PhD Courses and Seminars 2023-24

PhD courses

NB: this page undergoes continuous modifications and additions. Please check it frequently and/or subscribe to the official PhD Calendar

Alberto Gallegos Ramonet

“Untangling IoT technologies and uses within network simulations”

Curriculum:  Telecommunications and Telematics

Hours / CFU’s: 4 / 1

DAte: April 18, 2024

Abstract

The course will show how to use the ns-3 to develop complex simulation scenarios involving real devices (802.15.4 usb dongles) and emulated devices.
The course is aimed mainly at telecommunications, and computer engineering students.

Francesco Grasso

Title: Smart Hybrid AC/DC Microgrids

Curriculum: Electronics, Electromagnetics and Electrical Systems

Hours: 12 / 3 CFU Proposed dates: aprile-giugno 2024

Abstract: Smart grids are becoming as the next generation power systems, which encompass interconnected microgrids with high penetration of renewable generations and energy storage.
Hybrid AC/DC microgrids are the most likely future microgrid structure since DC subgrids feature high efficiency, better power quality, better current carrying capacity, and faster response.
DC subgrids can also be easily interfaced to the century-long AC utility grid.
During the past decade, smart hybrid AC/DC microgrids have received much attention and experienced significant development. However, as an emerging concept for the future grid structure, their technical details have not been understood very well. For example, with the high percentage of renewable energy and energy storage as well as the wide adoption of interfacing power electronics, there are great challenges in power management among the different elements in a microgrid within a very short time.
Power management, power converter control, integration of renewable generation
and energy storage, cybersecurity, communication technologies, and power quality are critical for the sound operation of a microgrid.
This course introduces the above topics for smart hybrid AC/DC microgrids and presents some effective solutions.”

Franco Bagnoli

Title: Cellular automata: Phase transition, chaos, synchronization, control

Curriculum: Control, Optimization and Complex Systems

Hours: 8 / 2 CFU

Proposed dates: May-june-july

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 concept can be mixed, for instance by studying the effect of a small noise. One of the recent field of study is that of controlling such systems, which are highly non-linear and this standard techniques cannot be used.

Pierluigi Mansueto

Continuous Multi-Objective Optimization

Date: April 2024

Curriculum: AOSC

Hours / CFU’s: 12 / 3

Abstract:

The course is concerned with continuous multi-objective optimization (MOO) problems with various constraint types. In particular, we will mainly consider the unconstrained, the box constrained and the general convex constrained settings. As a mathematical tool, MOO has received much attention over the years, being suitable both in operation research applications and in real-world problems where contrasting goals have to be taken into account. Having contrasting objective functions does not allow to find a solution where the functions are minimized simultaneously; in most of the cases, if not always, we must find one or multiple solutions representing different trade-offs of the goals, then giving the final solution choice to the user, based on their needs and application contexts. These solutions form the so-called Pareto Front; without regularity assumptions, its reconstruction is not a trivial task and, for this reason, many MOO algorithms raised in the last years, aiming to reconstruct it in the most possible accurate way.

The first part of the course regards a general overview of the main MOO concepts, focusing on the notions typically employed in gradient-based MOO approaches, i.e., approaches that use first-order information of the involved objective functions. In this context, the “classic” MOO approaches will be introduced. In the second part, we will review the most modern part of the MOO literature, analyzing the newest MOO algorithms. Unlike the “classic” ones, they present particular mechanisms to handle multiple solutions at each iteration; in brief, they are particularly designed to reconstruct the Pareto Front. In addition to being described from a theoretical perspective, the presented algorithms will be also commented from an experimental point of view.

 

Franco Bagnoli

Title: History of computers: from Babbage to ChatGPT (hardware, software, socialware)

Curriculum: all/soft skills (to be approved)

Hours: 12 / 2 Soft Skills CFU’s yet to be determined

Proposed dates: may/june

Abstract: We shall review the evolution of computers and their applications, from the first mainframes dedicated to computing, to the evolution in business world, the birth of 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 drive, the connections with literature and science fiction.

dr Pasquale Imputato (Univ. Napoli Federico II)

Design and Evaluation of Wi-Fi Networks through Simulation

Curriculum: INF, TLC

Hours / CFU’s: 12 / 3

Abstract: This course shows how the design and evaluation of wireless networks, specifically Wi-Fi networks, can be conducted through simulation. Given the complexity of Wi-Fi systems, effective resource planning and a proper evaluation of optimization strategies are crucial to meeting varying traffic demands. The course begins by reviewing Wi-Fi fundamentals, covering recent advancements discussed within the context of the 802.11 Working Group. Moving forward, we delve into the tools that support design and evaluation, focusing on the fundamentals of discrete event network simulation and ns-3 as a versatile simulator/emulator for modeling wireless networks. Furthermore, we will review the integration of ns-3 with Artificial Intelligence (AI) tools. Throughout the course, we will progress from simulating Wi-Fi networks to exploring advanced features like Multi-Link Operation (MLO) or Multi-AP in ns-3. The journey includes simulation execution, results data analysis, and concludes with takeaways regarding the network performance and the space for optimization.

 

Matteo Lapucci

Recent Advances in Nonconvex Optimization for Machine Learning

Curriculum: AOSC

Hours / CFU’2: 12h / 3 CFU

Proposed date: May 2024

Abstract:

The task of training deep machine learning models requires solving difficult nonlinear and nonconvex optimization problems. The massive push to develop intelligent automated systems has led researchers in AI and optimization to quickly devise algorithms that actually allow to handle such difficult problems. Yet, the empirical effectiveness and efficiency of these methodologies have not been fully understood for a while.

Fortunately, the gap between empirical evidence and theoretical foundations in this field is progressively narrowing. On one hand, the introduction of classical optimization analysis tools has allowed the identification and formalization of the main reasons why stochastic gradient methods work well for deep learning problems. Secondly, the experimentation of classical nonlinear optimization techniques in this context shows that there is a concrete possibility of improving state-of-the-art algorithms, making them more robust and faster.

In the course, we will:
– recall the main concepts and the basic algorithms in nonlinear optimization;
– review the state-of-the-art methods for finite-sum problems in deep learning;
– provide an overview of recent research on optimizers for deep learning. Topics to be discussed include:
– stochastic line searches;
– adaptive momentum terms;
– convergence and complexity results under PL and over-parametrized regime assumptions;
– characteristics of LLM training problems.

Marco Fanfani, Stefano Bilotta

From Data to Digital Twin for Smart Mobility

Curriculum: Computer Engineering

Hours / CFU’s: 12/3

Period: end of May /June


Abstract:
Nowadays a huge portion of the population lives in urban areas, and projections indicate that most of the cities are going to confront a growing urban population in the next few years. This undoubtedly poses new challenges, particularly in the mobility sector: public transport planning, traffic congestion, parking availability, and produced pollution, are some exemplar problems that city councils and stakeholders must address to guarantee citizens a high quality of life. To respond to such issues, this course will present data kinds and standards to gather mobility-related information, analytic methods to perform reconstructions and predictions, interfaces to present results and insights, as well as system architectures, to arrive at the construction of a full urban digital twin.

Franco Scarselli, Pietro Bongini

Graph Neural Networks

Curriculum: INF

Date: 2024/06/11-20

Location: Dipartimento Ingegneria dell’Informazione e Scienze Matematiche, University of Siena

Hours/CFU’s: 16 / 4 CFU


Abstract

Graphs, which can represent patterns along their relationships, are used in several application domains, including, f.i., bioinformatics, physics, chemistry, and social network analysis. Graph Neural Networks (GNNs) are a large class of machine learning models designed to be directly employed on graph data without any preprocessing. This course aims to present modern GNN models, along with the related theory, some applications and the perspective. First, we will introduce the main concepts behind machine learning for graphs and most common models based on neural networks, f.i., GCNs, GraphSAGE, GAT. Moreover, main theoretical results, which describe the expressive power and generalization capability of GNNs, will be presented. Later, we will examine more advanced models and we will examine the basic building modules that are used to design the recent architectures. A particular focus will be dedicated graph generation based on GNNs. Some applications will also be discussed in order to clarify the possible uses of GNNs.

Lorenzo Mucchi

Title: Fundamentals of Physical Layer Security

Curriculum: Telecommunications and Telematics

Hours: 8 / 2 CFU

Proposed dates: Jun/Jul 2024

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.

Agnese Mazzinghi, Angelo Freni

Title: RFID Theory, Methods, Applications, and Issues

Curriculum: Electronics, Electromagnetics and Electrical Systems

Hours: 16 / 4 CFU

Proposed dates: June 2023

Abstract: Radio Frequency IDentification (RFID) is a “wireless automatic identification and data capture (AIDC)” technology.
The technology is considered the core of the so-called “Internet of Things”, which refers to
the “possibility of discovering information about a tagged object by browsing an Internet address or database entry that corresponds to a particular RFID.” The area of applications for radio frequency identification (RFID) is increasing rapidly. Applications include supply chain management, access control to buildings, public transport payment, automotive security, airport baggage, express parcel logistics, automated libraries, healthcare,
livestock identification, and many more. The need for high volume, low cost, small size, and large data rate is increasing, while stringent regulations of transmit power and bandwidth have to be met.
Starting from the physical principles of RFID systems, a comprehensive overview of the various technologies, frequency ranges, and radio licensing regulations will be explored. Then, a detailed analysis of the near-field and far-field coupling between the reader and the tag, as well as the coding and modulation schemes, will be considered. The course includes a lab activity for the design, numerical analysis, optimization, manufacturing, and testing of an RFID UHF antenna tag.

Andrea Tani

UAV Communications

Curriculum: Telecommunications and Telematics

4 hours / 1 CFU

June 2024 

Abstract: Nowadays, Unmanned Aerial Vehicles (UAVs), also commonly known as drones have discovered a wide range of applications in various domains, including aerial inspections, photography, precision agriculture, traffic management, search and rescue operations, package delivery, and telecommunications, among others. To achieve ubiquitous and high-data-rate services in challenging scenarios, new-generation wireless networks must embrace innovative technologies. The integration of terrestrial and aerial nodes to create a vertical heterogeneous network offers flexible and reliable mobile communication infrastructure. Deploying UAVs as aerial base stations in wireless communication
systems promises cost-effective connectivity, particularly in areas without existing infrastructure coverage. Low-altitude UAVs outperform terrestrial communications in terms of deployment speed, flexibility, and quality of service, thanks to short-range line-of-sight links.
The first part of the course focuses on examining empirical models that characterize both Air-to-Air (AA) and Air-to-Ground (AG) propagation channels, considering various scenarios (Rural, Sub-Urban, Urban). It also provides an overview of UAV-aided wireless communications, addressing typical use cases like UAV-aided ubiquitous coverage and UAV-aided relaying. Special emphasis is placed on addressing the challenges related to trajectory planning and the deployment of UAV networks to achieve optimal wireless coverage.
The second part of the course deals with the UAV-to-UAV communications, UAV-Cellular
spectrum sharing, and provides insights into techniques for identifying and countering malicious jamming attacks.”

Stefano Caputo

Communication and Localization for Industrial Networks

Hours/CFU’s: 16 / 4

Dates: June 2024

Curriculum:  TLC

Abstract

Communication and localization are two essential functions for industrial networks, especially in the context of Industry 4.0 and beyond. This course will introduce the state-of-the-art techniques and challenges for integrating communication and localization in industrial networks.
The course will cover the following topics: Fundamentals of communication and localization, including signal models, beamforming, channel estimation, and ranging; joint communication and localization design, including resource allocation, network architecture, and performance analysis; localization and sensing applications, including smart manufacturing, asset tracking, and radar-communication coexistence; future directions and open problems, including 6G vision, integrated localization and sensing, and use of machine learning algorithms to support communication and localization services.
The course is based on theoretical lectures, and real world use cases, and will expose the students to the latest research and industrial developments in this field.

David Angeli

Title: Fundamentals of Economic Model Predictive Control

Curriculum: Control, Optimization and Complex Systems

Hours: 4 / 1 CFU

Proposed dates: July 2024

Abstract: Model Predictive Control (MPC) is an optimisation based technique for designing control actions both for linear and nonlinear systems subject to constraints. In this course we will first introduce traditional tracking MPC and then extend the approach to Economic MPC, which is focused on directly optimising the “economic” performance of a system through dynamic optimisation in receding horizon of an arbitrary cost functional. The impact on stability, feasibility and performance of such an approach will be analysed.

Gloria Gori

An overview on Statistical Model Checking 

Curriculum: Computer Engineering

Hours /CFU’s: 8 / 2

Date: September 2024

Abstract:
Statistical Model Checking (SMC) has emerged as a crucial technique to automatically verify the correctness of complex systems, particularly in safety-critical applications where it can be also useful to quantify the probability of event occurrence (e.g., system failure) to provide reliable forecasts of systems’ correct behavior.
SMC is a probabilistic verification method that assesses the correctness of system models by employing statistical techniques. Unlike traditional formal verification methods, SMC leverages statistical sampling to analyze the behavior of systems under uncertainty. By generating random or guided samples from the system’s state space, SMC can efficiently evaluate the likelihood of critical events and quantify the system’s performance.

In safety-critical applications such as railways, autonomous vehicles and aerospace systems, ensuring the reliability and safety of the system is paramount. Traditional verification techniques may struggle to handle the complexity and uncertainty inherent in these systems. SMC offers a practical solution by providing probabilistic guarantees of system behavior, allowing designers to identify potential issues and mitigate risks early in the development process.
The course will first recall the basic principles of model checking (algorithms and temporal logics) and of its use as a formal verification technique. It will then introduce the SMC technique and its capability to statistically address the state space exploration.

One of the key tools that implements SMC is UPPAAL SMC, an extension of the UPPAAL model checker tailored for statistical analysis. UPPAAL SMC enables users to model complex systems using timed automata, define probabilistic properties of interest, and perform statistical analysis to assess the system’s reliability.

Through hands-on exercises and practical examples, participants in this course will learn how to utilize UPPAAL SMC effectively for modeling and verifying safety-critical systems.

prof. Arkady Pikovskym, 
Univ. Potsdam  Control,

Dynamics of oscillators

curr: Optimization and Complex Systems
hours: 6 / 1.5 CFU

Dates: 6/11/2023, 10/11/2023, 13/11/2023 14:30-16:30

Lecture 1 – 11/06/2023 14:30-16:30 room 281 dept. Physics
Self-sustained oscillators
Phase and amplitude dynamics
Weak forcing and coupling; phase reduction; coupling functions
Synchronization phenomena
Inverse problems (inferring the phase dynamics and synchronization from observations)

Lecture 2 – 11/10/2023 14:30-16:30 room 212 dept. Physics
Large populations
Kuramoto model
Ott-Antonsen theory and its generalizations
Other type of synchrony: partial synchronization
Spatially distributed systems and chimera states

Lecture 3 – 11/13/2023 14:30-16:30 room 281 dept. Physics
Effects of noise
Noise-induced oscillations in excitable systems
Collective synchrony of noisy oscillators
synchronization by common noise

The course can be streamed (please contact franco.bagnoli@unifi.it if interested)

Prerequisites:

Basic knowledge about nonlinear dynamics would be helpful. I plan also to use Liouville equation (evolution of density) and Fokker-Planck equation (when treating noise). Probably knowing some elements of Lyapunov exponents would be good.”

Instructors: Roberto Verdecchia, UNIFI

Title: A pragmatic crash course on systematic literature reviews

Duration: 2h (CFU’s to be determined)

schedule: in presence Wednesday 29 November, 10.30-13.00, Santa Marta, room 051

For students that cannot attend in presence the course will be also online: https://robertoverdecchia.github.io/call 

Abstract: Literature reviews are a critical academic tool used to investigate the state of the art, summarize existing knowledge, and identify research gaps. This lecture is designed to cover all the basics of the literature review research method, including automated literature search, selection criteria, snowballing, data extraction, data synthesis, and tool support. By the end of the session, attendees will be equipped with the fundamental skills necessary to independently conduct systematic literature reviews. This lecture serves as a stepping stone for all those who aspire to contribute to the academic community through rigorous and methodical literature research.

Benedetta Picano

Matching Theory and Applications

Curriculum: INF

Hours / CFU: 12 / 3 CFU

Date: 5-12.2.2024

Simone Lolli, Luciano Alparone, Study of Atmosphere as the Key to Understanding our Planet and its Climate Change

Curriculum: TLC

Hours: 8 / 2 CFU

Period:  December 14 & 19, 2023

Abstract: This course focuses on the study of the atmosphere and its implications for Earth observation from space, meteorology and climate.
Contents:
1. Structure of the atmosphere
2. Absorption and scattering phenomena
3. Radiative transfer model through atmosphere
4. LiDAR instruments and their functioning
5. Applications of LiDARs for measurements of aerosols
6. Measurements of atmospheric parameters from Earth and space
7. Analysis of causes and trends of climate change
8. Discussion and concluding remarks

Andrea Tani

Adaptive Filtering in Wireless Communications

Curriculum: Telecommunications and Telematics

4 hours / 1 CFU

February 22, 2024

Abstract: Adaptive filtering is a fundamental component of statistical signal processing.
When dealing with signals stemming from environments with unknown or non-stationary
statistics, adaptive filters provide a worthwhile solution as they significantly enhance performance compared to conventional fixed filters. As a result, adaptive filters have found successful applications in various research areas, including communications, control, radar, sonar, seismology, biomedical engineering, and more. The course, after a general review of the fundamentals of adaptive linear filtering theory, starting from Wiener filter theory, provides a review of both the family of Least-Mean-Square (LMS) and of the Recursive Least-Squares (RLS) algorithms. Some low complexity implementations of the RLS relying on lattice structure will be also presented.
The second part of the course is devoted to the application to the wireless communications.
Example of adaptive filter application on adaptive channel equalization and spectrum estimation will be provided. In particular, we delineate the interference mitigation problem, i.e., how the adaptive filter is used to cancel unknown interference corrupting a desired signal, with particular regard to the design of techniques for self interference cancellation in full-duplex communication, which is recognized as key enabler of the next generation wireless communications system (6G).”

Antonio Luca Alfeo (UNIPI)

Dates: January 15,16,17, 2024

Hours  / CFU: 20 / 5

Registration required on: https://forms.gle/9fX3X83C4E5j9WPv6

Laura Carnevali

Markov Decision Processes: Theory and Applications

Curriculum: Computer Engineering

Hours: 12 / 3 CFU

Proposed period: February  2024

Preliminary 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, model checking of MDPs, applications of MDPs.

Fabio Schoen
Title: Optimization Models

Curriculum: Control, Optimization and Complex Systems

Hours: 12 / 3 CFU

Proposed dates: January 23-26, 2024

Abstract: This course is devoted to a presentation of modeling techniques and of special purpose models mainly based on linear and mixed integer formulations. We will present network flow models, minimax, min absolute, regression, rational objectives, how to use binary variables to impose logical constraints, structured Mixed Integer modes like knapsack, covering, location, sequencing. The main aim of the course will be to present modeling techniques, without much reference to algorithmic aspects.

Enrico Boni, Stefano Ricci, Francesco Guidi

Title: Electronics Embedded Systems for time critical applications

Curriculum: Electronics, Electromagnetics and Electrical Systems

Hours: 12 / 3 CFU

Proposed dates: end of January / Beginning Feburary, 2024

Abstract: 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.

Luigi Chisci

Title: Multi-agent and multi-object estimation

Curriculum: Control, Optimization and Complex Systems

Hours: 20 / 5 CFU

Proposed dates:
Monday 02/16/2024 9-13
Thursday. 02/19/2024 9-13
Friday 02/20/2024. 9-13
Monday 02/23/2024. 9-13 
Thursday 02/26/2024 9-13

Abstract: Goal: The course will provide an overview of advanced research in estimation, specifically concerning the two topics of multi-agent and multi-object estimation.
Multi-agent estimation deals with a network of agents with sensing, processing and communication capabilities that aim to cooperatively monitor a given system of interest. Multi-object estimation aims to detect an unknown number of objects present in a given area and estimate their states. Special attention will be devoted to the Kullback-Leibler paradigm for fusion of possibly correlated information from multiple agents and on the random-finite-set paradigm for the statistical representation of multiple objects. Applications to distributed cooperative surveillance, monitoring and navigation tasks will be discussed. 

Syllabus:
Recalls on Bayesian filtering.
Network modeling and Bayesian approach to multi-agent estimation.
Kullback-Leibler fusion and its properties.
Scalable fusion via consensus.
Distributed Kalman filtering with guaranteed stability.
Event-triggered communication for enhanced efficiency.
Random-finite-set (RFS) modeling of multi-objects.
Multi-object filtering.
Multi-object fusion.
Applications to multi-target tracking, simultaneous localization and
mapping (SLAM), source detection and localization.”

Raffaele Zippo (UNI Pisa) 

Network Calculus  (A PhD course offered by the PhD program in Smart Computing)

Department of Information Engineering, Via G. Caruso 16, University of Pisa
NB: in order to be able to participate on line, you must register By Sunday March 3rd, at https://forms.gle/vgEkQaRjJ6duuBww9

CURR: INF
Hours / CFU’s: 10 / 2.5
 
Abstract

This course shows how to derive performance bounds for queueing systems using Deterministic Network Calculus (DNC), a mathematical framework for worst-case performance analysis. First, we introduce the motivations and context where the DNC is used. Then, we introduce the basic concepts, namely arrival and service curves, and we show how to use them to compute performance bounds in an end-to-end perspective. We then move to tandems of network nodes, showing how the framework allows one to compose models of individual nodes to derive bounds. Finally, we discuss some relevant examples of systems and schedulers that can be modeled and studied with DNC. In all these, we first discuss intuitively the mechanisms that affect the worst-case analysis, how these are reflected in the mathematical model, and finally how they can be explored, also for practical uses, using the software library Nancy. Course Contents in brief: – Context and motivation: not just real-time systems require worst-case guarantees – Deterministic Network Calculus fundamentals: arrival and service curves – Delay, backlog and output bounds – The pay-burst-only-once principle – Modeling examples – Computing DNC results using the Nancy library

dr. Loretta Latronico (ESA)

Ethics of AI

Curriculum: all (Soft skills)

Hours: 20 / CFU’s: 3.5 SoftSkills CFU’s

Dates: March 18-21, 2024

8.30-13-30

Abstract: 
Artificial Intelligence (AI) is already happening today, and it is pervasive, often invisibly embedded in our day-to-day tools. As AI evolves, so do the many controversies that surround the use of this
advanced technology. From military drones to shopping  recommendations, AI is powering a wide array of smart products and services across nearly every industry—and with it, creating new ethical dilemmas for which there are no easy answers. As technology continues to develop at an unprecedented rate, those involved with AI often lack the tools and knowledge to expertly navigate ethical challenges. This course examines today’s most pressing ethical issues related to AI and explores ways to leverage technology to benefit mankind. It provides insights into how to achieve
responsible innovation of technology, to contribute to the quality of human life, sustainability and fair allocation of risks and benefits.

Course Contents in brief:
• Explore the foundations of Philosophy of Technology and  Responsible Innovation for the benefit of mankind
• Understand the technological basis of ethics in AI
• Analyse machine bias and other ethical risks
• Explore issues of AI in safety and progress, human rights, economics of happiness and deep ecology
• Assess the individual and corporate responsibilities related to AI deployment
• Examine the available frameworks for Derisking AI by design
• Examine the state-of-the-art for regulatory frameworks on artificial intelligence
• Exploit AI and Business Models Innovation in the space industry through the lenses of ethical challenges
• Work in teams to resolve Case study assignments inspired by real-life

References:
[1] M. Coeckelbergh (2020), AI Ethics, The MIT Press, http://mitpress.mit.edu
[2] B. Christian (2020), The alignment problem. Machine Learning and Human Values, First Edition,
New York, NY: W.W. Norton & Company
[3] Ethics I.1-7, Aristotle Physics II.1-3
[4] MIT Professional Education (Jun 2021), “Ethics of AI: Safeguarding Humanity

CV of the teacher:


CV of the Teacher
Loretta Latronico is an experienced Business Controller at the ESA Centre for Earth Observation (ESRIN) located in Italy. Her current research centers around AI and Business Models Innovation in
the space industry with a focus on Ethics of AI and Responsible Technology. She holds a Ph.D in Managerial Engineering at the University of Pisa, a Bachelor and Master Degree in Economics and Value Creation at the University “La Sapienza” of Rome. After graduation and a research experience in an ICT spin-off company of La Sapienza University, in 2010 she joined ESA-ESTEC in The Netherlands as Young Graduate in the Advanced Concepts Team. She then moved back to Italy in 2019. During these 11 years at ESA she has gained experience in various Space Missions and Programmes (Navigation, ATV, EarthCARE, Copernicus). Since 2011 she promotes development projects in Madagascar, where she has been several times in the past years as funder, volunteer and partner with direct involvement in the field work. She is passionate about Women empowerment, Permaculture, Ecology, Education, Performative Arts, Music, Photography, Astronomy and Economics of Happiness.

*: PhD course proposal, still to be approved by the PhD committee 

PhD Seminars

Aula 173

Prof. Angel Garcia-Fernandez, University of Liverpool


Title “Bayesian multi-target tracking and its application to autonomous vehicle perception”

 

Abstract: This talk will serve as an introduction to Bayesian multi-target tracking (MTT) systems. In MTT, there is an unknown number of objects that appear, move and disappear from a scene of interest, and we aim to estimate their states based on noisy measurements. This type of system with an unknown number of objects can be usually modelled via random finite sets (RFSs). For the standard dynamic and measurement models, the posterior density, which contains all information on the current set of targets, is given by the Poisson multi-Bernoulli mixture (PMBM) filter. The talk will explain the RFS and PMBM fundamentals, explain how Bayesian MTT can be combined with deep learning video detectors, show videos of applications to autonomous vehicle perception, and point out to available PMBM code online.

Organizer: prof. Giorgio Battistelli

Gabriele Eichfelder: Ingredients for local and global algorithms in multiobjective optimization


Date: 13/11/2024 15:00:00

Location: Aula Caminetto

Abstract: In multiobjective optimization one considers optimization problems with several competing objective functions. Such problems arise in a large variety of applications. They play, for instance, an important role in models of the energy market when intelligent neighborhood networks have to be integrated in overarching distributing networks and a variety of individual and competing criteria have to be taken into account. Next to multiple objective functions, these problems often possess additional difficulties as discrete-continuous variables, bilevel structures, or uncertainties.

 

In this talk we give an introduction to multiobjective optimization. We shortly present the most widespread solution approach which is known under the name scalarization. There, one formulates parameter-dependent single-objective replacement problems and solves those iteratively for a set of parameters. We discuss the limits of such approaches in terms of quality guarantees and for specific classes of optimization problems as those with additional difficulties as mentioned above. Moreover, we give some of the basic ideas of direct methods which avoid to scalarize first and, thereby, try to overcome some of these issues.

Organized by: Fabio Schoen