Next courses (approved by the PhD Committee)
19-21-26-28 ottobre dalle ore 15.00 alle 18.00
Prof. Leonardo Montecchi dell’Università di Campinas, Brasile,
“Model-Driven Engineering and its Application to Dependability Analysis”.
Abstract:
Model-Driven Engineering advocates the use of models as primary artifacts in software and system development. In this context, a model is any structured specification that follows a well-defined language, thus including graphical models like UML as well as textual specifications. Following this philosophy, models are the “source code” of the system, and artifacts are automatically generated from such abstract representations of the intended product. This requires ad-hoc languages that are able to describe concepts of the involved domain, that is, Domain-Specific Languages (DSLs). While initially intended primary for generating code, MDE has been increasingly adopted for other purposes, and in particular to automate the analysis of non-functional properties. In this course we introduce the MDE approach, and discuss how it can be applied for automating the dependability analysis of complex systems. In particular, we will discuss how the MDE approach can be exploited to automatically generate Petri nets models from architectural descriptions of the system.
Short Bio:
Leonardo Montecchi received the Ph.D. in Computer Science, Systems and Telecommunications at university of Florence, under the supervision of Prof. Andrea Bondavalli. His expertise revolves around different aspect of the modeling of complex systems, including formal models, performability models, and model-driven engineering, with specific application to safety-critical and mission-critical application domains (e.g., automotive, space, oil&gas) and System-of-Systems (SoS) architectures. His current interests focus on applying model-driven engineering techniques to support the development and V&V of resilient systems, with specific emphasis on automated quantitative performability analysis, and on formalization of system development artifacts through domain-specific languages.
- Course teacher: Ronald Tetzlaff – Alon Ascoli, Technische Universitãt Dresde
- Course title: Recent trends in memristors theory and applications (course)
- Specially suited for Curricula: AOSC
- Proposed teaching period: March-May 2021
- CFU’s: 2 (8 hours)
- Course teacher: Stefano Maddio
- Course title: Indoor Wireless Positioning
- Specially suited for Curricula: EEE, TLC
- Proposed teaching period: postponed to June/July 2021
- CFU’s: 2 (8 hours)
Abstract:
La consapevolezza spaziale è la chiave per un nuovo mondo di applicazioni nel mondo della comunicazioni personali: navigazione museale, tracciamento dei pazienti, guida per ipo-vedenti e molte altre ancora.
Negli ambienti al chiuso, i sistemi basati sul GPS non sempre sono efficaci per questo fine. L’errore del sistema GPS può essere incompatibile con la scala spaziale richiesta dall’applicazione.
Un’alternativa all’uso del GPS consiste nel utilizzare le comunicazioni wireless, oramai onnipresenti in ambienti indoor. Con lo stesso apparato con cui vengono scambiati dati e infatti possibile stimare la posizione di dispositivi mobili, senza la necessità di sensoristica aggiuntiva.
Questo corso si propone di trattare i sistemi di localizzazione indoor basati su sistemi di smart antennas applicato ai protocolli di comunicazione esistenti.
- Course teacher: Graziano Chesi, Hong Kong University
- Course title: An introduction to SOS programming with applications in dynamical systems (course)
- Specially suited for Curricula:AOSC, INF
- Proposed teaching period: June 26- July 7, 2021
- CFU’s: 2 (8 hours)
- Course teacher: Matthew O’Donnell, Ph.D., University Washington
- Course title: Light and Sound: Integrating Photonics with Ultrasonics (Seminar)
- Specially suited for Curricula:EEE
- Proposed teaching period: 15/12/2020
- CFU’s: 1 (1hours)
January 12, 2021 – 9.30 – 12.45
January 14 2021 – 9.30 – 12.45
January 19, 2021 – 9.30 – 12.45
January 21, 2021 – 9.30 – 12.45
Proposed CFU: 4 (16 hours)
This course is offered to a PhD program in Pisa, but might be useful for students of our PhD, mainly for cycle XXXVI students or for students who did not follow prof. Chisci course held in the last academic year
- Course teachers: Antonio Frangioni (Unipi), Manlio Gaudioso (Uni Cal), Stefano Lucidi (Sapenza), Michele Monaci (Uni BO), Maria Grazia Speranza (Uni BS)
- Course title: AIRO 2021 PhD School http://opslab.dieti.unina.it/index.php/en/laboratory
- Specially suited for Curricula: AOSC, INF, TLC
- Teaching period: February 8-10, 2021
- CFU’s: 4 (16 hours)
Registration before February 4th, 2021
- Course teacher: DINFO, KTH Royal Institute of Technology
- Course title: Bioradar for Medical Applications (course)
- Specially suited for Curricula: AOSC,EEE,INF,TLC
- Proposed teaching period: Feb 10, 2021
- CFU’s: 1 (6 hours)
- Course teacher: Stefano Ricci, Enrico Boni
- Course title: Electronics Embedded Systems for time critical applications (course)
- Specially suited for Curricula: EEE
- Proposed teaching period: Februart 15,16,17, 2021
- CFU’s: 3 (12 hours)
Modern Electronics Embedded Systems represent compact, low-cost, power-efficient, programmable electronics systems. They support a very wide range of applications in the fields of motion control, IoT, home and industrial automation, sensing, etc. The heart of such systems is typically a micro-controller (uC). This course shows how to exploit uC together with its rich set of peripherals, like ADC, DMA, complex timers, power control, etc. Particular attention will be devoted to time-critical applications, where the uC is supposed to react in few us. The course is based on Laboratory activity. Students and tutors will develop together example applications on developer’s boards.
- Course teacher: Marco Bertini, Lorenzo Seidenari, Tiberio Uricchio, DINFO, Unifi
- Course title: Computer Vision and Deep Learning in Practice (course)
- Specially suited for Curricula: EEE,TLC
- Proposed teaching period: February 22, 2021
- CFU’s: 4 (16 hours)
- COurse Title: Reinforcement Learning Virtual School – https://rlvs.aniti.fr/
- Registration: before March 1, 2021
- Course teachers:
Donald A. Berry University of Texas & Rice University
Marta Garnelo DeepMind & Imperial College London
Matthieu Geist Google Brain
Leslie Kaelbling MIT
Tor Lattimore DeepMind
Jean-Baptiste Mouret Inria
Matteo Pirotta Facebook AI Research
Doina Precup McGill University & DeepMind
Emmanuel Rachelson ISAE-SUPAERO, Université de Toulouse
Antonin Raffin German Aerospace Center
Olivier Sigaud Sorbonne Université
Mengdi Wang Princeton University
Dennis Wilson ISAE-Supaero, University of Toulouse - SPecially suited for Curricula: AOSC, INF
- CFU’s: 12 (48 hours)
- Period: 25/III – 9/IV/2021
Course: Sequence and graph learning
CFU: 3
Locatione: webex, (email paolo.frasconi@pm.me to get an invitation)
- Course teacher: Franco Bagnoli, Unifi
- Course title: Introduction to statistical physics from a computational perspective (course)
- Specially suited for Curricula: AOSC
- Proposed teaching period: Any period
- CFU’s: 4 (16 hours)
Course: Explainable AI
Dates: April 15, 22, 29, 2021, 16-19.15
CFU: 3
Location: webex, (email paolo.frasconi@pm.me to get an invitation)
- Course teacher: prof. Emilio Carrizosa, Universidad de Sevilla (Spain)
- Course title: Mathematical Optimization in Machine Learning (course)
- Specially suited for curricula: AOSC, INF,
- Proposed teaching period: June 7,8,9,10, 2021
- CFU’s: 2 CFU, 8 hours
- Organizer: Fabio Schoen
Mathematical Optimization in Machine Learning
Emilio Carrizosa,
University of Seville, Spain
Mathematical Optimization is at the core of many Machine Learning problems in classification, regression and dimensionality reduction, amon others. An important challenge is to make classification and prediction algorithms more interpretable, in the sense that we should know which attributes, and at which extent, contribute in the prediction.
Mathematical Optimization allows us to pose in a natural way the multiobjective problem of optimizing the performance and, at the same time, the number of attributes or measurement costs.
In this course we will illustrate the use of Mathematical Optimization strategies in different problems, such as dimensionality reduction (sparse PCA), sparse linear models with performance constraints, cost-sensitive Support Vector Machines with performance constraints or functional data, sparse classification and regression (ensembles of) trees, interpretable clustering, etc., with special focus on the methods developed by the research group in Optimization in IMUS, the Institute of Mathematics of the University of Seville.
Title: Multi-access Edge Computing
Teachers: Gian Paolo Rossi, Christian Quadri (Università di Milano)
DATE: June 14, 15, 21, 22, 23 (9:30-13:00)
CFU Proposal: 5 CFU
Organizer: Giuseppe Anastasi, UniPi
Il corso sarà in modalità virtuale, tramite TEAMS. Per partecipare (e ricevere il link per il collegamento) occorre registrarsi compilando la seguente form online:
https://docs.google.com/forms/
Tutti i dettagi sul corso sono disponibili sul sito web del dottorato, al seguente link:
Prof. Giovanni Toso (European Space Agency):
2 seminars (2+2 hours, 2 CFU):
- Multibeam antennas for Satellite Communications
- Active Antennas for Satellite Communications
for information: prof. Stefano Selleri
- Course teacher: David Angeli, DINFO, Unifi
- Course title: Fundamentals of Economic Model Predictive Control
- Specially suited for Curricula: AOSC
- Proposed teaching period: July 2021
- CFU’s: 1 (4 hours)
- Course teacher: Alessandro Fantechi
- Course title: Guidelines for software development in safety-critical domains: examples from different transportation domains
- Specially suited for Curricula: AOSC, INF
- Teaching period: July 16,23, 2021
- CFU’s: 2 (8 hours)
Abstract
- Course teacher: Giuseppe Pelosi, Stefano Selleri
- Course title: Finite Elements for Engineers
- Specially suited for Curricula: EEE
- Proposed teaching period: July 19, 20, 21, 2021
- CFU’s: 2 (8 hours)
Abstract
The course will present the finite element method (FEM) starting from its storical
development and general impact in engineering.
It will then present the classes of problems which can be solved with it, presenting
several examples, spanning from electric machines examples, to high frequency electromagnetics
and thermal or civil engineering.
Part of the course is devoted to advanced techniques for speeding up computation.
EUROPT Summer School
online, 30 August – 1 September 2021
Theme: The need, the challenge and the success of robust and nonsmooth optimization
Lecturers:
Aharon Ben-Tal (Technion – Israel Institute of Technology)
Manlio Gaudioso (Università della Calabria, Italia)
timetable: 10 am – 12 pm and 2-4 pm CEST
webpage: https://www.euro-online.org/websites/continuous-optimization/europtschool21/
The school will take place on a fully online format with the support of the International Centre for Mathematical Sciences – Edinburgh, UK.
Attendance is free of charge but registration is mandatory. Lectures will be particularly suited for PhD students and young researchers, but the school welcomes everyone wishing to participate. To register fill this form by 24 July.
Please, spread the news within your groups and stimulate students and young researchers to attend.
- Course teacher: prof. Sotirios A. Tsaftaris (Univ. Edinburgh)
- Course titles: #1: Fundamental s of representation learning and disentangled representations
#2: Learning disentangled representations for applications
in computer vision, healthcare and text: common designs,
challenges and opportunities - Specially suited for Curricula: INF
- Proposed teaching period: October 2021
- CFU’s: 1 (4 hours)
- Organizer: prof. Carlo Colombo
- Course teacher: Manfred Jaeger, Aalborg University
- Course title: Probabilistic Graphical Models (course)
- Specially suited for Curricula: INF, AOSC
- Proposed teaching period: 27-29/10, 3-5/11/2020
- CFU’s: 3 (12 hours)
- Course teacher: Dr. Andrei Zhuravlev, Russian Academy of Science
- Course title: Development of a high-performance and compact microwave system for personnel screening on the move, designed for mass use (course)
- Specially suited for Curricula: EEE
- Proposed teaching period: 04/11/2020
- CFU’s: 1 (3 hours)
- Course teacher: Dr. Andrei Zhuravlev, Moscow State University
- Course title: On the possible use of synthetic aperture radar for non-contact testing of rails rolling surface and rail joints – (course)
- Specially suited for Curricula: EEE
- Proposed teaching period: 05/11/2020
- CFU’s: 1 (3 hours)
- Course teacher: Dr. David Root, Keysigth Tech., USA
- Course title: Microwave Enabled Quantum Computation (Seminar)
- Specially suited for Curricula: EEE
- Proposed teaching period: 11/11/2020
- CFU’s: 1 (1 hours)
- Course teacher: Dr. David Root, Keysigth Tech., USA
- Course title: Quantum Algorithms: A First Look (Seminar)
- Specially suited for Curricula: EEE
- Proposed teaching period: 10/11/2020
- CFU’s: 1 (1 hours)
- Course teacher: Dr. Lesya Anishchenko, Bauman Moscow State University
- Course title: Bioradar for Medical Applications (course)
- Specially suited for Curricula: EEE
- Proposed teaching period: 03/11/2020
- CFU’s: 1 (3 hours)
- Course teacher: Massimiliano Pierobon, University of Nebraska-Lincoln (USA)
- Course title: Molecular Communications to interface
biological and electrical computing - Specially suited for Curricula: AOSC, INF, EEE, TLC
- Teaching period: November 24,25, 2020
- proposed CFU’s: 1
- Course teacher: Massimo Bombino, Riccardo Bernardini,Tullio Vardanega, Software Sicuro srl, Univ of Udine, Univ of Padova
- Course title: Reliable Sofftware Design and Production
- Specially suited for Curricula: AOSC, INF, TLC
- Proposed teaching period:Dec 15-17, 2020
- CFU’s: 5 (21 hours)
- Course teacher: Enrico Vicario
- Course title: Applications and methods of quantitative evaluation of stochastic models
- Specially suited for Curricula: INF
- Proposed teaching period: September 2020
- CFU’s: 3 (12 hours)
– This course is about using stochastic Petri Nets to specify the probability space of some system with concurrent stochastic durations;
– … and then solving stochastic processes identified in that probability space to evaluate measures of interest about qualities of the system, e.g. about performance or dependability.
– The approach is first developed in a Markovian setting with exponentially distributed durations, using the formalism of Generalized Stochastic Petri Nets (GSPN), whose underlying stochastic process falls in the class of Continuous Time Markov Chains (CTMC) and Discrete Time Markov Chains (DTMC);
– … and it is then extended to a non-Markovian setting, where models also include generally distributed durations and thus identify an underlying Markov Regenerative Process (MRP);
– … with a balance between ground and advanced concepts that will be adapted to fit the composition of the class.
– The Oris tool and its Sirio API (https://www.oris-tool.org/) will be presented, reporting on cases of application and providing means for hands-on experience in modeling and evaluation.
—- Teacher
Enrico Vicario (https://stlab.dinfo.unifi.it/
Full Professor of Computer Science at the University of Florence, Italy.
He holds a Ph.D. in Informatics and Telecommunications Engineering
received from the University of Florence in 1994. He works in the area
of Software Engineering, with focus on applications and methods of
quantitative evaluations and on software architectures and methodologies.
—- Lecture dates (Italian Time – CET):
4 lectures of 3 hours each, in video streaming:
Wednesday 9 September, 15:30 – 18:30
Friday 11 September, 15:30 – 18:30
Wednesday 16 September, 15:30 – 18:30
Friday 18 September, 15:30 – 18:30
- Course teacher: Giuliano Casale (Imperial College)
- Course title: Advanced software system modelling
- Specially suited for Curricula: INF
- Proposed teaching period: Sep 23, 2020-Oct 14, 2020
- CFU’s: 3 (4 hours)
Advanced software system modelling
This doctoral course will present advanced system modelling techniques
in use to evaluate performance and scalability in software systems and
services. The module will alternate theoretical foundations, case
studies, and hands-on experience. The syllabus touches upon three main
topics: (1) stochastic modelling of software systems and service
workflows; (2) model evaluation techniques; (3) learning model
parameters based on production data. The module will include demos and
hands-on exercises with Java Modelling Tools (http://jmt.sf.net/), a
family of graphical tools for simulation and analytical evaluation of
complex system models. The module does not assume prior knowledge of
modelling and is open to PhD students from all technical backgrounds.
—- Teacher
Giuliano Casale joined the Department of Computing at Imperial College
London in 2010, where he is a Reader in modelling and simulation.
Previously, he worked as a scientist at SAP Research UK and as a
consultant in the capacity planning industry. He teaches and does
research in performance engineering and cloud computing, topics on
which he has published more than 100 refereed papers. He serves on the
editorial boards of IEEE TNSM and ACM TOMPECS and as current chair of
ACM SIGMETRICS. Homepage: https://wp.doc.ic.ac.uk/gcasal
—- Lecture dates (Italian Time – CET):
4 lectures of 3 hours each, in video streaming:
– Wednesday 23 September, 9.30-12.30
– Thursday 1 October, 9.30-12.30
– Wednesday 7 October, 9.30-12.30
– Wednesday 14 October, 9.30-12.30
AIDA course
Graph Neural Networks and Neural-Symbolic Computation
Description: This is an introductory course to the theory and applications of Graph Neural Networks (GNN) and to related topics in Neural-Symbolic Computation. The course gives the foundations on neural computation involving patterns represented by graphs in fields ranging from computer vision to bioinformatics. In addition, GNN will be presented for different applications in the case of graph-based domains, where inferential processes are expected to involve also the neighbors of vertexes (e.g. social networks). Finally, the diffusion mechanisms taking place by GNN will be integrated with more general Neural-Symbolic models where the decision mechanisms need to be coherent with external representations of environmental knowledge.
- 9:00 – 12:00, 2/4 – Neural computation on directed graphs, Diffusion
on graphs, GNN - 9:00 – 12:00, 2/11 – Convolution on graphs, lab experiments
- 9:00 – 12:00, 2/18 – Neuro-symbolic computation
- 9:00 – 11:00, 2/25 – Lab experiments
- 11:00 – 12:00, 2/25 – Seminar by Petar Veličković, Deep Mind
Institution: Université Côte d’Azur
Short Course
ECTS: N/A
Level: Master 2
Semester: Spring
Course start, Duration: Starts on Feb. 4, ends on Mar. 4, 5 weeks, Thursdays 9-12
Language: English
Participation mode: Zoom
Lecturers: Professor Marco Gori, MAASAI, Université Côté d’Azur and SAILab, University of Siena
Course assistance and seminars:
- Dr. Petar Veličković, Deep Mind
- Dr. Michelangelo Diligenti, Google and SAILab, University of Siena
- Dr. Giuseppe Marra, KU Leuven
- Matteo Tiezzi, SAILab, University of Siena
For registration, please contact Lucile Sassatelli lucile.sassatelli@univ-cotedazur.fr
Link to course: http://web.univ-cotedazur.fr/en/idex/formations-idex/data-science/
Registration: Interested students should enter the course webpage to register.
Machine Learning and Deep Neural Networks
Description: Introduction to Machine Learning, Artificial Neural Networks, Perceptron, Multilayer perceptron. Backpropagation, Deep neural networks. Convolutional NNs, Deep learning for object detection, Deep Semantic Image Segmentation, Generative Adversarial Networks, Recurrent Neural Networks. LSTMs, Data Clustering, Decision Surfaces. Support Vector Machines, Distance-based Classification, Dimensionality Reduction, Kernel Methods, Bayesian Learning, Deep Reinforcement Learning, CVML Software Development Tools
Institution: Aristotle University of Thessaloniki
Department: Department of Informatics
Short Course
ECTS: 1.5
Level: MSc/Senior undergraduate
Semester: Spring Semester
Start Day, Duration: 17/2/2021, 2 Days
Language: English
Participation mode: teleconference/tele-exams
Participation terms: See course website
Lecturer: Prof. Ioannis Pitas, pitas@csd.auth.gr
Link to course: https://icarus.csd.auth.gr/cvml-short-course-machine-learning-and-deep-neural-networks/
AIDA course:
Computer Vision and Image Processing
Description:
- Image Processing: Introduction to Image Processing and Computer Vision, Image Formation, Image Sampling, 2D Systems, Image Transforms, Fast 2D Convolution Algorithms, Image Perception, Image Filtering.
- Computer Vision: Edge Detection, Region Segmentation, Texture Description, Shape Description, Image Acquisition, Camera Geometry, Stereo and Multiview Imaging, Structure from Motion, 3D Robot Localization and Mapping, Object Tracking.
Institution: Aristotle University of Thessaloniki
Department: Department of Informatics
Short Course
ECTS: 1.5
Level: MSc/Senior undergraduate
Semester: Spring Semester
Start Day, Duration: 24/2/2021, 2 Days
Language: English
Participation mode: teleconference/tele-exams
Participation terms: See course website
Lecturer: Prof. Ioannis Pitas, pitas@csd.auth.gr
Link to course: https://icarus.csd.auth.gr/cvml-short-course-computer-vision-image-processing/
Soft skill courses are available from the official page of the PhD programs at Unifi
We remind that it is necessary that every students gets CFU’s from these courses
Additional PhD courses, to be approved by the PhD Committee
Geometric Deep Learning, September 8-9, 2021
| Instructor | Stefano Berretti and Pietro Pala (Università di Firenze) |
| Location | Online |
| ECTS Credits | 3 |
Abstract
Research on Deep Learning techniques has mainly focused so far on data defined on Euclidean domains (i.e., grids). However, in a multitude of different fields, such as Biology, Physics, Network Science, Recommender Systems and Computer Graphics, one may have to deal with data defined on non-Euclidean domains (i.e., graphs and manifolds). The adoption of Deep Learning in these fields has been lagging until very recently, primarily since the non-Euclidean nature of data makes the definition of basic operations (such as convolution) rather elusive. Geometric Deep Learning deals with the extension of DL techniques to graph/manifold structured data. In this course, we will introduce this area of research by presenting recent research advancements in DL applied to point clouds, graphs, meshes and manifolds. Example applications will be illustrated in laboratory sessions.
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Stochastic Model Checking, September 13-17, 2021. Prof. Mieke Massink
| Instructor | Mieke Massink (CNR-ISTI) |
| Location | Online (contact profMassink for the url of the meeting) |
| ECTS Credits | 3 |
Abstract
The course introduces the use of formal methods for the specification and verification of quantitative properties of concurrent distributed systems. In particular we look at the foundations of such techniques among which various kinds of Markov chains and related probabilistic and stochastic process algebras for the modelling of systems, and to probabilistic and stochastic temporal logics for the specification of properties. Furthermore, the basic probabilistic and stochastic model-checking algorithms will be illustrated. Hands-on experience will be provided by performing smaller case studies in modelling and verification using the state-of-the-art PRISM model checker. Some lectures will be dedicated to illustrate more advanced recent developments and perspectives for future research in this field in particular in relation to the modelling and verification of smart and collective systems.