Digital Twin in Smart Cities 

Lecturer: Laura Po

Program:

Exam:
Not yet defined

CFD: 3

Modelling, Identification and Control of Robots

Lecturer: Luigi Biagiotti, Roberto Zanasi, Laura Giarrè

Program:
The course introduces the theory of optimal predictive control (Model Predictive Control), starting from the formulation of the optimal control problem with quadratic cost for linear systems (LQ control). The main stability and persistent feasibility results will be illustrated using numerical examples and applications in the automotive and Matlab toolboxes such as MPT and MPC toolbox.

Exam:
Not yet defined

CFD: 3

The “Wide” Advantage in Power Electronics

Lecturer: Nicolò Zagni

Schedule:
February 13-15-20-22 2024
09.00 AM -12.00 PM
Meeting Room MO27 Building - 1st Floor

Program:

The course introduces students to the performance improvement that power electronic circuits can achieve by taking advantage of the “wide-band-gap” semiconductor technologies.

Gallium-Nitride and Silicon-Carbide based devices recently entered the market but they are expected to replace in a near future the conventional Silicon-based devices that represent nowadays the mainstream technology.

The course will cover the following topics:

1. Power electronics and its applications/markets, wide-band gap semiconductors, figures of merit, basic power switching converters
2. SiC and GaN devices: operating principles, power transistors structures
3. Stability and Reliability of GaN HEMTs, trapping effects, trapping mechanisms, measurement techniques
4. Advanced research topics: hole redistribution model, breakdown mechanisms, partial Ron recovery, vertical GaN devices, ultra wide-band gap devices

Exam:
Written Test

CFD: 3

IP4Engineers

Lecturer: Isabella Ferrari

Schedule:
May 2024

Program:
This course examines the interaction between law and engineering, in order to provide students with an interdisciplinary perspective on nowadays global challenges. The course will deeply touch on the so-called 'Law by design' approach, comparing efficient solutions set by governments, parliaments and technical players on the domestic and the international stage.

Exam:
Not yet defined

CFD: 3

The basic principles of project management

Lecturer: Prof. Massimo Bertolini

Schedule:
7/05 - dalle 16 alle 18
9/05 - dalle 16 alle 18
14/05 - dalle 16 alle 18
16/05 - dalle 16 alle 18
21/05 - dalle 16 alle 18
23/05 - dalle 16 alle 18
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Per "l'iscrizione" al corso, ho creato il Team The basic principles of project management - Ph.D. Class | Generale | Microsoft Teams Basta che gli studenti si iscrivano / chiedano l'iscrizione

Program:
The course aims at providing the theoretical and practical fundamental knowledge of project management as a required tool for the design and the development of a project. The topics will be covered within the framework of PMBOK (Project Management Body of Knowledge) of PMI (Project Management Institute).

Exam:
Not yet defined

CFD: 3

AI and ML for financial data analysis

Lecturer: Petter Kolm (New York University)

Schedule:
June 2024

Program:

Exam:
Not yet defined

CFD: 0

Introduction to the Monte Carlo Methods for Engineering Applications 

Lecturer: Pierpaolo Palestri

Schedule:
27/5 ore 15-18
4/6 ore 10-13
10/6 ore 15-18
17/6 ore 15-18
Sala riunioni 1o piano MO26.

Per accedere al materiale didattico fare richiesta di iscrizione al Team: Introduction to the Monte Carlo Methods for Engineering Applications | Generale | Microsoft Teams

Program:
Techniques based on the generation of random numbers (referred as “Monte Carlo”) found widespread use in fields going from astrophysics, to semiconductor devices, chemistry, material science, and mechanics. For example, in device physics the Monte Carlo method consists in simulating the motion of sample particles as a sequence of classical motion interrupted by scattering events described by quantum mechanics. Random sampling is also used to solve integrals in many dimensions and to generate random parameters with wanted probabilistic distribution to be employed in simulation of various systems. The first part of the course provides examples of use of the Monte Carlo method to simulate the motion of sample particles, the generation of random variables and functions and the development of Kinetic Monte Carlo algorithms. Simple codes in MATLAB will be shared with the students and modified by them to solve relevant engineering problems. In the second part, the focus will shift to the use of Monte Carlo techniques to simulate carrier transport in semiconductor devices.

Exam:
Not yet defined

CFD: 3

Semiclassical and quantum mechanical foundations of modern nanoscale FET device operation

Lecturer: Luca Selmi

Schedule:

Giovedì 27.6 ore 9.30-11.30

Venerdì 28.6 ore 9.30-11.30

Martedì 2.7 ore 9.30-11.30

Mercoledì 3.7 ore 9.30-11.30

Giovedì 4.7 ore 9.30-11.30

Venerdì 5.7 ore 14.00-16.00

Sala riunioni MO2 piano terra

Program:
Based on a succinct and highly focused review of a few key concepts of quantum mechanics the course will introduce the foundations of the semi-classical approach to nanoscale electron device modeling. Selected elements of quantum mechanics are merged with classical electrostatics and transport theory to yield a state of the art description of advanced nanoscale FET. Conventional, strained and alternative channel materials, quasiballistic transport in a 2D or 1D electron gas, FinFET, double gate FDSOI, nanowire, nanosheet and nanofork gate all around device architectures will be addressed.

Exam:
Not yet defined

CFD: 3

Privacy-preserving computation methods

Lecturer: Julián Salas Piñón
https://talent.uoc.edu/es/julian-salas-pinon.html

Schedule:

The course encompasses 12 hours, detailed as follows:

Monday 22 July:
• Introduction to privacy concepts, cases of attacks to naïve anonymization (2h)
• Pseudonymity, k-anonymity and variants. (1h)
• Activity (1h)

Tuesday 23 July:
• Differential Privacy (DP) (2h)
• Local DP (1h)
• Activity (1h)

Wednesday 24 July:
• Privacy preserving techniques for Machine Learning (2h)
• Differentially private supervised and unsupervised learning (1h)
• Activity (1h)

Program:
Data analysis and mining brings many benefits for understanding our society. At the same time, it reveals a lot about ourselves, from our habits to our personality traits. These characteristics may also be used to disclose private information, and even to re-identify our data if it is not correctly protected. To motivate privacy enhancing techniques, we review some of the past attacks to privacy resulting from naïve anonymization (of medical records, AOL internet search queries, Netflix movie ratings and NYC-taxis geo-located data) and present measures to prevent attribute and identity disclosure (namely k-anonymity and differential privacy) in different contexts such as databases, networks and geo-located data. We finish the course explaining the trade-offs between the risk of disclosure and utility loss in Machine Learning Algorithms for Responsible AI.

Exam:
written test, oral test

CFD: 3

Securing the Internet of Things: Emerging Trends, Challenges, and Future Research Directions

Lecturer: Carlo Mazzocca
Carlo Mazzocca is a postdoc researcher at the University of Bologna. His research interests mainly include digital identity, security mechanisms based on distributed ledger technologies, and authentication and authorization solutions for the cloud-to-thing continuum

Schedule:
9-11 July, 2024
aula P1.1 dalle 14 alle 19

Program:
This Ph.D. course provides a comprehensive exploration of emerging trends for securing the Internet of Things (IoT) environments. For each topic presented, students will learn state-of- the-art solutions, challenges, and future research directions.

Introduction (1.5 hours)

• Introduction to IoT and cybersecurity
• Common cybersecurity threats and vulnerabilities
• Attack vectors in IoT environments
• Analysis of real-world IoT security breaches

Lightweight Cryptography Algorithms for IoT Devices (1.5 hours).

• Limits of conventional cryptography algorithms
• Existing lightweight cryptography algorithms
• Open challenges and research directions

Digital Identification in IoT Networks (3 hours)

• Identity of Things
• Access control models and identity management systems
• PKI certificates, decentralized identifiers, and verifiable credentials
• Revocation of PKI certificates and verifiable credentials
• Open challenges and research directions

Machine Learning in IoT Security (3 hours)

• Firmware analysis
• Attack detection and mitigation
• Intrusion detection systems
• Federated learning
• Public IoT datasets for network security research
• Open challenges and research directions

Distributed Ledger Technologies for IoT (3 hours)

• Access control and identity management systems
• Interoperability, confidentiality, integrity, and privacy
• Application domains
• Integration of distributed ledger technologies and federated learning
• Open challenges and research directions

Exam:
Students will be asked to produce an essay about possible intersections between their research interests and the topics/open issues presented in this course

CFD: 3

Enhance your Soft Skills: ICT Summer School for Personal and Professional Development

Schedule:
26-30 Agosto 2024

Program:
The partecipation to the school is mandatory for the students of the first year

Exam:
Not yet defined

CFD: 5

Natural Language Interfaces to Data

Lecturer: Georgia Koutrika, Athena Research Center

Schedule:
le lezioni si terranno martedì 15, mercoledì 16 e giovedì 17 ottobre dalle ore 9 alle ore 13 presso la sala riunioni al primo piano dell'edificio MO27

Program:

In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system.

 

Outline

1. Text-to-SQL

• The Text-to-SQL problem
• Benchmarks
• A Taxonomy for Deep Learning Text-to-SQL Systems
• Key Systems
• Research Challenges

2.SQL-to-Text

• The SQL-to-Text problem
• Challenges
• Key Systems
• Research Challenges

3.Data-to-Text

• What is Data-to-Text
• Subfields of Data-to-Text
• Table-to-Text

 

Exam:
Hands-on examination on existing systems

CFD: 3

Exploring the Frontier of Database Research: the Alliance with Foundation Models

Lecturer: Matteo Paganelli

Schedule:

• Monday  Sept 09 14:00-18:00 - P0.5 (FA-0D)
• Tuesday  Sept 10 09:00-13:00 - Lab. P0.1 (FA-0A)
• Wednesday  Sept 11 14:00-18:00 - P0.2 (FA-0B)

Program:

Given the success of Foundation Models (FMs) in various fields, a key question emerges: how can these models benefit database research?
This course addresses this question by first introducing the basic background to understand how foundation models work. Secondly, it presents state-of-the-art approaches in data preparation, primarily focusing on encoder-only transformer architectures. The third module will provide an overview of tabular foundation models. Then, the course examines recent attempts to apply in-context learning to downstream data preparation tasks. The final module explores applications of the Retrieval-Augmented Generation (RAG) paradigm, discusses the main limitations of FMs, and outlines potential new directions in database research.
The course combines theoretical lessons with coding demonstration sessions.

 

Module 1: Introduction to Foundation Models (FMs) - 3h
1.1 The transformer architecture
1.2 Hands-on transformers with HuggingFace's Transformers library
Module 2: FMs for data preparation - 3h
2.1 The data integration pipeline
2.2 State-of-the-art methods for data preparation
Module 3: Tabular FMs - 2h
3.1 Introduction to tabular FMs
3.2 Hands-on tabular FMs
Module 4: In-context learning in data preparation tasks - 2h
4.1 Applications
4.2 Entity Matching with the OpenPrompt library (PromptEM)
Module 5: FMs limitations & next research directions - 2h
5.1 Hands-on Retrieval-Augmented Generation (RAG) with LangChain
5.2 FMs limitations
5.3 Next research directions

Exam:
yes

CFD: 3

Explainable AI in the Neural Network Domain

Lecturer: Prof. Piotr Andrzej Kowalski

Schedule:

• Thursday, 7th November: 09.00 AM - 01.00 PM | Sala Riunioni MO27
• Friday, 8th November: 02.00 PM - 06.00 PM | Sala Riunioni MO27
• Monday, 11th November: 02.00 PM - 06.00 PM | Sala Riunioni MO26
• Tuesday, 12th November: 09.00 AM - 01.00 PM | Sala Riunioni MO27
• Thursday, 14th November: Exam (Written Project) | Sala Riunioni MO27

Program:

This course introduces the principles and techniques of Explainable Artificial Intelligence (XAI) within the context of neural networks. Participants will explore methods to interpret and explain decisions made by neural networks, addressing the "black-box" nature of these models. The course will blend theory and hands-on practice through real-world case studies in domains such as healthcare and environmental monitoring.

Course Modules:

1. Introduction to Neural Networks and XAI

   • Overview of neural networks' structure and behavior.
   • Understanding the black-box issue and the need for explainability.

2. Interpretability Techniques

   • Introduction to feature importance, LIME, and SHAP techniques.
   • Methods to interpret complex models and enhance transparency.

3. Sensitivity Analysis I

   • Explore gradient-based and perturbation-based sensitivity analysis to measure how inputs influence neural network predictions.

4. Sensitivity Analysis II

   • Learn about global and local sensitivity analysis methods, which quantify the importance of features at different levels.

5. XAI Techniques in Practice

   • Practical application of XAI in areas like healthcare and environmental studies, offering case studies and real-world examples.

6. Ethical Considerations in XAI

   • Addressing bias, fairness, and transparency in AI models.
   • Ethical concerns in deploying AI systems, ensuring responsible use.

7. Future Trends in XAI and Neural Networks

   • Exploration of emerging trends and research directions in XAI and their potential impact on AI technologies.

By the end of the course, attendees will gain a deeper understanding of:

• Neural Networks and the importance of explainability.
• Key interpretability techniques such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations).
• Sensitivity Analysis including both global and local methods.
• Practical applications of XAI in fields such as medicine and air pollution.
• Ethical concerns, addressing bias and fairness in AI models.

Exam:
The final exam will be in the form of a written project, applying the techniques learned throughout the course.

CFD: 4