PhD courses
NB: this page undergoes continuous modifications and additions. Please check it frequently and/or subscribe to the official PhD Calendar
Alberto Tesi
Date: November 25, 27
25 Nov 14,15 room S12 Santa Marta
27 Nov 9:00 room “Caminetto” Santa Marta
Hours/CFU’s: 8/2
Curriculum: Control, Optimization and Complex Systems
more details in email
Carlo Carobbi
Date: 5/12/24; 9-13 am
Hours/CFU’s: 4/1
Room: 177 Santa Marta
on line: https://meet.google.com/rmm-uaoa-vph
Curriculum: Electronics, Electromagnetics and Electrical Systems
Abstract:
Technical standards frequently provide support to, if not the basis of, academic studies and research in all the fields of engineering. Many, probably the majority, of the thesis works, PhD dissertations and scientific papers in engineering take advantage, inspiration or are even devoted to the content of specific technical standards. Nonetheless, few of the authors of such works are aware about the process leading to the writing and publication of a technical standard, its maintenance, the context in which technical standards are used by industry and referred to by regulations issued by national and regional authorities. Therefore, generally the outcomes of these studies have no impact on standards’ development. There are many reasons for this, such as academic research results are not brought to the attention of standard organizations, or do not address the actual needs of users of technical standards. The scope of this presentation is to provide information about the work of standard organizations and the context in which technical standards are applied, particularly in the electrical and information engineering sector.
Laura Carnevali
Date: 09,11,16,18 December, Santa Marta
Hours/CFU’s: 12/3
Date/Room/Time:
09/12/24, Santa Marta, aula 049, 10:15-13:15
11/12/24, Santa Marta, aula 035, 10:15-13:15
16/12/24, Santa Marta, aula 173, 09:00-12:00
18/12/24, Santa Marta, aula 173, 09:00-12:00
Curriculum: Informatics
Abstract: This course introduces the fundamentals of Markov Decision Processes (MDPs), a powerful framework for modeling of sequential decision problems under uncertainty. Topics include (but are not limited to): syntax and semantics of MDPs, adversaries, probabilistic reachability of MDPs, applications of MDPs using the PRISM tool.
Simone Magistri
Date/room:
08/01/2025, 14:00 – 17:00, Aula 49, Santa marta.
10/01/2025, 14:00 – 17:00 , Aula 49, Santa Marta.
13/01/2025, 14:00 – 17:00, Aula 175, Santa marta.
17/01/2025, 14:00 – 17:00, Aula 49, Santa marta.
Hours/CFU’s: 12/3
Curriculum: Computer Engineering
Abstract: The widespread adoption of Artificial Intelligence in real-world applications has been driven by the ability of Deep Neural Networks (DNNs) to solve increasingly complex problems in various fields, such as computer vision and natural language processing. However, the current DNN learning paradigm is static: DNNs are typically trained for single tasks with fixed datasets, making them rigid and prone to becoming outdated as real-world data and tasks evolve, particularly in sectors like transportation, healthcare, and robotics. Updating models often requires costly re-training on both old and new data, which is computationally and environmentally expensive due to the large number of parameters and the demanding training procedures involved. Additionally, privacy concerns may prevent access to old data, making re-training unfeasible. Naively updating models solely on new tasks leads to catastrophic forgetting—where models lose prior knowledge when learning new information. Continual Learning, also known as Incremental Learning, is a field of machine learning aimed at developing adaptive learning paradigms for DNNs to continuously learn new tasks while preserving previous knowledge. This course explores the fundamentals of continual learning, including continual learning scenarios such as Domain Incremental, Class Incremental, and Task Incremental, as well as regularization techniques on state-of-the-art models like Convolutional Neural Networks (CNNs) and Transformers. We will also examine recent breakthroughs, open challenges, and research directions for advancing this field.
The goal of the course is to introduce the concept of Physics Informed Deep Neural Networks (PINN), discuss its implementation from scratch in PyTorch and use advanced ad-hoc developed open-source libraries such as Nvidia-modulus for addressing real-world problems in various fields (engineering, physics, oil). We discuss recent topics such as Mixture-of-Models, Neural Operators, Physics-Informed Kolmogorov-Arnold Networks and Physics-Informed Computer Vision.
– Duration of Course: 16 hrs/4CFU (for Smart Computing, Ingegneria
Where & When:
Plesso Didattico Morgagni – viale Morgagni, 44-48, 50134 Firenze (FI)
– aula 217, Tuesday 14/01, 9:30 – 11:30
– aula 219, Wednesday 15/01, 9:30 – 12:30
– aula 219, Thursday 16/01, 9:30 – 12:30
– aula 219, Monday 20/01, 9:30 – 12:30
– aula 219, Wednesday 22/01, 9:30 – 12:30
– aula 219, Friday 24/01, 9:30 – 11:30
The course will encompass:
1. An in-depth study of differential equations and their numerical resolution using PINN.
2. An exploration of the theory and applications of Physics-Informed Neural Networks to many fields (Engineering, Physics, Medicine, Oil Industry, etc).
3. Practical sessions involving real-world applications.
Each lecture will comprise a part about the theory and a half of hands-on practice over Python Scripts and notebooks.
Either the lecture slide and the lecture code will be shared, and something is already available on the course page. For more info about the course, see
https://androbomb.github.io/teaching/
https://forms.gle/waufiF6kEwTRsveK9
2. Join the Discord channel as soon as possible.
Should you require any further information or clarification, please do not hesitate to mail me at
bombini__AT__fi.infn.it
CV: INF
Hours/CFU: 12 hours /3 CFU
Time: 9.30 : 12.30
For other details see:
Lorenzo Mucchi
Stefano Caputo
Title: Fundamentals of Physical Layer Security
Curriculum: Telecommunications and Telematics
Hours: 8 / 2 CFU
Dates: 28-29 GEN 9.30-13.30
Room: 035 Santa Marta
Abstract: Physical layer security (PLS) is a novel paradigm that aims to achieve secure wireless communications by exploiting the physical properties of the transmission channels, such as noise, interference, and fading. Unlike conventional cryptographic methods, which rely on mathematical algorithms and secret keys, PLS uses signal design and processing techniques to degrade the signal quality of the eavesdroppers and realize keyless secure transmission. PLS offers several advantages, such as avoiding the difficulties in key distribution and management, providing flexible security levels through adaptive transmission design, and leveraging the features of next-generation wireless networks, such as spatial diversity, cooperation, and cognition. This PhD course provides an overview of the main techniques of PLS and illustrates the applications of PLS in 5G and 6G networks, highlighting the challenges and opportunities of physical layer security in the contemporary landscape.
Luigi Chisci
Note: students are invited to register Here
Date: 10-18 / 2 /2025
Hours/CFU’s: 20/5
Date/Time:
10/2/2025 Lunedi ore 9-13
11/2/2025 Martedi ore 9-13
13/2/2025 Giovedi ore 9-13
17/2/2025 Lunedi ore 9-13
18/2/2025 Martedi ore 9-13
Curriculum: Control, Optimization and Complex Systems
Abstract: This course aims to provide both theoretical and practical tools to tackle estimation problems encountered in several areas of engineering and science. In particular, it is shown how to formulate such estimation problems as instances of a general dynamical system state estimation problem and how to derive the mathematical solution of the latter problem. Then it is shown that, for a linear Gaussian system, such a solution yields the well known Kalman filter. Further, approximate techniques (e.g. extended and unscented Kalman filters, particle filter, etc.) are presented for the case of nonlinear and/or non-Gaussian systems, for which an exact closed-form solution cannot be found. To conclude the theoretical part, theoretical limitations (i.e. the Cramer-Rao lower bound) on the quality of estimation are discussed. In the second part of the course, we illustrate some applications of linear/nonlinear Kalman filtering (e.g., tracking, robotic navigation, environmental data assimilation).
Andrea Tani
Date:
20 Feb. 2025 9.30 – 13.30 room 171 SM
21 Feb. 2025 14.00 – 18:00 room 171 SM
CFU: 2
CV: TLC
room: 171
Abstract:
The ever-increasing demand for bandwidth in wireless communication systems, driven by multimedia and Internet of Things (IoT) applications, has led to a significant shortage of available spectrum resources as reported by the U.S. Federal Communications Commission, the underutilization of licensed spectrum due to regulated access has proven to be “a more significant problem than the physical scarcity of spectrum. This observation has highlighted the need to move away from static resource allocation strategies and has driven intense research into dynamic spectrum sharing among systems with heterogeneous radio access technologies and different priorities for access to licensed and unlicensed bands. These research efforts have culminated in the development of cognitive radio (CR) technology, which seeks to exploit underutilized spectral resources in the frequency, time, and space domains by reusing them opportunistically. Notably, dynamic spectrum sharing and management have been recognized as potential enablers for 6G, falling under the category of “new spectrum.” At the core of cognitive radio lies spectrum sensing, tasked with determining whether a particular portion of the spectrum is “available” or not. In other words, the goal is to discriminate between two mutually exclusive hypotheses. Following a brief introduction to the application scenarios of the CR paradigm, the first part of the course provides the theoretical foundation of spectrum sensing within the framework of detection theory, with a particular focus on blind techniques. The second part of the course addresses the challenges associated with applying blind spectrum sensing techniques in the context of in-band full-duplex technology, as well as their operation at millimeter-wave and terahertz frequencies, both of which are envisioned as potential 6G enablers. Finally, we present the application of spectrum sensing in the integration of unmanned aerial vehicles UAVs into 5G/6G networks, addressing challenges such as dynamic spectrum access in multi-UAV scenarios
Stefano Caputo
Date: Gen-Feb 2025
Time/Room: TBD
CV: TLC
CFU: 3
Abstract: The course will give to the students the fundamentals of visible light communications (VLC). VLC refers to a data communications medium using visible light between 400 THz (780 nm) and 800 THz (375 nm). Low-cost wireless communication network can be created using VLC, often known as Li-Fi systems. LiFi is a potential solution to the shortage of global wireless radio spectrum. The light can be used as a communication medium for ubiquitous computing, because light-producing devices (such as indoor/outdoor lamps, TVs, traffic signs, commercial displays, car headlights/taillights, etc.) are used everywhere. The course will provide a general background on LiFi technology, discussing the major advantages and existing challenges to technology integration in 6G mobile Networks. Recent key advancements in physical layer techniques, such as localization, ultra-high-speed indoor connection, ultra-low latency outdoor link, will be discussed.
Pierluigi Mansueto
Date: Feb 2025
Hours/CFU’s: 8/2
Date/Time: NA
Curriculum: Control, Optimization and Complex Systems
Abstract: Clustering is one of the most extensively studied problems in unsupervised learning, as highlighted by various surveys and books. Its aim is to organize a collection of elements into coherent groups called clusters: similar elements should be assigned to the same cluster while different elements should be in different clusters. This course concerns the analysis of the main models and approaches for clustering problems, with a particular focus on one of the most recognized formulation, i.e., the Minimum Sum-of-Squares Clustering (MSSC). In addition to the well-known K-MEANS, we will also examine: methodologies that do not require the number of clusters (k) a priori; novel meta-heuristics to improve the K-MEANS performance; one of the (relatively) new frontiers for this topic, that is, semi-supervised clustering.
Franco Bagnoli
Date: Feb-Mar 2025
Hours/CFU’s: 8/2
Date/Time: NA
Curriculum: Control, Optimization and Complex Systems
Abstract: Cellular automata are fully discrete systems and are used as simple models in many context, from physics to biology to computer science. They can be defined in a deterministic way, anche thus be studied as dynamical systems, extending the notions of chaos, for instance, or in a probabilistic way furnishing many examples of phase transition. The two concepts can be mixed, for instance by studying the effect of a small noise. One of the recent fields of study concerns the problem of controlling such systems, and since they are highly non-linear, standard techniques cannot be used and custom ones have to be developed.
1) Introduction to discrete modeling and simulations
2) Probabilistic cellular automata and phase transitions
3) Deterministic cellular automata, attractors
4) Control, generalizations and extensions
Franco Bagnoli
Date: Mar-Apr 2025
Hours/CFU’s: 12/2
Date/Time: NA
Curriculum: Soft Skills
Abstract: We shall review the evolution of computers and their applications, from the first mainframes dedicated to computing, to the evolution in the business world, the birth of the Internet, the switch to personal computers, and finally to the internet & microprocessor world of today. In parallel, we shall examine the development of operating systems and computer languages, the social impact and its driving force, the connections with literature and science fiction.
Carlo Odoardi, Lorenzo Capineri
Date: 9,26 Apr 2025
Hours/CFU’s: 6/1
Curriculum: Soft Skills
Abstract:
Part 1. Strategic Human Resource Management for Innovation Prof. Carlo Odoardi
The strategic management of the human capital present in an organization falls within the framework defined as “organizational innovation” which can be defined as the connection of people to organizational objectives and goals in order to improve performance and develop a culture aimed at enhancing skills and professionalism to support innovation, flexibility and competitive advantage in continuous collective or organizational learning. An organization focused on the enhancement of people must necessarily equip itself with a series of new systems and measurement methods to verify and monitor the innovative variables necessary to determine specific professional behaviours at an individual, team and organizational or managerial level in an integrated vision. Today, research and experimentation carried out over the last twenty years in work contexts has highlighted the strategic importance of the interaction and integration between “organizational innovation and technological innovation”. The field of intervention research has produced a series of models, methods and tools for the development of organizations in an innovative way and above all to outline new organizational models (agile organizations or smart organizations) and management models (new managerial and leadership models for innovation) where the system of professional relational networks are central to dealing with continuous metamorphoses.
Part 2. The complexity of the role of the future engineer Prof. Lorenzo Capineri
This course focuses on the complexity of STEM education, especially for engineering courses, considering the present and future role of these professionals in society. The high specialization of technologies has required vertical education in engineering topics, while the role of engineers of the future will be broader to solve societal problems at a global level. The ability to fill this role can be improved by stimulating the education of engineers with humanistic and social topics, multicultural teamwork and preparation to stimulate creativity and innovation within a laboratory community. In this path we find the relevance of Adriano Olivetti training process. This course reports some experiences and methodologies of innovative teaching that can be useful for the design of university teaching courses in STEM subjects”. Contributions from large industry managers is foreseen.
*: PhD course proposal, still to be approved by the PhD committee
PhD Seminars
Normally 1 CFU
Dr. Jannis Kurtz Amsterdam University
Date 6 Feb. 2025
Time 14:30
room 175 Santa Marta
Abstract: In recent years, there has been a rising demand for transparent and explainable machine learning (ML) models. A large stream of works focuses on algorithmic methods to derive so called counterfactual explanations (CE). Although significant progress has been made in generating CEs for ML models, this topic has received minimal attention in the Operations Research (OR) community. However, algorithmic decisions in OR are made by complex algorithms which cannot be considered to be explainable or transparent. In this work we argue that there exist many OR applications where counterfactual explanations are needed and useful. In the first part of the talk, we translate the concept of CEs into the world of linear optimization problems and define three different classes of CEs: strong, weak and relative counterfactual explanations. For all three types we derive problem formulations and analyze the structure. We show that the weak and strong CE formulations have some undesirable properties while relative CEs can be derived by solving a tractable convex optimization problem. We test all concepts on a real-world diet problem and we show that relative CEs can be calculated efficiently on NETLIB instances. In the second part of the talk, we analyze CEs for binary knapsack problems, making a first step towards general integer problems. For special cases of the problem, we present solution methods which perform well in preliminary computational experiments.
Soft Skill
CFU 1
Profili dei ricercatori e Valutazione della ricerca
Partecipate al nostro webinar informativo su “Profili dei Ricercatori e Valutazione della Ricerca,” dove esploreremo gli strumenti e le strategie essenziali per costruire e migliorare i profili dei ricercatori. Questa sessione mira a fornire ai partecipanti le conoscenze necessarie per dimostrare e descrivere efficacemente l’impatto del lavoro di un autore.
Obiettivi Formativi:
-Costruire il proprio profilo ricercatore
-Dimostrare e descrivere i dati dell’impatto di un autore
Questo webinar è progettato per ricercatori, accademici e professionisti interessati a comprendere come sfruttare Web Of Science e InCites per la valutazione della ricerca e la costruzione del profilo.
Non perdere questa opportunità per migliorare le competenze e conoscenze nella valutazione dell’impatto della ricerca.
Data / Ora: 16 Gennaio 2025 11:00-12:00
Link di registrazione
https://clarivate.com/academia-government/events/profili-dei-ricercatori-e-valutazione-della-ricerca/
13 Dicembre 2024, ore 9:00 – 16:30
Scuola di Ingegneria, Università di Firenze
Aula Caminetto – Salone di Villa Cristina
La Giornata affronta la continuità tra didattica, ricerca e trasferimento tecnologico, incardinati nella Scuola di Ingegneria e nel Dipartimento di Ingegneria dell’Informazione, con l’industria elettronica dell’area fiorentina. Dai contributi industriali emergerà la figura professionale dei futuri ingegneri elettronici. Alla giornata parteciperanno le seguenti aziende:
Leonardo SpA; Eldes Srl; Microtest SpA; Saitec Srl; Powersoft SpA; Promel Srl; Pasquali Microwave Systems Srl; Advance Microwave Engineering Srl; Ampatech Srl; Pramatech Srl; SSE Srl; ABB e-mobility SpA.
E’ necessaria l’iscrizione: https://forms.gle/zL74NT9SEPEpJznq8
In chiusura verranno consegnati i premi di laurea attribuiti agli studenti della laurea magistrale in Ingegneria dei Sistemi Elettronici.
Hervè A. Corti, Introduction to Quantum Computatione Title
Room 051 Santa Marta
Time 10.15
Date: 11/12/24
Lavinia Amorosi
Title: A Mathematical Programming Approach to Hierarchical Clustering
date/place: December 3rd 2024, 11:30 a.m., Room 049 Santa Marta
abstract: Hierarchical clustering is a statistical technique to study the occurring groups (clusters) within a dataset creating a hierarchy of clusters. This is represented by a rooted tree (dendrogram) whose leaves correspond to the data points, and each internal node represents the cluster containing its descendant leaves. Among methods to perform hierarchical clustering, the agglomerative ones are based on greedy procedures that return a sequence of nested partitions where each level up joins two clusters of the lower partition relying on a local criterion.
In this talk, motivated by the lack of exact approaches that guarantee global optimality, we focus on a unified mathematical programming formalization for single and complete linkage procedures. We evaluate, according to different measures commonly used in this context, the dendrograms obtained from the exact resolution of the formulations and those produced by the greedy approach.
Furthermore, by exploiting the mathematical formulation, we also present a scalable matheuristic algorithm capable of generating better quality dendrograms than those produced by the greedy approach even for large size datasets.
Date/time: 26 Nov. 16:00
Room: 035 Santa Marta
Title: Image matching: vecchie glorie e nuovi orizzonti
Abstract. L’image matching (calcolo di corrispondenze) è alla base della maggior parte dei metodi di ricostruzione 3D e di stima di posa in computer vision, con applicazioni notevoli quali SfM (Structure from Motion), Visual SLAM (Simultaneous Localization and Mapping) e più recentemente NeRF (Neural Radiance Fields) and GS (Gaussian Splatting). Dopo aver introdotto le fasi principali del processo di image matching, verrà presentata l’evoluzione di tali approcci fino allo stato dell’arte attuale, mostrando i pregi e i difetti delle tecniche più rilevanti (alcune della quali basate su deep learning) e le criticità connesse ai metodi di valutazione e ai dataset impiegati. Infine verranno discusse semplici strategie generali basate sull’utilizzo di piani virtuali per il miglioramento di metodi di image matching.
E. Planas
2024, 13th Dic, 9:00 a.m., On line at link:
https://us02web.zoom.us/j/87052041160?pwd=WvmfWSNWSI48NWGhXXjfaboCZ88GOB.1
Meeting ID: 870 5204 1160
Passcode: 0uARAZ
Abstract:
This seminar introduces the National Institute of Informatics (NII) andopportunities for international cooperation with NII. NII is the Inter-University Research Institute (ROIS) for advanced ComputerScience Research and Global Science Data Infrastructure support, situated inthe heart of Tokyo, Japan. NII is ranked top 2 for publications in ComputerScience in Japan. Research activity is divided in four Departments:
1.Principles of Informatics;
2.Information Systems Architecture Science;
3.Digital Content and Media Sciences;
4.Information and Society.
The Global Liaison Offi ce (GLO) at NII has established partnerships with more than 100 MOU partners. GLO supports international exchanges via the“MOU Grant Program” under which a NII researcher can invite a colleaguefrom overseas for a stay, or send some member of his team for a researchvisit (up to 2 months, around 40 researches a year supported). GLO alsooff ers the “NII International Internship Program” in which we welcome andfund MOU partners Master’s and PhD. students for a research stay in one ofour teams (3 to 6 months, around 140 students per year).
A smart city represents an improvement of today’s cities both functionally and structurally, that strategically utilizes many smart factors, such as information and communications technology (ICT), to increase the city’s sustainable growth and strengthen city functions, while ensuring citizens’ quality of life and health. Cities can be viewed as a microcosm of “objects” with which citizens interact daily: street furniture, public buildings, transportation, monuments, public lighting and much more. Moreover, a continuous monitoring of a city’s status occurs through sensors and processors applied within the real-world infrastructure. Industrial sites represent similar scenarios, where data collected from distributed objects
allow to actuate powerful control strategies.
The Internet of Things (IoT) concept imagines all these objects being “smart”, connected to the Internet, and able to communicate with each other and with the external environment, interacting and sharing data and information. Each object in the IoT can be both the collector and distributor of information regarding
mobility, energy consumption, air pollution, as well as potentially offering cultural and tourist information.
As a consequence, cyber and real worlds are strongly linked in a smart city, such as in industrial site. New services can be deployed when needed and evaluation mechanisms will be set up to assess the health and success of the system under control. This talk will present some innovative developments in areas related to smart cities and smart industries, leveraging on the features supported by network intelligence at the edge of the network.
Soft Skills
Courses and seminars for Soft Skills organised out from Department of Information Engineering
1 CFU per 6 hours of course
BiblioTech, Bibliographic Search for Engineering PhD Students
Dispensed from Technology area Library
Courses list from “Student Learning” Florence University
Dispensed from Florence University
Courses list for Ph.D. students from Florence University
Dispensed from Florence University