giugno
univrit
Rosalba Giugno è Professore Ordinario di Informatica presso il Dipartimento di Informatica dell'Università di Verona. Ha conseguito la Laurea in Informatica presso l'Università di Catania nel 1998 con Lode. Ha conseguito il Dottorato di Ricerca in Informatica presso l'Università di Catania nel 2003. Per il suo programma di dottorato ha trascorso tre anni di ricerca all'estero, presso l'Università del Maryland, la New York University e la Cornell University (NY). È referente del Master in Medical Bioinformatics. È PI del laboratorio InfoOmics dell'Università di Verona. Da quando si è trasferita a Verona nel 2016, guida un gruppo di ricerca composto da 5 dottorandi e diversi studenti di tesi di laurea magistrale. La sua ricerca è focalizzata su algoritmi per grafi e reti biologiche, integrazione e analisi di dati biomolecolari, modellazione di sistemi biologici per la medicina personalizzata. È autrice di 130 pubblicazioni scientifiche, 70 su riviste internazionali. È editor di Information Systems. Ha partecipato a diversi progetti nazionali ed europei, anche con partner industriali. Fa parte del comitato scientifico di congressi e scuole internazionali. Dal 2017 al 2022 è stata Direttore del laboratorio Infolife composto da 35 gruppi di ricerca denominati "nodi", che fanno capo ad altrettante Unità di Ricerca delle Università italiane.
Modules running in the period selected: 22.
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Di seguito sono elencati gli eventi e gli insegnamenti di Terza Missione collegati al docente:
| 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 |
| 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 |
| Bioinformatics and Natural Computing | Our research is mainly focused on the following topics: 1) Discrete and algorithmic analyses of biological dynamics (metabolism and replication, and their interplay in cellular processes); 2) Informational and computational analysis of genomes (genomic dictionaries, genomic indexes, genomic distributions of specific parameters, genome representations, genome synthesis and reconstruction from dictionaries). In these research areas, theories and algorithms are investigated and software packages are developed for computational experiments and analyses. |
Bioinformatics and medical informatics
Life and medical sciences |
| Computational Biology | Efficiently analyze biomedical data by developing innovative methods for mining biological networks, integrating diverse datasets, analyzing omics data, reconstructing pangenomes, performing haplotype-aware genome analyses, predicting drug repurposing and combination therapies, identifying optimal treatments, and implementing advanced strategies for patient classification and stratification. |
Information Systems and Data Analytics
Computational Biology |
| Deep learning | Focuses on training neural networks with multiple layers to automatically learn patterns and representations from large amounts of data. Using architectures such as convolutional neural networks (CNNs) for images, recurrent neural networks (RNNs) for sequential data, and transformers for diverse tasks, deep learning excels at complex tasks like image recognition, natural language processing, speech recognition, reinforcement learning, time series analysis, and autonomous driving. |
Artificial Intelligence
Machine learning |
| Systems Biology, Computational Network Biology | Design of algorithms and methods to understand biological systems using data mining and bioinformatics techniques. The focus is on biological network modeling, perturbation and analysis; classification of phenotypes by coding and non-coding expression profiles; drug synergy and mechanism of action by drug target/off-target/combination prediction. |
Bioinformatics and medical informatics
Life and medical sciences |
| Office | Collegial Body |
|---|---|
| member | Faculty Board of PhD in Computer Science - Department Computer Science |
| member | Computer Science Teaching Committee - Department Computer Science |
| member | Computer Science Department Council - Department Computer Science |
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