Hours:
16 hours (4 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:

The function of living tissues is intimately linked to their complex architectures. Biofabrication technologies are rapidly advancing as powerful tools capable to capture salient features of tissue composition and thus guide the maturation of engineered construct into mimicking functionalities of native organs. In biofabrication, multiple cell types and biomaterials are patterned in three dimension through automated processes, either via bioprinting or bioassembly. The current paradigm in bioprinting relies on the additive layer‐by‐layer deposition and assembly of repetitive building blocks, typically cell‐laden hydrogel fibers or voxels, single cells, or cellular aggregates. Since its initial conception and its first implementations through inkjet printing technologies, bioprinting rapidly introduced a new toolset for bioengineers and material scientists to produce new strategies to restore the function of impaired tissues. In this course, both currently available and innovative bioprinting approaches will be reviewed, with a particular focus on how these techniques can be combined to mimic the multi-material hierarchical composition of living tissues. Key concepts underlying extrusion, laser and light-based technologies will be discussed, together with the recent emergence of layerless volumetric and field-based printing methods. Finally, technological advances and challenges towards the biofabrication of both in advanced in vitro models for biomedical and pharmaceutical research, as well as the production of clinically-relevant multi-tissue constructs for regenerative medicine via will be discussed, in light of specific, state-of-the-art examples of biofabricated tissues.

Course Contents in brief:

1. Introduction to additive manufacturing
2. Fundamentals of biofabrication: enable cell processing via bioprinting and bioassembly technologies
3. Hydrogels, bioinks and biomaterial inks
4. Extrusion-based bioprinting
5. Sacrificial templates and suspended printing
6. Light-driven bioprinting
7. Layer-by-layer and layerless 3D biofabrication
8. Field-based fabrication strategies (magnetic, sound and light fields)
9. Printability, shape fidelity and automation in biofabrication processes
10. Smart and stimuli-responsive prints
11. Addressing specific challenges in tissue engineering: examples of applications in regenerative medicine and in vitro models

Schedule:

Day1 - 29 March 2021 - 9:00 - 13:00

1. Introduction to additive manufacturing (1hr)
2. Fundamentals of biofabrication (1hr)
3. Hydrogels, bioinks and biomaterial inks (2 hrs)

Day2 - 30 March 2021 - 9:00 - 13:00

1. Extrusion-based bioprinting (1:30 hr)
2. Sacrificial templates and suspended printing (1hr)
3. Light-driven bioprinting (1:30 hr)

Day3 - 31 March 2021 - 9:00 - 13:00

1. Layer-by-layer and layerless 3D biofabrication (1hr)
2. Field-based fabrication strategies (magnetic, sound and light fields) (1 hr)
3. Printability, shape fidelity and automation in biofabrication processes (1 hr)
4. Smart and stimuli-responsive prints (1hr)

Day4 - 1 April 2021 - 9:00 - 13:00

Addressing specific challenges in tissue engineering: examples of applications in regenerative medicine and in vitro models

1. Musculoskeletal tissue engineering (1hr)
2. Liver repair (1hr)
3. Cardiovascular and neural applications (1hr)
4. In vitro models and biofabrication for organ-on-chip applications (1hr)

Hours:
18 hours (4 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:

This course aims to provide attendants with statistical tools for handling data collection, base statistical testing and linear modeling, including ANOVA, non-parametric tests and Linear Regression framework.

The course is focused on solving the major problems and difficulties encountered during the analysis of data collected from open field experiments, therefore subject to high variability due to factors that are not always controllable or measurable, data loss, difficulty of replication of experiments over time and large and variable surfaces.

The course will consist of classes explaining the theory and practical exercises related to the applications, by using the statistical software R. Attendants will be provided with sample code in R notebooks. Attendants can then use the codes provided to test, replicate and adapt the statistical models presented during the lectures to their own data.

Course Contents in brief:

Proposal for 6 lectures, 3 hours each, divided into theory and practice

1. Descriptive statistics, centrality and variability measures
2. Sampling methods
3. Principles of Design of Experiments
4. Hypothesis testing
5. ANOVA and Contrasts
6. Linear Regression and Post hoc tests

Schedule:

1. 20/01/2021, 9:30 - 12:30
2. 27/01/2021, 9:30 - 12:30
3. 03/02/2021, 9:30 - 12:30
4. 10/02/2021, 9:30 - 12:30
5. 17/02/2021, 9:30 - 12:30
6. 24/02/2021, 9:30 - 12:30

Hours:
20 hours (5 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:
Smart sensors are ubiquitous in nowdays society and will become more and more important in the next future, their applications ranging over many aspects of daily life, such as: (i) healthcare: wearable devices for monitoring of physiological parameters in both hospitals or home (e.g., elderly); (ii) monitoring of athlete's perfomance; (iii) continuous monitoring of industrial plaforms for automatic diagnosis (selfmonitoring) or optimization in the production line (Industry 4.0); (iv) human-computer interfaces; (v) automotive. These sensors often give a continuous time monitoring and, thus, a large dataset of complex signals that need to be processed, both in real time or offline, depending on the specific application. Many
monitoring systems are developed as a wireless sensor network, i.e., a network with a complex topology of the links, where each link is a communication channel between sensors.
Thus, a proper processing of these large datasets of time signals is crucial, not only regarding computational efficiency, but also for the extraction of informative parameters (information retrieval).
This course will cover the basic concepts and techniques used in the processing of complex time signals, also including feature extraction in multivariate signals with particular attention to connectivity features of time-varying networks.
In the first part, we will give an overview of the main results of classical signal processing. Some practical examples will be given during the lectures, in order to make the students more confident with signal processing tools.
In the second part, we will focus on connectivity measures defined on time-varying networks. We will discuss some specific applications, mainly in health applications and physiological monitoring.
Finally, in the third part, we will give a few insights on recent developments in the processing of signals with complex intermittency, showing some applications in the field of neurophysiology.

Course Contents in brief:

1. Statistical signal processing
2. Multivariate signals
3. Time-varying networks
4. Network connectivity and connectomics
5. Complex systems
6. Signals with complex intermittency

Schedule:

1. lunedì 08 marzo 2021: dalle 9 alle 13
2. martedì 09 marzo 2021: dalle 9 alle 13
3. mercoledì 10 marzo 2021: dalle 9 alle 13
4. giovedì 11 marzo 2021: dalle 9 alle 13
5. venerdì 12 marzo 2021: dalle 9 alle 13

This course has been moved to a date to be determined, depending on the evolution of the pandemic.

Hopefully, it will be scheduled in July. The course involves a laboratory activity that cannot be carried out remotely, so Prof. Leupers will take the course as soon as the pandemic situation will allow it.

Updates will be posted on this webpage of the PhD program.

Hours:
20 hours (5 credits)

Room:
Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

Short Abstract:
Virtually all digital IC platforms today are based on flexible programmable processor cores, with a trend towards Multi/Many-core architectures comprising 10-100 cores. This trend is imposed by high performance and power/energy efficiency demands. Specifically in competitive embedded application domains like smartphones, wireless infrastructure, and automotive, there are tight efficiency constraints on power, energy, timing, design cost, and production cost of the underlying HW platforms. The need for flexibility and efficiency leads to heterogeneous platform architectures, composed of off-the-shelf (yet partially customizable) IP cores, like RISCs, and custom application-specific processors, such as DSP or security accelerators. Moreover, these cores communicate over complex on-chip interconnect and memory subsystem architectures. These trends impose huge challenges for ICT system and semiconductor industry. Novel design methodologies and tools are required for managing the skyrocketing HW platform design complexity, while simultaneously optimizing systems and components for performance, power, and costs. Furthermore, migrating legacy application software code or firmware as well as developing and debugging new software for highly parallel HW platforms causes a significant design productivity gap. This course presents various advanced system-level design methodologies in a practice-oriented way, intended to enable industrial embedded systems engineers to manage the complexity of current and future HW/SW multicore devices and to achieve predictable and competitive results in shorter time. Topics include: Software compilation techniques, System-on-Chip design methodology, power optimization, Virtual Prototyping and simulation, and Application Specific Processor Design. Furthermore, a brief outlook on hardware security issues will be provided.

Course Contents in brief:

1. Multiprocessor Systems-on-Chip
2. Electronic System Level Design
3. SoC architecture exploration and power estimation
4. Virtual Prototyping
5. Multicore programming tools
6. Application-specific processing elements (ASIPs)

Schedule:

TBD

Hours:
18-20 hours (5 credits)

Room:

From remote by using Google Meet. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:
Information theory has been celebrated mostly for its success in modelling information sources and communication systems, however it can be applied to many other application areas always bringing useful and somewhat unexpected insights. The relationship between information theory and statistics is one of such cases. As a matter of fact, some of the most famous results in hypothesis testing and large deviation theory can be revisited from an information theoretic perspective leading to new findings and a deeper understanding of the involved concepts. It is the goal of this course to provide a brief introduction to the use of information theoretic concepts in statistics and, conversely, use some well-known results in statistics to revisit the most celebrated theorems of information theory. In the last part of the course the concepts developed in the first lectures are applied to build a theory of adversarial hypothesis testing, aiming at determining the ultimate achievable performance when hypothesis testing is cast into an adversarial setting encompassing the presence of an adversary aiming at inducing an error in the test. The links of the theory with adversarial machine learning and AI security will discussed with examples drawn from the multimedia forensics field.

Course Contents in brief:

1. Information theory in a nutshell
2. The method of types and its relationship with statistics
3. Application to large deviation theory and hypothesis testing
4. Adversarial hypothesis testing: an information theoretic perspective
5. Links with adversarial machine learning and multimedia forensics

Schedule:

22,23,25,26 February, 1 March 2021 - 9:00-13:00

Hours:
16 hours (4 credits)

Room:
From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:
The course has the purpose of providing an understanding of the foundations of quantum technologies, with reference, in particular, to quantum computing and quantum communication. Focus will also be on the nature of the problems for which they can provide a significant advantage in comparison to their classical counterparts. After reviewing a few basic concepts in quantum mechanics, we will introduce the qubit and the basic single- and two-qubit operators. We will then discuss the no-cloning theorem and quantum teleportation as well as the general implementation of a quantum algorithm with a quantum network.  Dense coding, the Deutsch algorithm, the Shor algorithm and its application to number factorization will be covered in detail. An example of a basic algorithm for quantum cryptography will also be presented. We will conclude with an overview of the status of the art form the experimental point of view and of the most promising implementations for a quantum computer.

Course Contents in brief:

1. States in quantum mechanics, superposition of states, entanglement, Bell’s theorem, Dirac notation
2. Concept of a universal quantum computer vs. a universal classical computer
3. Qubits, single- and two-qubit operators (identity, NOT, Y, Z, Hadamard, generic rotation, controlled NOT)
4. No-cloning theorem, teleportation, dense coding scheme
5. Oracles and Deutsch algorithm
6. Shor algorithm and large number factorization
7. Quantum cryptography
8. Silicon-based quantum computer
9. Quantum computer based on superconducting qubits

Schedule:

• Monday, March 15th, 13:20-16:20
• Tuesday, March 16th, 8:30-11:30
• Wednesday, March 17th, 14:30-17:30
• Thursday, March 18th, 14:30-17:30
• Friday, March 19th, 9:30-13:30

Hours:
16 hours (4 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short 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).

Course Contents in brief:

1. A general dynamic estimation problem in state-space form
2. Recursive Bayesian filtering
3. Kalman filter as recursive Bayesian filter in the linear Gaussian case
4. Beyond the Kalman filter: nonlinear filters for nonlinear and/or non-Gaussian estimation problems (extended Kalman filter, unscented Kalman filter, particle filter, Gaussian sum filter).
5. Theoretical limits on the quality of estimation
6. Applications to surveillance, robotic navigation and environmental data assimilation.

Schedule:

1. January 12, 2021 – 9:30-12:45
2. January 14 2021 –  9:30-12:45
3. January 19, 2021 – 9:30-12:45
4. January 21, 2021 – 9:30-12:45

Hours:
18 hours (4 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:

The advent of Multi-access Edge Computing (MEC) is radically transforming the design of network infrastructure and enabling the feasibility and the success of several industrial verticals, including healthcare, IoT, automotive, smart cities, media and entertainment. MEC relies on the flexible use of a pool of shared network and computing resources, which need to be placed at the edge of the network, and on the softwarization of hardware network functions (NFV) to achieve the different and the stringent network requirements.

MEC is today a very hot research topic in networking, it is subject to a strong standardization process, and is at the heart of 5G network, which has been holistically designed with MEC.  The course focuses on all these issues organizing the contents according to the sequence briefly described below.

Course Contents in brief:

1. Why: analysis of the main industrial verticals requiring and benefiting from a MEC approach. This section will be helpful to an audience with different background in engineering;
2. How: comprehensive description of most important ETSI standardization contributions to MEC, mainly focusing on the emerging network architecture and the integration with 5G networks;
3. Where should MEC facilities be deployed: analysis of MEC provisioning in terms of flexible resource deployment, which is achieved through Network Function Virtualization, and agile migration of several Virtual Network Functions;
4. Case studies: in-depth analysis of realistic scenarios in two industrial verticals (automotive and media gaming) to show how they can leverage on MEC to achieve bandwidth, latency and computing requirements.

Schedule:

1. Day1 – 14 June 2021 - 9.30-13.00
2. Day2 – 15 June 2021 - 9.30-13.00
3. Day3 – 21 June 2021 - 9.30-13.00
4. Day4 – 22 June 2021 - 9.30-13.00
5. Day5 – 23 June 2021 - 9.00-13.00

Hours:
20 hours (5 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:
This course is an advanced course focusing on the intersection of Statistics and Machine Learning. The goal is to study modern statistical methods for supervised and unsupervised learning, and the underlying theory for those methods. Numerous illustrations in the context of signal / image processing will be provided. The students are expected to have basic knowledge of:

1. linear algebra
2. functional analysis.
3. basic probabilities concepts
4. foundations of machine learning concepts.

Course Contents in brief:

1. Reminders on multivariate statistics: ML, Bayesian theory, hypothesis testing, linear regression;
2. Robust theory: robust estimation, robust regression approaches;
3. Clustering: hierarchical clustering, DBSCAN, HDBSCAN algorithms;
4. Mixture models: GMM and more general distributions mixture, distribution fitting, parameters estimation, EM algorithms;
5. Model selection;
6. Applications to image and signal processing;

Schedule:

1. Day1 - 26 April 2021 - 9:30-12:00, 13:30-16:00
2. Day2 - 27 April 2021 - 9:30-12:00, 13:30-16:00
3. Day3 - 28 April 2021 - 9:30-12:00, 13:30-16:00
4. Day4 - 29 April 2021 - 9:30-12:00, 13:30-16:00

Hours:
16 hours (4 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:
This PhD course focuses on Web search and discusses the challenges in the three main areas of Web search: i) crawling, ii) indexing, and iii) query processing. The course introduces each area by discussing the state of the art in the field and by presenting the open research questions. The emphasis of the course is on query processing, an area where machine learning provides an important contribution to advance the state of art. After an introduction of the different query processing techniques, the course i) introduces supervised techniques explicitly focused to target the ranking problem, ii) discusses several efficiency/effectiveness trade-offs in query processing and iii) analyse several related optimization techniques. The course will also provide an overview of the query processing techniques employing deep neural networks. Two hands-on sessions will cover indexing and query processing of public Web collections.

Course Contents in brief:

1. Modern Web Search ( 4 hours )
   1. The web: history, peculiarities and the importance of the search.
   2. Anatomy of a modern Web search engine: crawling, indexing, query processing.
   3. Crawling: definition and application. Architecture of a modern crawler.
   4. Challenges in crawling the Web
2. Fast Indexes for Web search ( 4 hours )
   1. Data structures for indexing Web documents
   2. Modern techniques for efficient text retrieval
   3. Challenges in indexing the Web
   4. Hands On : Indexing and basic query processing on a public Web collection
3. Machine learning in modern query processors ( 8 hours )
   1. Machine learning approaches for IR: Learning to Rank
   2. Efficiency/Effectiveness Trade-offs, Cascading Architectures
   3. Neural information retrieval
   4. Hands On : Learning to Rank and Deep Neural Networks for efficient Web search

Schedule:

1. 05/07/2021 - 9:00 - 13:00
2. 06/07/2021 - 9:00 - 13:00
3. 07/07/2021 - 9:00 - 13:00
4. 08/07/2021 - 9:00 - 13:00