6  COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2019/2020)   12  eLearning 6  ALGORITMI PER BIOINFORMATICA
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2019/2020)   12  eLearning 6  (Algorithm design)
PhD in Computer Science Attività didattica dottorato (2019/2020)   50  eLearning 1 
Master's degree in Computer Science and Engineering Algorithms (2018/2019)   12  eLearning 6  COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2018/2019)   12  eLearning 6  ALGORITMI PER BIOINFORMATICA
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2018/2019)   12  eLearning 6  (Algorithm design)
Master's degree in Computer Science and Engineering Algorithms (2017/2018)   12  eLearning 6  COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2017/2018)   12  eLearning 6  ALGORITMI PER BIOINFORMATICA
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2017/2018)   12  eLearning 6  (Algorithm design)
Bachelor's degree in Bioinformatics Algorithms (2016/2017)   12  eLearning 6  ALGORITMI PER BIOINFORMATICA
Master's degree in Computer Science and Engineering Algorithms (2016/2017)   12  eLearning 6  COMPLESSITÀ
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2016/2017)   12  eLearning 6  (Algorithm design)
Master's degree in Computer Science and Engineering Algorithms (2015/2016)   12  eLearning 6  COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2015/2016)   12  eLearning 6  ALGORITMI PER BIOINFORMATICA
Bachelor's degree in Bioinformatics Algorithms (2014/2015)   12  eLearning 6  ALGORITMI PER BIOINFORMATICA
Master's degree in Computer Science and Engineering Algorithms (2014/2015)   12  eLearning 6  COMPLESSITÀ

 
 

Research groups

Algorithms
The group investigates structural aspects of fundamental problems in Computer Science and their mathematical models. This leads to the design of better algorithms protocols and systems as well as understanding of their implicit computational limits. Specific areas of interests include: algorithm design, data structures, string algorithms, computational complexity, combinatorial optimization, coding and information theory, machine learning. Most results obtained are in the intersection of algorithmics with several other areas in theory and applications, including bioinformatics, communication networks, operating research and artificial intelligence.
Algorithmic Bioinformatics and Natural Computing
Application of theoretical methods and data analysis to model information underlying biological processes: graph and string algorithms for systems biology; advanced data structures for sequence data; distance measures for biological sequences; natural (biotechnological, membrane) computing; pattern recognition, machine learning for biomedical data.
Bioinformatics and Natural Computing
Algorithmic analysis of biological processes.
Artificial Intelligence (AI)
The group conducts research in Artificial Intelligence, including Automated Reasoning, Search Algorithms, Knowledge Representation, Machine Learning, Multi-Agent Systems, and their applications.
Research interests
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. Bioinformatics and medical informatics
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. Machine Intelligence
Machine learning


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