Phd courses 2020

Cyber-Physical Systems and Cloud for Smart Industry

Lecturer:  Daniele Mazzei (UNIPI)

Period: February 2020

IOT, Industrial IOT, sensors and edge gateway are disruptively changing our factories connecting machines, people and apparatus to cloud architectures aimed at supporting the management in the improvement of production efficiency. The course will explore different Industrial technologies highlighting the architectural paradigms and strategy used for the design, development, deploy and maintenance of smart factory.

Scheduling:

  • 3-7 and 10-12 February 2020, hr: 11-13, Sala Seminari EST

 

Design-by-Contract and Behavioral Types

Lecturers:  Roberto Bruni (UNIPI), Hernan Melgratti (University of Buenos Aires)

Period: February

The design-by-contract approach promotes the usage of executable specifications to describe the mutual obligations that regulate the interaction between different modules. Contracts are embedded in code and checked at runtime to help developers identify faulty modules. Behavioral types instead provide a description of the interaction aspects of a program and are used to specify the communication protocols that regulate the communication among distributed components. In this course we will present the current approaches for combining contracts and behavioral types. We will cover the following topics: Design-by-Contract approach (DbC), Higher-order contracts (Findler & Felleisen), Binary Session Types, Chaperone contracts for session types, Multiparty Session Types (MST), DbC for MST, Dependent Session Types.

Scheduling:

  • 10, 11, 13, 14, 18, 20, 21, 24, 25, 27 February 2020, hr: 14-16, Sala Seminari OVEST

 

Theory of Computing

Lecturer:  Ugo Montanari (UNIPI)

Period: February-May 2020, see course page for details.

Some basic properties of models of computation are studied, like operational and abstract semantics, typing, higher order, concurrency, interaction. Algebraic semantics and elementary category theory are employed, but no prerequisites are required except for some elementary knowledge of logic and algebra. 

Scheduling:

  • starting lecture Friday 28th February 11:00-13:00, Room Fib L1

 

Shape Analysis and Geometry Processing

Lecturers:  Paolo Cignoni (CNR ISTI), Fabio Ganovelli (CNR ISTI), et alii

Period: February/March

The course deals with 3D shape analysis and geometric processing, which are key topics in Computer Graphics, Vision and Digital Fabrication. The course introduces well established and recent geometric concepts and tools (curvature and geodesics, spectral methods, computational topology, topological persistence), emphasizing the discrete and computational viewpoint. In the last decades these topics have attracted the attention of many researchers thanks to the fast-paced growth of the research in 3D, pushed by the technological advances in gaming, autonomous navigation, biomedical, digital cultural heritage, 3D printing, etc. The aim of the course is to let the math student appreciate how geometry is successfully used in applied sciences; and to provide the computer science student the mathematical basis needed to efficiently tackle open issues in such a lively research field.

Computer Science PhD students are suggested to take the module of 16h concerning more on the algorithmic aspects, just talk to the lecturers.

Scheduling:

 

Reinforcement Learning

Lecturer:  Davide Bacciu (UNIPI)

Period: April 2020

The course will introduce students to the fundamentals of reinforcement learning (RL). The course will start by recalling the machine learning and statistics fundamentals needed to fully understand the specifics of RL. Then, it will introduce RL problem formulation, its core challenges and a survey of consolidated approaches from literature. Finally, the course will cover more recent reinforcement learning models that combine RL with deep learning techniques.

Space will be devoted to present RL applications in areas that are relevant for students of industrial and information engineering, such as robotics, pattern recognition, life sciences, material sciences, signal processing, computer vision and natural language processing.  The course will leverage a combination of theoretical and applicative lectures.

A student successfully completing the course should be able to lay down the key aspects differentiating RL from other machine learning approaches. Given an application, the student should be able to determine (i) if it can be adequately formulated as a RL problem;  (ii) be able to formalise it as such and (iii) identify a set of techniques best suited to solve the task, together with the software tools to implement the solution.

Scheduling:

 

Programming Tools and Techniques in the Pervasive Parallelism Era

Lecturers:  Marco Danelutto (UNIPI), Patrizio Dazzi (CNR ISTI)

Period: May 2020

The course covers techniques and tools (already existing or that are in the process of being moved to mainstream) suitable to support the implementation of efficient parallel/distributed applications targeting small scale parallel systems as well as larger scale parallel and distributed systems, possibly equipped with different kind of accelerators. The course follows a methodological approach to provide a homogeneous overview of classical tools and techniques as well as of new tools and techniques specifically developed for new, emerging architectures and applicative domains. Perspectives in the direction of reconfigurable coprocessors and domain-specific architectures will also be covered.

Scheduling:

  • 19, 20, 21, 22 and 25, 26, 27, 28 May; hr. 11:00 – 13:00

 

Quantum Computing

Lecturers:  Fabrizio Luccio, Massimo D’Elia, Patrizia Gianni, Fabio Gadducci (UNIPI), Simone Montangero (UNIPD).

Period: Deferred to the next academic year because of COVID-19’s emergency

Eight lectures of two hours each, presented by scholars of different disciplines and directed mainly to Ph.D. students of Computer Science. An introduction of the whole course will be given by Fabrizio Luccio, including an overview of the foreseeable structure of a quantum computer. Then a set of lectures will be given by Simone Montangero and Massimo D’Elia on the principles of quantum mechanics and their applications in quantum computation and information. Patrizia Gianni will present Shor’s and Grover’s quantum algorithms and their extensions. Fabio Gadducci will discuss formal methods for quantum processes, including category theory and process representation.

Under the guidance of one of the lecturers, each student will select some research material to be thoroughly studied and reported in a final discussion, possibly with the submission of a written report.

 

Algorithmic Tools for Massive Network Analytics

Lecturers:  Roberto Grossi (UNIPI), Paolo Boldi (UNIMI), Alessio Conte (UNIPI), Andrea Marino (UNIFI)

Period: Deferred to the next academic year because of COVID-19’s emergency

Many complex real-world phenomena are naturally modeled as networks: social interactions, internet/web, knowledge bases, power grids, financial transactions, biological pathways, to name a few. Network analysis aims at finding interesting properties hidden in the linked structure. This analysis is challenging from a computational point of view due to the sheer size of the networks and the combinatorial nature of the corresponding graph problems. These lectures will present some powerful combinatorial algorithms for graphs and provide an algorithmic toolkit to store, analyze and extract meaningful patterns from large-scale networks.

 

Algorithmic Foundations on Sensors, IoT and Cyber-Physical Systems

Lecturers:  Stefano Chessa (UNIPI), Sajal Das (University of Missouri)
Period: Deferred to the next academic year because of COVID-19’s emergency

We live in an era in which our physical and personal environments are becoming increasingly intertwined and smarter due to the advent of pervasive sensing, wireless communications, computing, and actuation technologies. Indeed our daily living in smart cities and connected communities will depend on a wide variety of smart service systems and cyber-physical infrastructures, such as smart energy, transportation, healthcare, supply chain, etc. Alongside, the availability of low-cost wireless sensors, Internet of Things (IoTs) and rich mobile devices (e.g., smartphones) are empowering humans with fine-grained information and opinion collection through crowd-sensing about events of interest, resulting in actionable inferences and decisions. This synergy has led to the cyber-physicalsocial (CPS) convergence with human in the loop that exhibits complex interactions, interdependencies and adaptations between engineered/natural systems and human users with a goal to improve quality of life and experience in what we call smart living. However, the main challenges are posed by the scale, heterogeneity, big data, social dynamics, and resource limitations in context recognition and situation awareness using sensors, IoTs and CPS networks. This course will present unique research challenges from smart sensing to smart living, followed by algorithms on sensors, IoTs and CPs. Specifically, novel algorithms, frameworks and models will be designed for energy-efficient data gathering, data fusion, sensor coverage and connectivity, security and trustworthiness, and information quality in multi-modal context recognition and mobile crowd sensing. In additional algorithm and model development, case studies and experimental results from smart energy, vehicular  CPS and smart healthcare applications will be presented. Each topic of the course will be concluded with directions for future research.