bicego
univrit
Curriculum
(pdf, it, 217 KB, 31/08/20)
Curriculum (English)
(pdf, en, 176 KB, 04/03/21)
Modules running in the period selected: 40.
Click on the module to see the timetable and course details.
| 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 |
