Alberto Castellini

Alberto Castellini Foto 2019,  January 24, 2020
Position
Temporary Assistant Professor
Academic sector
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Research sector (ERC)
PE6_7 - Artificial intelligence, intelligent systems, multi agent systems

Office
Ca' Vignal 2,  Floor 1,  Room 80
Telephone
+39 045 802 7908
E-mail
alberto|castellini*univr|it <== Replace | with . and * with @ to have the right email address.
Personal web page
http://profs.scienze.univr.it/~castellini/

Office Hours

Lunedi 16.00-18.00 (previo accordo via email)

Curriculum

Italian version: Alberto Castellini è ricercatore a tempo determinato (Rtd-b) presso il dipartimento di informatica dell'Università di Verona. I suoi interessi di ricerca si focalizzano sullo sviluppo di tecniche di Intelligenza Artificiale, Machine Learning e Data Analysis, con applicazioni su sistemi intelligenti di vario genere, come sistemi ciberfisici, sistemi robotici, smart grid e smart building. Le principali metodologie su cui lavora sono modelli predittivi per serie temporali multivariate, metodi per pianificazione con incertezza, apprendimento con rinforzo, tecniche di regressione e clustering, interpretabilita' di modelli predittivi, situation assessment e rilevamento di anomalie. Alberto Castellini ha partecipato a numerosi progetti di ricerca nazionali ed internazionali occupandosi di tematiche relative allo sviluppo di modelli predittivi e decisionali. I suoi principali lavori sono pubblicati su riviste scientifiche internazionali di intelligenza artificiale e sistemi intelligenti (e.g., Engineering Applications of Artificial Intelligence, IEEE Intelligent Systems, Robotics and Autonomous Systems). Le conferenze internazionali a cui ha contribuito come autore sono, ad esempio, International Joint Conference on Artificial Intelligence (IJCAI), International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) e International Conference on Automated Planning and Scheduling (ICAPS). Inoltre partecipa ai comitati di programma di conferenze come AAAI Conference on Artificial Intelligence, Conference on Uncertainty in Artificial Intelligence ed altre nell'ambito dell'intelligenza artificiale.

English version: Alberto Castellini is a fixed-term researcher (Rtd-b) at the computer science department of the University of Verona. His research interests focus on the development of Artificial Intelligence, Machine Learning and Data Analysis techniques, with applications on intelligent systems of various kinds, such as, cyber-physical systems, robotic systems, smart grids and smart buildings. The main methodologies he works on are predictive models for multivariate time series, methods for planning under uncertainty, reinforcement learning, regression and clustering techniques, interpretability of predictive models, situation assessment and anomaly detection. Alberto Castellini has participated in numerous national and international research projects dealing with the development of predictive and decision-making models. His main works are published in international scientific journals on artificial intelligence and intelligent systems (e.g., Engineering Applications of Artificial Intelligence, IEEE Intelligent Systems, Robotics and Autonomous Systems). The international conferences he has contributed to as an author are, for example, the International Joint Conference on Artificial Intelligence (IJCAI) and the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) and International Conference on Automated Planning and Scheduling (ICAPS). He also participates in conference program committees such as AAAI Conference on Artificial Intelligence, the Conference on Uncertainty in Artificial Intelligence and others in the field of artificial intelligence.

Modules

Modules running in the period selected: 30.
Click on the module to see the timetable and course details.

Course Name Total credits Online Teacher credits Modules offered by this teacher
Master's degree in Data Science Statistical learning (2024/2025)   6    (Parte II)
(Parte I)
Bachelor's degree in Computer Science Programming I [Matricole dispari] (2023/2024)   12  eLearning (Teoria)
Master's degree in Artificial intelligence Reinforcement learning and Advanced programming for AI (2023/2024)   12  eLearning (Reinforcement Learning)
Master's degree in Data Science Statistical learning (2023/2024)   6  eLearning PART I
PART II
Bachelor's degree in Computer Science Programming I [Matricole dispari] (2022/2023)   12  eLearning (Teoria)
Master's degree in Artificial intelligence Reinforcement learning and Advanced programming for AI (2022/2023)   12  eLearning (Reinforcement Learning)
Master's degree in Data Science Statistical learning (2022/2023)   6  eLearning PART II
PART I
Bachelor's degree in Biotechnology Information Technology (2021/2022)   6  eLearning
Bachelor's degree in Computer Science Operating Systems (2021/2022)   12  eLearning [Laboratorio 2] (Laboratorio)
[Laboratorio 1] (Laboratorio)
Master's degree in Data Science Statistical learning (2021/2022)   6  eLearning PART II
Bachelor's degree in Computer Science Operating Systems (2020/2021)   12  eLearning [Laboratorio 2] (Laboratorio)
Master's degree in Mathematics Statistical learning (2020/2021)   6  eLearning PART II
Master's degree in Data Science Statistical learning (2020/2021)   6  eLearning (Teoria)
Bachelor's degree in Computer Science Operating Systems (2019/2020)   12  eLearning [Laboratorio 1] (Laboratorio)
Master's degree in Mathematics Statistical learning (2019/2020)   6  eLearning PART II
Bachelor's degree in Computer Science Operating Systems (2018/2019)   12  eLearning [Laboratorio 2] (Laboratorio)
Master's degree in Mathematics Statistical methods for data analysis (2018/2019)   6  eLearning (Machine learning)
Bachelor's degree in Bioinformatics Introduction to Programming (2017/2018)   12  eLearning PROGRAMMAZIONE PER BIOINFORMATICA
Master's degree in Bioinformatics and Medical Biotechnology Algorithms and programming languages for bioinformatics (2013/2014)   12    LINGUAGGI PER BIOINFORMATICA (laboratorio)
LINGUAGGI PER BIOINFORMATICA (teoria)
Master's degree in Bioinformatics and Medical Biotechnology Algorithms and programming languages for bioinformatics (2012/2013)   12    LINGUAGGI PER BIOINFORMATICA (Teoria)
LINGUAGGI PER BIOINFORMATICA (Laboratorio)
Master's degree in Bioinformatics and Medical Biotechnology Algorithms and programming languages for bioinformatics (2011/2012)   12    LINGUAGGI PER BIOINFORMATICA (Teoria)
Master's degree in Bioinformatics and Medical Biotechnology Algorithms and programming languages for bioinformatics (2010/2011)   12    LINGUAGGI PER BIOINFORMATICA (Teoria)
LINGUAGGI PER BIOINFORMATICA (Laboratorio)
ALGORITMI PER BIOINFORMATICA

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Research groups

Artificial Intelligence (AI)
The group conducts research in Artificial Intelligence, including Automated Reasoning, Search Algorithms, Knowledge Representation, Machine Learning, Multi-Agent Systems, and their applications.
ISLa - Intelligent Systems Lab
Artificial intelligence, statistical learning and data analysis for intelligent systems
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. Machine Intelligence
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. 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
Projects
Title Starting date
Comparative analysis of solutions based on evolutionary algorithms for generalized and multi-objective VRP 12/21/23
SPACE13 INNOVATION-LAB 1/11/22
Intelligent Heating Control based on Reinforcement Learning Techniques 10/1/20
SAFE PLACE Sistemi IoT per ambienti di vita salubri e sicuri 9/10/20
Support for data acquisition, management and analysis in the context of "smart-land" applications 1/27/20
Computer Engineering for Industry 4.0 1/1/18
PreMANI – Manifattura Predittiva: progettazione, sviluppo e implementazione di soluzioni di Digital Manufacturing per la previsione del la Qualità e la Manutenzione Intelligente 11/7/17
INTCATCH- Development and application of Novel, Integrated Tools for monitoring and managing Catchments 6/1/16




Organization

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