Alberto Castellini

Alberto Castellini Foto 2019,  January 24, 2020
alberto|castellini*univr|it <== Replace | with . and * with @ to have the right email address.
Not present since
September 30, 2021



Research interests
Topic Description Research area
Intelligent Agents Design and development of autonomous entities that can sense, model and interact with the environment in which they operate. Main research topics include: action planning, reinforcement learning, reasoning in face of uncertainty. Computing methodologies - Artificial intelligence
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. Applied 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). Computing methodologies - Machine learning

© 2002 - 2021  Verona University
Via dell'Artigliere 8, 37129 Verona  |  P. I.V.A. 01541040232  |  C. FISCALE 93009870234