List of approved PhD courses for Academic year 2021-2022
This list is partial and subject to continuous update. We will add new courses as soon as they are approve by the PhD committee or just proposed, waiting for a formal approval.
4 Hours, 1 CFU
October 11th, 2022 – 09:30 – 13:00
Prof. Angela Slavova, “Cellular Neural Networks (CNN): Theory and Recent Applications in Engineering”
Sala riunioni Dinfo
Many phenomena with complex patterns and structures are widely observed
in nature. For instance, how does the leopard get its spots, or how does the zebra
get its stripes, or how does the fingerprint get its patterns? These phenomena are
some manifestations of a multidisciplinary paradigm called emergence or
complexity. They share a common unifying principle of dynamic arrays, namely,
interconnections of a sufficiently large number of simple dynamic units can exhibit
extremely complex and self-organizing behaviors. The CNN is an analogue
dynamic processor array which reflects just this property: the processing elements
interact directly within a finite local neighborhood.
In this mini course, the theory of CNN will be presented. The CNN is
simply an analogue dynamic processor array, made of cells, which contains linear
capacitors, linear resistors, and linear and nonlinear controlled sources. After the
introduction of the CNN paradigm, CNN Technology got a boost when the
analogic cellular computer architecture, the CNN Universal Machine, was
invented. The most successful chips embedded in a computational infrastructure
provided the framework for analogic cellular software development.
We shall present many applications of CNN in engineering. The
programmability and the rapidity of the chip make the CNN attractive; the
nonlinearity, as we will see, allows nonlinear signal processing to be obtained, but
these advantages are counterbalanced by the need for a large silicon area per cell
and quite large power consumption. Stimulating applications of CNN have in fact
been developed in a wide range of disciplines, ranging from classical and
sophisticated image filtering to the biological signal processing solution of
nonlinear partial differential equations, physical systems and nonlinear phenomena
modeling, the generation of nonlinear and chaotic dynamics, associative memories,
neurophysiology, robotics, etc.
Prof. Angela Slavova
Prof. Angela Slavova graduated at the University of Russe, Computer Science, M.
Sc. in 1986. She got her Ph.D. in Mathematics in 1994. In 2005 she became a
Doctor of Science and in 2007 Full Professor at the Institute of Mathematics and
Informatics, Bulgarian Academy of Sciences. Since 2004 Prof. Slavova is a Head
of the Department of Mathematical Physics, Institute of Mathematics, Bulgarian
Academy of Sciences, and since 2011 – Head of the Department of Differential
Equations and Mathematical Physics at the same Institute. Prof. Slavova’s research
work is in the field of differential Equations, Cellular Nonlinear/Nanoscale
Networks; nonlinear waves propagation; equations of Mathematical Physics;
applications of differential equations in sciences. She participated in more than 40
conferences, workshops and seminars as an invited speaker. She got in the period
1992-1993 Fulbright Scholarship at FIT, USA, January-July 1998 – CNR
Fellowship, University of Florence, Italy, 2016 – Dresden Senior Fellowship,
Technical University of Dresden, Germany, 2019 – Eleonore Trefftz Women
Fellowship, Technical University of Dresden, Germany. She was visiting professor
at the University of Ioannina, Greece, University of Catania, Italy, University of
Torino, Italy, Astronomical Observatory, Torino, Italy, Ariel University, Israel,
University of Ferrara, Italy, University of Bologna, University of Florence, Italy,
Ben-Gurion University, Israel, Technical University of Dresden, Germany,
Technical University of Lisbon, Portugal, etc. Professor Slavova has more than
120 publications in prestigious journals in Applied Mathematics, IEEE Journals,
etc. She is an author and co-author of 4 monographs in Kluwer Academic
Publishing, World Scientific, Singapore, Cambridge Scholars, and Springer. Prof.
Slavova is a member of AMS, SIAM, Chair of the Bulgarian Section of SIAM,
EMS, IEEE Technical Committee on CNNAD. She has 2 NATO grants with the
University of Florence, Italy, CNR grant with the University of Bologna, Italy,
Bilateral grants with Ariel University, Israel, 3 DFG grants with the Technical
University of Dresden, Germany. Prof. Slavova is a program committee chair of
the International Conference on Applications of Differential Equations in Sciences
(NTADES) since 2014, of CNNA 2016, Dresden, Germany, and ECCTD 2020,
- October 3, 2022 10:00 – 13:00;
- October 6 and 7 15:00 – 18:00.
- an additional lecture, to be defined
This course gives an introduction to multi-objective optimization (MOO) and explains how to develop and tune efficient multi-objective genetic algorithms (MOGAs). The course starts giving a mathematical background on MOO. The key concepts of genetic algorithms are then introduced for real, integer, mixed-integer, and combinatorial optimization problems, including some techniques to handle constraints. The focus then moves on some approaches to find the best hyperparameter values, and to carry out a performance evaluation based on typical measures of MOO. The course ends presenting some multi-criteria decision making techniques, with particular reference to choosing the final Pareto-optimal solution to implement.
In particular we will face structural variants detection with TGS and SGS data, differential expression with bulk RNA-seq data and population cell typing with the new single cell RNA-seq experimental strategy.
Teacher: Matteo Lapucci, PhD
Title: Optimization Algorithms in Machine Learning
Period: 18-22 July 2022
CFU’s: 2 (8 hours)
Proposed by: Fabio schoen
Modern machine learning techniques heavily rely on optimization procedures to carry out models training. In this short course, the most widely employed optimization algorithms in machine learning settings will be discussed. In particular, we will investigate the main theoretical and computational issues concerning the training procedures for Support Vector Machines and Deep Networks.
Francesco Chiti, Laura Pierucci
Kuly 20, 2022
From Quantum Networking to the Quantum Internet
4 h / 1 CFU
This Course provides an insight on the novel Quantum Networking (QN) paradigm by first giving some preliminary notions on quantum communications, and, further, investigating the emerging architectures supporting the definition of the emerging Quantum Internet (QI) concept. The current technologies expected to be adopted to achieve QN and QI are reviewed with a specific focus on quantum satellite backbones, together with the Software-Defined Networking (SDN) approach, which is fundamental for these systems.This Course provides an insight on the novel Quantum Networking (QN) paradigm by first giving some preliminary notions on quantum communications, and, further, investigating the emerging architectures supporting the definition of the emerging Quantum Internet (QI) concept. The current technologies expected to be adopted to achieve QN and QI are reviewed with a specific focus on quantum satellite backbones, together with the Software-Defined Networking (SDN) approach, which is fundamental for these systems.
Franco Scarselli (University of Siena)
Theoretical fundamentals of Neural Networks
Lorenzo Mucchi, Fundamentals of Molecular and Nanoscale Communications
8h / 2 CFU
Molecular communication (MC) is a bio-inspired paradigm where the exchange of information is realized through the transmission, propagation, and reception of molecules. This paradigm was first studied in biology, since it is successfully adopted in nature by cells for intracellular and intercellular communication. MC is considered a promising option for communications in nanonetworks, which are defined as the interconnections of intelligent autonomous nanometer-scale devices, or nanomachines. Thanks to the feasibility of MC in biological environments, MC-based nanonetworks have the potential to be the enabling technology for a wide range of applications, mostly in the biomedical, but also in the industrial and surveillance fields. Fundamentals of Diffusion-Based Molecular Communication in Nanonetworks analyzes the MC paradigm from the point of view of communication engineering and information theory, and provides solutions to the modeling and design of MC-based nanonetworks.
2022/06/TBD:A. Mazzinghi, A. Freni: RDIF Theory, Methods, Applications and issues
16 hours / 4 CFU
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.
dr Camilla Baratto (CNR, Brescia)
CFU’s: 2 (8 hours)
System and solutions for biomedical science take advantage of a multidisciplinary approach that involve engineering , physics, chemistry and biology. Sensors are nowadays ubiquitous, and their use can help monitoring human’s health. This course is an introduction to bio-chemical sensors that are applied in this field, setting the basic knowledge in the sensor field. The course will give an overview of the functioning principles of the most employed gas sensors, with the introduction of important criteria like sensitivity, selectivity, stability, etc. Besides explaining the meaning of the performance criteria, there will be plenty of examples illustrating the performance of various types of sensors. Then the course will give an introduction to the most popular detection principles. A special attention is given also to Raman spectroscopy as a technique applied to biomedical diagnosis. RAMAN and FLUORESCENCE SPECTROSCOPY a. The basics of Raman Spectroscopy b. RS-Surface Enhanced RS (SERS), TERS, CARS c. Key experimental considerations for implementing the main experimental Raman spectroscopic techniques d. Application examples of SERS and fluorescence spectroscopy to detection of biomolecules
Enrico Vicario (DINFO, Unifi)
Applications and methods of quantitative evaluation of stochastic processes
Federico Pernici (MICC/ DINFO/Unifi)
Incremental Representation Learning for Visual Search
Rosaria Silipo, Head of data Science at Knime
Deep learning is used successfully in many data science applications, such as image processing, text processing, and fraud detection. KNIME Analytics Platform includes an integration to the Keras library for deep learning, combining the codeless ease of use of KNIME Analytics Platform with the extensive coverage of deep learning paradigms by the Keras library.
Though codeless, implementing deep learning networks still requires orientation to distinguish between the learning paradigms, the feedforward multilayer architectures, the networks for sequential data, the encoding required for text data, the convolutional layers for image data, and so on.
This course will provide you with the basic orientation in the world of deep learning and the skills to assemble, train, and apply different deep learning modules.
This is an instructor-led course consisting of eight, 60 minute online sessions. Each session has an exercise for you to complete at home.
Session 1: Introduction to KNIME Analytics Platform
Session 2: Classic Neural Networks
Session 3: Introduction to KNIME Deep Learning Extensions
Session 4: Deep Learning Case Studies and Different Design Components
Session 5: Recurrent Neural Networks for Sequence Analysis
Session 6: Recurrent Neural Networks for NLP
Session 7: Convolutional Neural Networks for Image Processing
Session 8: Generative Adversarial Networks
Summer School on Optimization, Big Data and Applications (OBA), third edition (Veroli [FR])
Teachers: Alessandro Verri (Univ. di Genova), Claudio Gentile (CNR/IASI, Roma) Andrew Gordon Wilson (New York University), Peter Frezier (Cornell University)
24 h / 6 CFU
This is the third edition of the OBA summer school. Visit https://webgol.dinfo.unifi.it/oba for info on the 2019 Edition. New info will be published soon. This course is residential (no streaming) and participation is subject to acceptance by the Scientific Committee.
In order to apply for the OBA2022 Summer School, a brief curriculum vitae et studiorum must be sent no later than April 15, 2022.
Acceptance will be communicated by May 15, 2022.
Marco Gori (University of Siena)
June 20-24, 2022
Roberto Giorgi and Marco Procaccini (University of Siena)
May 23-27, 2022
There is an ever-increasing need for more application performance and energy efficiency. However, since 2005 computing platforms are providing substantially higher performance only by increasing the number of processors (not anymore by just increasing clock frequency). Current processors range from multi-core encompassing even 128 cores to GP-GPU with more than 10000 cores on the single-chip and to the aggregation of these resources connected via high-speed interconnects. Parallel programming permits us to express an algorithm to properly exploit what the technology is providing us. But what are the principles of parallel programming that best allow (or limit) maximum performance? This course covers several foundational topics central to parallel computing, starting from the basic methods to properly identify performance bottlenecks and provides the advanced practical means to program a parallel machine by using the most used standards such as OpenMPI, OpenMP, CUDA. We analyze these concepts through several hands-on examples and real-world data.
Artificial Intelligence solutions for Natural Language Processing. Presentation of the different technologies and methods needed to carry on the whole workflow for analyzing unstructured text data: Web Crawling, Text Parsing, Natural Language Processing (NLP) and Sentiment Analysis. Description of the main open source techniques and tools applied to the analysis of data gathered from Social Media (Twitter). Tools for Information Extraction (also on distributed systems based on Hadoop) from unstructured textual data, exploiting pattern matching and NLP techniques: tokenizers, syntactic and morphological analysis, Part-of-Speech tagging, syntactic parsing and use of open source taxonomies and dictionaries for text annotation. Use of Deep Learning techniques, based on LSTM neural networks and Attention Mechanism for NLP and Sentiment Analysis, presenting real use cases.
Fabrizio Tronci – Huawei Pisa Research Centre, The basics of Functional Safety in the automotive domain
February 7, 15, 2022
6 hours / 1.5 CFU
Functional safety has become increasingly important for automotive development in recent years. The number of new, safety-related functions in the road vehicles will continue to increase on the way to highly automated and fully automated driving. As a consequence, the interactions between individual functions will become even more prominent and will increase the complexity of the “vehicle system”.
The course will provide an overview of functional safety basic concepts and introduce the recommendations of ISO 26262 standard with particular focus of embedded software development.
Moreover, the essential methodologies and analysis will be explained by using realistic use cases.
Organized by: Alessandro Fantechi
Giovanni Collodi, Stefano Selleri,
High Frequency solutions for Internet of Things connectivity
February 11,15, 2022
8h / 2 CFU
The course focuses on TX/RX Front Ends for Internet of Things (IoT) applications. IoT will become a pervasive technology within the next 10 years. In the course the TX/RX front end is analyzed as the central element enabling connectivity, in its parts: Antenna e Transceiver, which must be co-designed and co-optimized and cannot be considered independently in an effective design.
The course illustrates the different Transceiver and Antenna architectures, relating them to the different communication standards, highlighting their critical aspects, existing commercial solutions and illustrating possible future trends.
Mara Bruzzi, Research and development in photovoltaic cells
February 14-18, 2022
12h / 3 CFU
Photovoltaic cells are nowadays subject of intense research. The course will investigate principles of operation of new generation of photovoltaic cells as dye sensitized and multijunction solar cells for applications indoors and outdoors. The course will follow an experimental approach in laboratory with measurement of the current voltage characteristics in different illumination conditions and numerical analyses.
Lucia Ballerini, Leonardo Bocchi, Maria Valdes-Hernandez (to be confirmed),
Medical Image Analysis: application to brain
8 h / 2 CFU
february 28th-March 4th, 2022
Medical image processing basics:
2D image processing
3D image processing
ROI / VOI analysis
Brain MR pre-processing & registration
Multimodality registration using MATLAB
Bias field correction
Brain image fusion
Brain image segmentation
Rosaria Silipo, Head of data Science at Knime
This course introduces the main concepts behind Time Series Analysis, with an emphasis on forecasting applications: data cleaning, missing value imputation, time-based aggregation techniques, creation of a vector/tensor of past values, descriptive analysis, model training (from simple basic models to more complex statistics and machine learning based models), hyperparameter optimization, and model evaluation.
Learn how to implement all these steps using real-world time series datasets. Put what you’ve learnt into practice with the hands-on exercises.
This is an external instructor-led course consisting of eight, 60 minutes online sessions. Each session has an exercise for you to complete at home.
Session 1: Introduction to KNIME Analytics Platform
Session 2: Introduction to Time Series Analysis
Session 3: KNIME Components for Time Series Analysis
Session 4: Understanding Stationarity, Trend and Seasonality
Session 5: Naive Method, ARIMA models, Residual Analysis
Session 6: Machine Learning based methods
Session 7: Model Optimization
Session 8: Deep Learning and Time Series Analysis
Angelo Freni, Juan Mosig, Anja Skrivervik, Zvonimir Sipus, Alexander I. Nosich, Agnese Mazzinghi – FREQUENCY DOMAIN TECHNIQUES FOR ANTENNA ANALYSIS: From Inhouse to Commercial EM Solvers
Date: October 3-7, 2022
CFU’s: 8 (32 hours)
The course aims to give the student an appreciation of the uses and limitations of frequency domain computational techniques applied to scattering and antenna problems. The module gives the student a thorough background in the methodology of these techniques from a fundamental standpoint, while giving a grasp of the practical applications. Emphasis will be given to the practical problems encountered in the implementation of the integral equation techniques (Method of Moments, linear systems, integration techniques, Green’s functions, stratified media, convergence, singularities, periodic problems). Simple problems are considered to give an understanding of how the choices made in designing the algorithms translate into the real strengths and limitations of the software.