Daniele Meli

Foto Meli Daniele,  July 11, 2023
Position
Temporary Assistant Professor
Role
RTD-A
Academic sector
IINF-05/A - Information Processing Systems
Research sector (ERC-2024)
PE6_7 - Artificial intelligence, intelligent systems, natural language processing

Office
Ca' Vignal 2,  Floor 1,  Room 80
Telephone
+39 045 802 7908
E-mail
daniele|meli*univr|it <== Replace | with . and * with @ to have the right email address.

Office Hours

Si riceve su appuntamento

Curriculum

Modules

Modules running in the period selected: 7.
Click on the module to see the timetable and course details.

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

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. 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
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
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
Projects
Title Starting date
Development of an efficient and robust motion planning solution for hyper-redundant robotic manipulators 10/1/22
ARS - Autonomous Robotic Surgery 10/1/17




Organization

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