castellani
univrit
Tuesday,
Hours 11:00 AM
- 1:00 PM,
Ca' Vignal 2, Floor 1, room 47
The meeting can be organized online (please write to: umberto.castellani@univr.it).
Curriculum (EN)
(pdf, en, 81 KB, 28/02/23)
Curriculum (ITA)
(pdf, it, 89 KB, 28/02/23)
Modules running in the period selected: 79.
Click on the module to see the timetable and course details.
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MyUnivrDi seguito sono elencati gli eventi e gli insegnamenti di Terza Missione collegati al docente:
| Topic | Description | Research area |
|---|---|---|
| Computational Geometry | Shape analysis: development of local and global shape descriptors, extraction of curve skeletons from 3D meshes, mesh processing tools. Shape retrieval methodologies and benchmarking. |
Bioinformatics and medical informatics
Computer graphics |
| Computational Geometry | Shape analysis: development of local and global shape descriptors, extraction of curve skeletons from 3D meshes, mesh processing tools. Shape retrieval methodologies and benchmarking. |
Artificial Intelligence
Computer graphics |
| Image processing and computer vision | Basic image processing, e.g. image enhancement, interpolation (digital zooming). Image processing applied to medical images, 2D and 3D segmentation, feature extraction, texture analysis. Optical flow extraction and processing. | IMAGE PROCESSING AND COMPUTER VISION |
| Neuroimaging | The main activity is in the field of neuroimaging, including structural (diffusion MRI) and functional (EEG, fNIRS, functional MRI) imaging as well as perceptual analysis through cognitive science methods. The goal is to gain an holistic view of human brain when inspected in specific conditions by the integration of multi-modal multi-scale probing and modeling. In addition, advanced computer vision and pattern recognition methods are employed for designing numerical biomarkers for the characterization of healthy and pathological conditions. |
Bioinformatics and medical informatics
Life and medical sciences |
| Neuroimaging | The main activity is in the field of neuroimaging, including structural (diffusion MRI) and functional (EEG, fNIRS, functional MRI) imaging as well as perceptual analysis through cognitive science methods. The goal is to gain an holistic view of human brain when inspected in specific conditions by the integration of multi-modal multi-scale probing and modeling. In addition, advanced computer vision and pattern recognition methods are employed for designing numerical biomarkers for the characterization of healthy and pathological conditions. |
Algorithms, Logic, and Theory of Computing
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). |
Bioinformatics and medical informatics
Machine learning |
| 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). |
Artificial Intelligence
Machine learning |
| Office | Collegial Body |
|---|---|
| member | Faculty Board of PhD in Computer Science - Department Computer Science |
| member | Computer Science Teaching Committee - Department Computer Science |
| member | Information Engineering Teaching Committee - Department Department of Engineering for Innovation Medicine |
| Comitato scientifico del Corso di Perfezionamento e di Aggiornamento professionale in Tecniche di comunicazione aziendale - Department Management | |
| Scientific Committee for the Masters Degree in the Production of Video clips and Multimedia Communication | |
| Comitato Scientifico del Master Universitario in Digital Content Creation (I livello) - Department Computer Science | |
| member | Commissione AQ Laurea magistrale in Ingegneria e Scienze Informatiche - LM18-32 - Computer Science Teaching Committee - Department Computer Science |
| member | Computer Science Department Council - Department Computer Science |
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