Alessandro Farinelli

foto,  October 24, 2023
Position
Full Professor
Academic sector
INFO-01/A - Informatics
Research sector (ERC-2024)
PE6_7 - Artificial intelligence, intelligent systems, natural language processing

PE6_5 - Security, privacy, cryptology, quantum cryptography

Research sector (ERC)
PE6_7 - Artificial intelligence, intelligent systems, multi agent systems

PE6_5 - Cryptology, security, privacy, quantum crypto

Office
Ca' Vignal 2,  Floor 1,  Room 64B
Telephone
+39 045 802 7842
E-mail
alessandro|farinelli*univr|it <== Replace | with . and * with @ to have the right email address.
Personal web page
https://profs.scienze.univr.it/~farinelli/

Office Hours

Monday, Hours 10:30 AM - 12:30 PM,  

Curriculum
  • pdf   CV (en)   (pdf, en, 240 KB, 03/06/24)
  • pdf   CV (it)   (pdf, it, 246 KB, 03/06/24)

Alessandro Farinelli is full professor at the University of Verona, Department of Computer Science.

His research interests focus on the development of innovative methodologies for Artificial Intelligence systems with applications in the field of robotics. In particular, his main research topics focus on:
-- coordination of multi-agent systems
-- distributed optimization
-- reinforcement learning
-- data analysis for cyber-physical systems.

Alessandro Farinelli has been scientific responsible for several national and international research projects focused on issues related to the development of Artificial Intelligence systems. His scientific contributions target mainly international journals in the area of Artificial Intelligence (e.g., Artificial Intelligence and Journal of Artificial Intelligence Research) and intelligent robotic systems (Autonomous Robots and Robotics and Autonomous Systems). The major conferences he contributes to, both as a speaker and organizer, include the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), the International Joint Conference on Artificial Intelligence (IJCAI), and the International Conference on Intelligent Robots and Systems (IROS).

Modules

Modules running in the period selected: 48.
Click on the module to see the timetable and course details.

Course Name Total credits Online Teacher credits Modules offered by this teacher
Bachelor's degree in Computer Science Artificial Intelligence (2024/2025)   6  eLearning
Master's degree in Artificial intelligence Planning and Automated Reasoning (2024/2025)   12  eLearning PLANNING
Master's degree in Artificial intelligence AI & Robotics (2024/2025)   6    (Teoria)
Bachelor's degree in Bioinformatics Artificial intelligence (2023/2024)   6  eLearning
PhD in Computer Science Autonomous Agents and Multi-Agent Systems (2023/2024)   5  eLearning
Master's degree in Computer Engineering for Robotics and Smart Industry Mobile robotics (2023/2024)   6  eLearning (Teoria)
Master's degree in Artificial intelligence Planning and Automated Reasoning (2023/2024)   12  eLearning PLANNING
Master's degree in Artificial intelligence Cooperative Game Theory in the (Deep) RL Era (2023/2024)   2     
Master's degree in Computer Engineering for Robotics and Smart Industry Mobile robotics (2022/2023)   6  eLearning (Teoria)
Master's degree in Artificial intelligence Planning and Automated Reasoning (2022/2023)   12  eLearning PLANNING
Bachelor's degree in Computer Science Programming I [Matricole dispari] (2022/2023)   12  eLearning (Teoria)
Master's degree in Computer Science and Engineering Foundations of Artificial Intelligence (2021/2022)   6  eLearning (Laboratorio)
(Teoria)
Master's degree in Computer Engineering for Robotics and Smart Industry Mobile robotics (2021/2022)   6  eLearning (Laboratorio)
(Teoria)
Master's degree in Data Science Statistical learning (2021/2022)   6  eLearning PART I
Master's degree in Computer Science and Engineering Foundations of Artificial Intelligence (2020/2021)   6  eLearning (Laboratorio)
(Teoria)
Master's degree in Computer Engineering for Robotics and Smart Industry Mobile robotics (2020/2021)   6  eLearning (Laboratorio)
(Teoria)
Master's degree in Data Science Statistical learning (2020/2021)   6  eLearning (Teoria)
(Laboratorio)
Bachelor's degree in Bioinformatics Algorithms (2019/2020)   12  eLearning LABORATORIO DI PROGRAMMAZIONE II (Teoria)
LABORATORIO DI PROGRAMMAZIONE II (Laboratorio)
Master's degree in Computer Science and Engineering Foundations of Computing (2019/2020)   12  eLearning INTELLIGENZA ARTIFICIALE (Laboratorio)
INTELLIGENZA ARTIFICIALE (Teoria)
Bachelor's degree in Bioinformatics Algorithms (2018/2019)   12  eLearning LABORATORIO DI PROGRAMMAZIONE II (Teoria)
LABORATORIO DI PROGRAMMAZIONE II (Laboratorio)
Master's degree in Computer Science and Engineering Foundations of Computing (2018/2019)   12  eLearning INTELLIGENZA ARTIFICIALE (Laboratorio)
INTELLIGENZA ARTIFICIALE (Teoria)
Bachelor's degree in Bioinformatics Algorithms (2017/2018)   12  eLearning LABORATORIO DI PROGRAMMAZIONE II
Master's degree in Computer Science and Engineering Foundations of Computing (2017/2018)   12  eLearning INTELLIGENZA ARTIFICIALE
Bachelor's degree in Bioinformatics Algorithms (2016/2017)   12  eLearning LABORATORIO DI PROGRAMMAZIONE II
Master's degree in Computer Science and Engineering Foundations of Computing (2016/2017)   12  eLearning INTELLIGENZA ARTIFICIALE
Bachelor's degree in Bioinformatics Algorithms (2015/2016)   12    LABORATORIO DI PROGRAMMAZIONE II
Master's degree in Computer Science and Engineering Foundations of Computing (2015/2016)   12    INTELLIGENZA ARTIFICIALE
Bachelor's degree in Bioinformatics Algorithms (2014/2015)   12    LABORATORIO DI PROGRAMMAZIONE II
Master's degree in Computer Science and Engineering Artificial Intelligence (2014/2015)   6   
Bachelor's degree in Bioinformatics Algorithms (2013/2014)   12    LABORATORIO DI PROGRAMMAZIONE II
Master's degree in Computer Science and Engineering Artificial Intelligence (2013/2014)   6   
Bachelor's degree in Bioinformatics Algorithms (2012/2013)   12    LABORATORIO DI PROGRAMMAZIONE II
Master's degree in Computer Science and Engineering Artificial Intelligence (2012/2013)   6   
Bachelor's degree in Bioinformatics Algorithms (2011/2012)   12    LABORATORIO DI PROGRAMMAZIONE II
Master's degree in Computer Science and Engineering Artificial Intelligence (2011/2012)   6   
Bachelor's degree in Bioinformatics Algorithms (2010/2011)   12    LABORATORIO DI PROGRAMMAZIONE II
Master's degree in Computer Science and Engineering Automated reasoning (2010/2011)   6   
Bachelor's degree in Bioinformatics Algorithms (2009/2010)   12    LABORATORIO DI PROGRAMMAZIONE II
Master's degree in Computer Science and Engineering Automated reasoning (2009/2010)   6   

Di seguito sono elencati gli eventi e gli insegnamenti di Terza Missione collegati al docente:

  • Eventi di Terza Missione: eventi di Public Engagement e Formazione Continua.
  • Insegnamenti di Terza Missione: insegnamenti che fanno parte di Corsi di Studio come Corsi di formazione continua, Corsi di perfezionamento e aggiornamento professionale, Corsi di perfezionamento, Master e Scuole di specializzazione.

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
PARCO – Parallel Computing
The aim of the research group is the development and optimization of Software targeting multi-core CPU/many-core GPUs for resource constrained computing platform (e.g., Edge Computing) and for High-performance Computing (HPC) platforms.
Robotics, Artificial Intelligence and Control
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. These area focuses on the interaction and integration of solution technques for several research topics such as automated planning and reasoning, reinforcement learning, statistical learning and reasoning in face of uncertainty. Artificial Intelligence
Distributed artificial intelligence
AI & robotics Application of AI techniques to increase the autonomy level of robotic systems. This includes the adaptation of algorithms for autonomous planning and reinforcement learning to: i) handle the cyber-physical constraints imposed by robots operating in partially observable and uncertain scenarios; ii) guarantee the reliability and robustness of robotic systems that operate in open environments (e.g., interacting with humans and other robotic systems); iii) facilitate the use of robotic systems in realistic application by proposing novel paradigms of interaction with users (e.g., train a robot to execute a task rather than specify a control program). Artificial Intelligence
Planning and scheduling
Unsupervised learning Is an approach where models are trained on unlabeled data, with the goal of identifying hidden patterns or structures within the data without predefined labels. It is commonly used for tasks like clustering, dimensionality reduction, and anomaly detection. Open research in unsupervised learning focuses on improving the ability to discover meaningful structures in complex, high-dimensional datasets, often with limited prior knowledge. Key challenges include developing more effective clustering algorithms, improving the interpretability of models that uncover latent structures, and handling high levels of noise or sparsity in data. Additionally, there is ongoing work to bridge the gap between unsupervised learning and other paradigms, such as semi-supervised, self-supervised or contrastive learning, and to enhance the robustness of unsupervised models in real-world applications. Artificial Intelligence
Machine learning
Reinforcement learning Reinforcement Learning (RL) is a learning paradigm where agents to learn how to take a sequence of decisions through interactions with their environment. RL trains a model by considering a reward signal that is associated with the actions performed in the environment (high reward for good actions and the opposite). The model aims at optimizing the expected accumulated reward over time. RL is very intersting for practical applications (e.g., robotics, recommender systems) because it requires minimal specifications from the user and it can adapt to unpredicatble changes in the enrvironment. Main challenges relates to devising safe policies for the agents, e.g., learning while avoiding catastrophic falures (safe reinforcement learning and offline reinforcement learning), to properly evaluate the quality of a trained system, e.g., how can we guarantee that the agent will behave properly in unseen situations, and to improve sample efficiency, e.g., model-based reinforcement learning. Artificial Intelligence
Machine learning
Supervised learning Is an approach where models are trained on labeled data to learn a mapping from inputs to outputs, enabling them to predict correct labels for new, unseen data. While widely used for tasks like classification, regression, and time series forecasting, open research in this field addresses several challenges. Key questions include how to make models more robust to label noise and inconsistencies, improve sample efficiency to reduce the need for large labeled datasets, and enable effective transfer learning across different tasks and domains with limited labeled data. Additionally, addressing issues of fairness and bias in supervised models, as well as improving scalability to handle large datasets without compromising performance, and attention/transformer-based approaches remain active areas of exploration. Artificial Intelligence
Machine learning
Deep learning Focuses on training neural networks with multiple layers to automatically learn patterns and representations from large amounts of data. Using architectures such as convolutional neural networks (CNNs) for images, recurrent neural networks (RNNs) for sequential data, and transformers for diverse tasks, deep learning excels at complex tasks like image recognition, natural language processing, speech recognition, reinforcement learning, time series analysis, and autonomous driving. Artificial Intelligence
Machine learning
Explainable artificial intelligence The goal of explainable AI (XAI) is to i) explain black-box models; ii) develop AI models which are interpretable by construction. For instance, this involves causal analysis and discovery, logical models of agency (with logic programming) and logical machine learning (with inductive logic programming). XAI helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making; moreover this field focuses on methods for improving model and decision interpretation using statistical and graphical tools. Artificial Intelligence
Machine learning
Planning under uncertainty Planning under uncertainty focuses on sequential decision-making in uncertain environments, namely, situations with imperfect information. (Partially Observable) Markov Decision Processes are used to represent these contexts. The goal of planning under uncertainty is to generate optimal policies for these problems, namely, functions able to suggest optimal actions in situations faced by the agent. The main challenges concern dealing with large problems (scalability), acquiring new knowledge about the environment (adaptability), preventing undesirable behaviors (safety), safe policy improvement (robustness), interacting with humans (human-in-the-loop), supporting human understanding (explainability), bridging planning and reinforcement learning (model-based RL), bridging symbolic and probabilistic/data-driven planning. Among the most recent approaches to tackle these challenges, online methods based on Monte Carlo Tree Search have achieved strong results in the last years in both strategic games (e.g., board games such as Go) and real-world applications (e.g., robotics, cyber-physical systems, and decision support systems). Artificial Intelligence
Planning and scheduling
Multi-agent planning Multiagent planning deals with planning approaches applied to multi-agent systems. The main goal of these techniques is to generate solutions for sequential decision making that promote synergy among multiple autonomous agents to achieve collective goals. Among the main topic of this field there are decentralized optimization, multiagent path planning, multiagent learning, cooperation and coordination. Important tools in this fiels are, for instance, coordination graphs that are used in recent cooperative multi-agent planning and reinforcement learning (MARL) algorithms where coordination between agents is essential to accomplish the task. Coordination graphs allow to represent how agents can coordinate using some communication via message passing. Applications of multiagent planning span over a wide set of domains including autonomous driving, logistic (e.g., fleet of autonomous robots), environmental monitoring (fleet of mobile drones for data acquisition). Artificial Intelligence
Planning and scheduling
Neurosymbolic planning Neurosymbolic AI focuses on combining standard data-driven AI (e.g., reinforcement learning) with symbolic approaches (e.g., logic programming and inductive logic programming), in order to enhance the explainability of AI systems (e.g., autonomous agents), their efficacy in human-robot interaction, and foster incremental knowledge acquisition and generalization in planning. Artificial Intelligence
Planning and scheduling
Multi agent systems Design and development of multiagent systems, where intelligent agents can interact among them, with the environment and with humans. This area focuses on the interaction and integration of solution techniques related to multiagent planning, statistical learning, multi-agent reinforcement learning and game theory. Artificial Intelligence
Distributed artificial intelligence
Projects
Title Starting date
Comparative analysis of solutions based on evolutionary algorithms for generalized and multi-objective VRP 12/21/23
Development of Artificial Intelligence methods to support insurance policy sales. 11/21/22
Development of an efficient and robust motion planning solution for hyper-redundant robotic manipulators 10/1/22
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
ROS-based design and synthesis of monitors for semi-formal verification of robotics applications 3/9/20
Support for data acquisition, management and analysis in the context of "smart-land" applications 1/27/20
Study and development of machine learning techniques for data prediction. 10/22/19
Model-Based Design and Verication Flow for Embedded Vision Applications 2/26/19
Computer Engineering for Industry 4.0 1/1/18
Active Malware Analysis based on Reinforcement Learning techniques 1/1/18
COREWOOD - Riposizionamento competitivo del la filiera del legno 11/7/17
GHOTEM - Global House Thermal & Electrical Energy Management 11/7/17
Development of an ICT platform for sport performance analysis 12/13/16
INTCATCH- Development and application of Novel, Integrated Tools for monitoring and managing Catchments 6/1/16
EXPO-AGRI - EXtra-field Plant Observation for monitoring and forecast of agricultural infections - Joint Projects 2015 2/1/16
Automatic process control for energy saving and resource recovery in waste water management 3/28/14
Piattaforme di servizio P2P: rete logica, requisiti e strumenti (PRIN 2008) 1/27/10




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