Ferdinando Cicalese

Foto,  January 12, 2015
Position
Full Professor
Academic sector
INFO-01/A - Informatics
Research sector (ERC-2024)
PE6_6 - Algorithms and complexity, 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, automata

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
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

Tuesday, Hours 2:30 PM - 4:30 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: 35.
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 Artificial intelligence Computational Game Theory (2024/2025)   6  eLearning
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2024/2025)   12  eLearning (Algorithm design)
Master's degree in Computer Science and Engineering Problems solving: Algorithms and Complexity (2024/2025)   12    (Teoria)
PhD in Computer Science Elements of Machine Teaching (2024/2025)   3  eLearning
Master's degree in Artificial intelligence Computational Game Theory (2023/2024)   6  eLearning
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2023/2024)   12  eLearning (Algorithm design)
Master's degree in Computer Science and Engineering Problems solving: Algorithms and Complexity (2023/2024)   12  eLearning (Teoria)
PhD in Computer Science Elements of Machine Teaching: Theory and Appl. (2023/2024)   3   
Master's degree in Artificial intelligence Computational Game Theory (2022/2023)   6  eLearning
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2022/2023)   12  eLearning (Algorithm design)
PhD in Computer Science Lezioni Dottorandi (2022/2023)   50  eLearning
Master's degree in Computer Science and Engineering Problems solving: Algorithms and Complexity (2022/2023)   12  eLearning (Teoria)
Master's degree in Computer Science and Engineering Algorithmic Game Theory (2021/2022)   6  eLearning
Bachelor's degree in Bioinformatics Informational Methods (2021/2022)   6  eLearning
Master's degree in Computer Science and Engineering Problems solving: Algorithms and Complexity (2021/2022)   12  eLearning (Teoria)
Master's degree in Computer Science and Engineering Algorithmic Game Theory (2020/2021)   6  eLearning
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2020/2021)   12  eLearning (Algorithm design)
Bachelor's degree in Bioinformatics Informational Methods (2020/2021)   6  eLearning
Master's degree in Computer Science and Engineering Problems solving: Algorithms and Complexity (2020/2021)   12  eLearning
Master's degree in Computer Science and Engineering Algorithms (2019/2020)   12  eLearning COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2019/2020)   12  eLearning ALGORITMI PER BIOINFORMATICA
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2019/2020)   12  eLearning (Algorithm design)
Master's degree in Computer Science and Engineering Algorithms (2018/2019)   12  eLearning COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2018/2019)   12  eLearning ALGORITMI PER BIOINFORMATICA
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2018/2019)   12  eLearning (Algorithm design)
Master's degree in Computer Science and Engineering Algorithms (2017/2018)   12  eLearning COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2017/2018)   12  eLearning ALGORITMI PER BIOINFORMATICA
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2017/2018)   12  eLearning (Algorithm design)
Master's degree in Computer Science and Engineering Algorithms (2016/2017)   12  eLearning COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2016/2017)   12  eLearning ALGORITMI PER BIOINFORMATICA
Master's degree in Medical Bioinformatics Fundamental algorithms for Bioinformatics (2016/2017)   12  eLearning (Algorithm design)
Master's degree in Computer Science and Engineering Algorithms (2015/2016)   12    COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2015/2016)   12    ALGORITMI PER BIOINFORMATICA
Master's degree in Computer Science and Engineering Algorithms (2014/2015)   12    COMPLESSITÀ
Bachelor's degree in Bioinformatics Algorithms (2014/2015)   12    ALGORITMI PER BIOINFORMATICA

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.

MyUnivr

Di seguito sono elencati gli eventi e gli insegnamenti di Terza Missione collegati al docente:

  • Eventi di Terza Missione: eventi di Public Engagement e Formazione Continua.
  • Insegnamenti di Terza Missione: insegnamenti che fanno parte di Corsi di Studio come Corsi di formazione continua, Corsi di perfezionamento e aggiornamento professionale, Corsi di perfezionamento, Master e Scuole di specializzazione.

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.
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
Active learning In active learning, the model iteratively queries an oracle (typically a human annotator) to label only the most informative data points that would contribute most to improving the model's accuracy. By doing so, active learning reduces the labeling cost and accelerates the model's learning process. This approach is particularly useful when labeled data is scarce or expensive to obtain. The research focuses on developing effective selection criteria to identify the most informative data points for labeling, thereby improving the efficiency of the active learning process. Artificial Intelligence
Machine learning
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
Unsupervised learning Is an approach where models are trained on unlabeled data, with the goal of identifying hidden patterns or structures within the data without predefined labels. It is commonly used for tasks like clustering, dimensionality reduction, and anomaly detection. Open research in unsupervised learning focuses on improving the ability to discover meaningful structures in complex, high-dimensional datasets, often with limited prior knowledge. Key challenges include developing more effective clustering algorithms, improving the interpretability of models that uncover latent structures, and handling high levels of noise or sparsity in data. Additionally, there is ongoing work to bridge the gap between unsupervised learning and other paradigms, such as semi-supervised, self-supervised or contrastive learning, and to enhance the robustness of unsupervised models in real-world applications. Artificial Intelligence
Machine learning
Supervised learning Is an approach where models are trained on labeled data to learn a mapping from inputs to outputs, enabling them to predict correct labels for new, unseen data. While widely used for tasks like classification, regression, and time series forecasting, open research in this field addresses several challenges. Key questions include how to make models more robust to label noise and inconsistencies, improve sample efficiency to reduce the need for large labeled datasets, and enable effective transfer learning across different tasks and domains with limited labeled data. Additionally, addressing issues of fairness and bias in supervised models, as well as improving scalability to handle large datasets without compromising performance, and attention/transformer-based approaches remain active areas of exploration. Artificial Intelligence
Machine learning
Projects
Title Starting date
Novel Methodologies and Tools for Next Generation Cyber Ranges - NOMEN 5/21/24
Algoritmo di ottimizzazione dei riposi per autisti professionali 2/22/24




Organization

Department facilities

Share