bicego
univrit
Ricevimento: su appuntamento via email
Office hours: by appointment requested via email
Curriculum
(pdf, it, 186 KB, 18/02/25)
Curriculum (English)
(pdf, en, 182 KB, 30/01/25)
La ricerca di Manuele Bicego si focalizza principalmente sulla Statistical Pattern Recognition, una disciplina che mira a studiare e sviluppare tecniche, algoritmi e modelli per l’analisi di dati reali, tipicamente caratterizzati in termini di classi o gruppi (categorie). In questo contesto Manuele Bicego ha prodotto contributi sia metodologici che applicativi, legati a diversi contesti, quali l’analisi di immagini e segnali, la biometria e, in maniera preponderante, la bioinformatica (analisi di dati di espressione genica, proteomica, metabolomica).
L’intensa attività scientifica in questi contesti è confermata dalle numerose pubblicazioni in importanti riviste e conferenze internazionali, dai premi e riconoscimenti ricevuti, dalla continua attività editoriale e di revisione, dalla partecipazione a vari progetti di ricerca finanziati, e dalle numerose collaborazioni instaurate con diversi centri di ricerca, corroborate da periodi anche lunghi di soggiorni e scambi.
Per maggiori informazioni si veda la pagina http://profs.scienze.univr.it/~bicego
Modules running in the period selected: 53.
Click on the module to see the timetable and course details.
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 Computational Biology | Design and testing of Pattern Recognition and Machine Learning techniques for the analysis and understanding of biological data. In particular the main focus in on designing solutions for the analysis of "counting data" - namely data which express the level of presence of entities (e.g. gene expression data, or proteomics data) - using probabilistic graphical models like topic models. The main goal is to devise highly interpretable solutions, being interpretability of methods and solutions the most stringent need in nowadays bioinformatics research. |
Bioinformatics and medical informatics
Life and medical sciences |
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