bicego
univrit
Giovedì h 10.30 -11.30 (in alternativa su appuntamento da concordare via email)
Thursday h 10.30-11.30 (in alternative, please send an email to schedule)
Curriculum
(pdf, it, 217 KB, 31/08/20)
Curriculum (English)
(pdf, en, 176 KB, 04/03/21)
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.sci.univr.it/~bicego
Modules running in the period selected: 40.
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MyUnivr| Topic | Description | Research area |
|---|---|---|
| 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 |
| Pattern Recognition | The main focus is on the study and development of automatic techniques and models able to extract information from real world data, typically in terms of classes or clusters. Special attention is on probabilistic models - like Hidden Markov Models, Mixtures, Topic Models - and on kernel machines - like Support Vector Machines. In these contexts the interest is in designing novel models/methodologies, like hybrid generative-discriminative methods, generative embeddings and kernels, novel classification or clustering schemes, model selection techniques and others. The focus is on reasoning on representation issues (how to extract features, how to process the original problem space) as well as on unconventional employment of standard techniques (like boosting or SVM for clustering). Another field of interest is the processing of sequential data (using for example Hidden Markov Model). |
Machine Intelligence
Machine learning |
| Computer Vision | Investigation of computational tools for the analysis of images and videos, with main goal of extracting useful information. In particular, 2D/3D object classification, 3D reconstruction, person detection and classification, video analysis and understanding, activity recognition, and in biometrics (face recognition and authentication, facial feature extraction, multimodal biometrics, behavioural biometrics) with application in video surveillance and biomedical image analysis. |
Machine Intelligence
Artificial intelligence |
| Office | Collegial Body |
|---|---|
| member | PhD Commitee - Department Computer Science |
| member | Computer Science Teaching Committee - Department Computer Science |
| member | Computer Science Department Council - Department Computer Science |
