{"id":889,"date":"2019-09-15T11:49:19","date_gmt":"2019-09-15T09:49:19","guid":{"rendered":"https:\/\/dottorato.di.unipi.it\/phd-courses-2020\/"},"modified":"2021-11-19T19:43:24","modified_gmt":"2021-11-19T18:43:24","slug":"phd-courses-of-previous-years","status":"publish","type":"page","link":"https:\/\/dottorato.di.unipi.it\/en\/phd-programme\/teaching\/phd-courses-of-previous-years\/","title":{"rendered":"Phd courses of previous academic years"},"content":{"rendered":"

a.y. 2020\/2021<\/h2>\n

Rigorous Modelling Methods for Software Engineering<\/h5>\n

Lecturer:\u00a0<\/strong> Egon Boerger<\/p>\n

The course introduces a rigorous method to model software-intensive systems at different levels of abstraction which can be related by provably correct refinement statements that faithfully link application domain to programmers\u2019system views. The used technical concept is that of Abstract State Machines, a form of pseudo-code which extends finite state machines to programs working on abstract data types at any level of abstraction and can be given a simple precise semantics. The method is illustrated by modeling real-life case studies in various domains: control systems, Java\/C# program interpreter, thread handling, programming patterns, process networks (distributed network algorithms, routing protocols in mobile ad hoc networks (AODV), weak shared memory db management (Cassandra)), business processes (WebServiceProvider, SardexBusinessLogic), concurrent systems. Participants will be invited (and supported) to try out their own models for concepts they are working on.<\/p>\n

Foundation of Computing<\/strong><\/a><\/h5>\n

Lecturers:\u00a0<\/strong> Ugo Montanari<\/p>\n

(Note that this course is open to Ph.D students but it is actually a MS course. It therefore consists of aroung 48 hours of classes spread from mid February till mid of May, with only 4 hours of classes per week.)<\/em><\/p>\n

Students learn the essential properties of some widely employed models of computation for higher order, concurrency, interaction, mobility. Algebraic semantics and elementary category theory are employed.<\/p>\n

Basic Principles of Security and Blockchain<\/h5>\n

Lecturers:\u00a0<\/strong> Laura Ricci, Fabrizio Baiardi<\/p>\n

Blockchain technology has a great potential to radically change our socio-economical systems by guaranteeing secure transactions between untrusted entities, reducing their cost and simplifying many processes. Such a technology is going to be exploited in many different areas like IoT, social networking, health care, electronic voting and so on. Even if several applications are already leveraging its disruptive potentials, the basic principles of this technology deserve further research. Furthermore, this technology is strictly related to the general theme of the security of information system. For this reason, the course will present, in the first part, some basic concepts of information system security, then it will discuss the basic pillars of blockchains, like consensus and game theory. The first 6 hours will deal with Security issues: ICT Risk Assessment, Adversary Simulation, MITRE Attack Matrix, Offensive Security and Attack Platform, ICT Resilience. The other 10 hours will deal with Blockchain issues: Fault Tolerance & Paxos, Distributed Consensus: basic definitions, Byzantine Agreement, Eventual Consistency, Game Theory, Distributed storage, Blockchain: consensus models.<\/p>\n

Design Thinking for Scientists<\/h5>\n

Lecturers:\u00a0<\/strong> Daniele Mazzei<\/p>\n

Design Thinking is an iterative process in which we seek to understand the user, challenge assumptions, and redefine problems in an attempt to identify alternative strategies and solutions that might not be instantly apparent with our initial level of understanding. Design Thinking provides a solution-based approach to solving problems. It is a way of thinking and working as well as a collection of hands-on methods. Design Thinking is not an exclusive property of designers all great innovators in literature, art, music, science, engineering, and business have practiced it. The course will introduce to design thingin\u00a0and human centered design approaches giving PhD students an overall view on how this methods can be applied to the research context.<\/p>\n

Reinforcement Learning<\/a><\/h5>\n

Lecturers:\u00a0<\/strong> Davide Bacciu<\/p>\n

The course will introduce students to the fundamentals of reinforcement learning (RL). We will start by introducing RL problem formulation, its core challenges and a survey of consolidated approaches from literature, including dynamic programming, value-function learning and policy learning. We will then cover model-based RL and exploration strategies. Finally, the course will discuss more recent reinforcement learning models that combine RL with deep learning techniques.<\/p>\n

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;\u00a0 (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.<\/p>\n

Program analysis for verification<\/h5>\n

Lecturers:\u00a0<\/strong> Letterio Galletta (IMT), Pierpaolo Degano<\/p>\n

This course provides an overview of advanced techniques for analyzing software and for checking their properties. The lectures will present the theory underlying various techniques as well as demonstrations of software tools that implement them. Moreover, the lecture will illustrate how advanced static analysis techniques are applied in the field of computer security, Internet of Things, smart contract and safe AI. The course will mix theory and practice; students will formalize analyses and prove them correct, will implement analyzers from scratch, and also will experience state of the art static analysis tools.<\/p>\n

Programming Tools and Techniques in the Pervasive Parallelism Era<\/h5>\n

Lecturers:\u00a0<\/strong> Marco Danelutto, Patrizio Dazzi (CNR ISTI)<\/p>\n

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,\u00a0emerging architectures and applicative domains. Perspectives in the direction of reconfigurable coprocessors and domain-specific architectures will also be covered.<\/p>\n

Quantum Computing<\/h5>\n

Lecturers:\u00a0<\/strong> Fabrizio Luccio, Simone Montangero (UNIPD), Patrizia Gianni, Roberto Bruni, Fabio Gadducci<\/p>\n

Nine lectures of two hours each, presented by scholars of different disciplines and directed mainly to Ph.D. students of Computer Science. Fabrizio Luccio introduce the whole course, with a computer scientist\u2019s overview of quantum computing. Then a set of lectures will be given by Simone Montangero on the principles of quantum mechanics and their applications in quantum computation and information. Patrizia Gianni will present Shor\u2019s and Grover\u2019s quantum algorithms and their extensions. Finally, Roberto Bruni and Fabio Gadducci will discuss Quantum programming languages.<\/p>\n

Final evaluation of the participants: Study and written presentation of scientific material, to be agreed with one of the lecturers.<\/p>\n

Algorithmic Tools for Massive Network Analytics<\/h5>\n

Lecturers:\u00a0<\/strong> Roberto Grossi, Paolo Boldi (UNIMI), Alessio Conte, Andrea Marino (UNIFI), Giuseppe Italiano (LUISS), Giordano Da Lozzo (Roma Tre), Fabio Vandin (UNIPD)<\/p>\n

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.<\/p>\n

Monte Carlo methods and sampling for computing<\/h5>\n

Lecturers:\u00a0<\/strong> Francesco Banterle (CNR)<\/p>\n

This course introduces students to Monte Carlo methods and sampling techniques with a focus on visual computing. These are crucial to accelerating the computations of a variety of computational simulations where we need to draw high-quality samples or to integrate a complex multi-dimensional function such as physically-based rendering for computing the radiance of buildings, estimating the price of options, or how epidemics spread out. At the end of this course, students will have both theoretical and practical tools that they can apply to a variety of problems to achieve high-quality solutions. During the course, students will see and study successful examples of this beautiful theory to visual computing; e.g., visual processing, computational finance and biology.<\/p>\n

Algorithmic Game Theory<\/h5>\n

Lecturers:<\/strong> Giancarlo Bigi<\/p>\n

The lectures aims at introducing the main paradigms of game theory within an algorithmic perspective in order to analyse the behaviour of multi-agent systems and phenomena. Suitable mathematical models and tools will be provided to help the understanding of inner mechanisms of competition and cooperation, threads and promises. A few application to problems from different areas are provided upon the students’ interests (for instance, computational economics, traffic and social networks, security, distributed computing and machine learning). A critical analysis of the power and limitations of game theory is addressed as well.<\/p>\n

a.y. 2019\/2020<\/h2>\n
Cyber-Physical Systems and Cloud for Smart Industry<\/h5>\n

Lecturers:\u00a0<\/strong>Daniele Mazzei (UNIPI)<\/p>\n

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.<\/p>\n

Design-by-Contract and Behavioral Types<\/h5>\n

Lecturers:\u00a0<\/strong>\u00a0Roberto Bruni (UNIPI), Hernan Melgratti (University of Buenos Aires)<\/p>\n

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.<\/p>\n

Theory of Computing<\/h5>\n

Lecturer:\u00a0<\/strong>\u00a0Ugo Montanari (UNIPI)<\/p>\n

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.<\/p>\n

Shape Analysis and Geometry Processing<\/h5>\n

Lecturers:\u00a0<\/strong>\u00a0Paolo Cignoni (CNR ISTI), Fabio Ganovelli (CNR ISTI), et alii<\/p>\n

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.<\/p>\n

Reinforcement Learning<\/h5>\n

Lecturer:\u00a0<\/strong>\u00a0Davide Bacciu (UNIPI)<\/p>\n

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.<\/p>\n

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. \u00a0The course will leverage a combination of theoretical and applicative lectures.<\/p>\n

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;\u00a0 (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.<\/p>\n

Programming Tools and Techniques in the Pervasive Parallelism Era<\/h5>\n

Lecturers:\u00a0<\/strong>\u00a0Marco Danelutto (UNIPI), Patrizio Dazzi (CNR ISTI)<\/p>\n

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,\u00a0emerging architectures and applicative domains. Perspectives in the direction of reconfigurable coprocessors and domain-specific architectures will also be covered.<\/p>\n

\n

a.y. 2018\/2019<\/h2>\n

Matrix computations: a quick overview and some applications<\/h5>\n

Lecturer:\u00a0<\/strong>\u00a0Gianna Del Corso (UniPI), Federico Poloni (UniPI)<\/p>\n

We overview some useful techniques of numerical linear algebra with several applications to data analysis. The theoretical aspects will be complemented with a few examples from applications. Theory: review of singular value decomposition and other factorizations, Kronecker products, Fast Fourier transform, nonnegative matrix factorizations, nonnegative matrices and M-matrices. Applications: image deblurring \/ denoising, vector space model, latent semantic indexing, clustering, Pagerank \/ invariant measures of Markov chains, Centrality measures.<\/p>\n

Genomic data analysis with applications<\/h5>\n

Lecturer:\u00a0<\/strong>\u00a0Ugo Borello (Biologia, UniPI), Filippo Geraci (IIT CNR)<\/p>\n

The goal of the course is to introduce the main framework for computational analysis of genomic data: from differential analysis to integration of omics data and biological networks.<\/p>\n

We focus on data generated by Next Generation Sequencing (NGS) technology and we discuss the impact of these technologies on biology and bioinformatics.<\/p>\n

NGS data analysis is the first step to differential analysis, which is the task of measuring a certain (often large) number of variables in two different populations of biological samples and deriving the subset of variables that are statistically different. We will see several techniques and applications of differential analysis: from differential expression to the discovery of biomarkers. To this end, we will also review the basics of clustering, classification and feature selection.<\/p>\n

Subsequently, we will introduce the problem of multi-omics data analysis, its advantages and challenges. We will focus on the integration of data coming from different technologies reviewing different state-of-the-art methods.<\/p>\n

In the last part of the course, we will introduce the network structure of biological data. We will show how some problems in graph theory are closely related to biological problems. For example, we will see how the problem of finding protein complexes can be easily mapped into the problem of finding communities in a graph.<\/p>\n

Polyhedral Combinatorics<\/h5>\n

Lecturer:\u00a0<\/strong>\u00a0Laura Galli (UniPI), Adam Letchford (University of Lancaster, UK)<\/p>\n

Many important optimization problems arising in practical applications are discrete or combinatorial.\u00a0 Indeed, such problems arise in fields as diverse as operational research, statistics, computer science, engineering and the physical sciences.\u00a0 Although most combinatorial problems are theoretically intractable (\u201cNP-hard\u201d), algorithms and software have improved dramatically during the past couple of decades, to the point where real-life instances with thousands of variables and constraints can now be solved to proven optimality or near-optimality.\u00a0 One of the key components of this success is the study of certain convex polyhedra.\u00a0 The study of such polyhedra is known as polyhedral combinatorics.\u00a0 This course gives an introduction to this fascinating topic, covering theory, algorithms, applications and software. Topics covered: \u2013 Recap on Combinatorial Optimisation \u2013 Modelling Problems as Integer Programs \u2013 Classical Solution Approaches \u2013 Fundamentals of the Polyhedral Approach \u2013 Some Simple Polyhedra \u2013 Polyhedra Associated with NP-Hard Problems \u2013 Algorithms and Implementation \u2013 Applications<\/p>\n

Data Stream Processing from the Parallelism Perspective<\/h5>\n

Lecturer:\u00a0<\/strong>\u00a0Gabriele Mencagli (UniPI),\u00a0Matteo Andreozzi, ARM<\/p>\n

An ever-growing number of devices are capable of sensing the world by producing huge flows (data streams) of information regarding the users and the environment. A large set of applications need efficient processing techniques to extract insights and complex knowledge from such a massive and transient data deluge. Furthermore, to keep the processing in real-time, existing systems must expose parallel features to adapt the algorithms and the way the processing is performed to unpredictable and time-varying input rates and workloads.<\/p>\n

The computing paradigm enabling the processing in those scenarios is called Data Stream Processing, which comprises in its most general meaning other closely-related paradigms like Event Processing and Complex Event Processing. The course aims at giving a solid background on this research field, with an introduction on the theory behind stream processing, an overview of the most famous Stream Processing Systems (e.g., Apache Storm, Apache Flink and Spark Streaming) and the most recent results achieved by the scientific community in terms of optimizations, cost models and patterns. Furthermore, in the final part of the course, the FastFlow and WindFlow parallel libraries will be described in details, by outline the research advancements for ultra-low latency streaming systems targeting multi-core environments and C++ programming.<\/p>\n

Theory and Practice of Learning From Data<\/h5>\n

Lecturer:\u00a0<\/strong>\u00a0Luca Oneto (UniPI)<\/p>\n

This course aims at providing an introductory and unifying view of information extraction and model building from data, as addressed by many research fields like Data Mining, Statistics, Computational Intelligence, Machine Learning, and\u00a0Pattern Recognition. The course will present an overview of the theoretical background of learning from data, including the most used algorithms in the field, as well as practical applications in industrial areas such as transportation,\u00a0manufacturing, etc.<\/p>\n

\n

a.y. 2017\/2018<\/h2>\n

An introduction to Deep Learning<\/h5>\n

Lecturer:\u00a0<\/strong>Antonio Gull\u00ec (Google)<\/p>\n

The goal of this course is to provide an handson introduction to Deep Learning. The traditional concepts used in neural networks are introduced, together with modern convolutional networks, generative adversarial networks, recurrent networks, and autoencoders. In addition, we will look at Reinforcement Learning and its application \u00a0to AI Game Playing, a popular direction of research and application of neural networks. All the examples will be coded in Keras a python high-level framework running on the top of Google\u2019s Tensorflow, Microsoft\u2019s CNTK, Amazon\u2019s MLXnet, and University of Montreal\u2019s theano.<\/p>\n

Graph Mining Algorithms<\/h5>\n

Lecturer:\u00a0<\/strong>Andrea Marino (UniPI)<\/p>\n

In this course we will focus on the computation of several graph topological measures, for which polynomial-time algorithms are available and in \u201cpractice\u201d are not useful, due to the huge size of the networks to be analysed. We will consider efficient algorithms for the following problems: computing diameter, distance distribution, average distance, and\u00a0centrality measures,\u00a0counting triangles and probabilistic counting in general, clustering and\u00a0graph pattern mining.<\/p>\n

Shape Analysis and Geometric Processing<\/h5>\n

Lecturer:\u00a0<\/strong>Paolo Cignoni, Fabio Ganovelli, Daniela Giorgi, Maria Antonietta Pascali (ISTI \u2013 CNR)<\/p>\n

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.<\/p>\n

Skills Boosting for New (Research) Horizons<\/h5>\n

Lecturer:\u00a0<\/strong>Paolo Ferragina (UniPI),\u00a0Michele Padrone (UniPI),\u00a0Davide Morelli (BioBeats),\u00a0Flavio Tosi (Business Exploration),Camilla Van den Boom (Eindhoven University), Nicola Redi (Venture Factory), Ray Garcia (Buyont Capital, NY)<\/p>\n

This course aims to help PhDs in Computer Science to scale and enrich their technical and scientific abilities with experiences, (soft) skills and networking useful for your future either as a researcher, or as an entrepreneur, or as a chief software engineer, or as the CTO of a successful company ! It is indeed very well known that the best talents are found at the\u00a0intersection<\/em>\u00a0of community\/networking, skills, right mindset (good attitude and self-motivated) and technical competency. The course is intended to have PhD students to ACT on their own and be able to evaluate their scientific achievements from other perspectives, find new avenues for grant funds for research and developing their ideas.<\/p>\n

Formal Methods for Program Verification<\/h5>\n

Lecturer:\u00a0<\/strong>Massimo Bartoletti (UniCa), Pierpaolo Degano (UniPI), Dino Di Stefano (Facebook), Gianluigi Ferrari (UniPI), Letterio Galletta (UniPi)<\/p>\n

We will focus on a repertoire of formal techniques that prevent applications and systems to misbehave.
\nThis issue is particularly relevant because now-a-days software is everywhere. Thus it has to be designed to properly work in open, highly mutable environments of different nature, including cyber-physical ones.Software must then come with guarantees on its behaviour, robustness, reliablity, security, scalability, and efficiency. Precise guarantees can only be offered by a formal proof of software correctness, carried on a sound abstract model of the system in hand. We will also present and possibly experiment with mechanical tools, used in industries and academia, which support program verification.<\/p>\n

Distributed Systems Security<\/h5>\n

Lecturer:\u00a0<\/strong>Roberto di Pietro (College of Science and Engineering, Dhoa-Quatar)<\/p>\n

This course focuses on fundamental and advanced concepts in Distributed Systems, addressing their foundations, current technologies, and security aspects. Topics include, but are not limited to, distributed hash tables (peer-to-peer systems), failure detectors, synchronization, election, distributed agreement, consensus, gossiping, replication, key-value stores, NoSQL, blockchain technology. These topics are discussed in the context of real-life and deployed systems such as clouds and datacenters, databases, peer to peer systems, clusters, cryptocurrencies.<\/p>\n

Modeling methods<\/h5>\n

Lecturer:\u00a0<\/strong>Egon Boerger (UniPI)<\/p>\n

The course explains two\u00a0major methods for design and analysis of\u00a0 computational systems, known as ASM (Abstract State Machines)\u00a0and Event-B method. The focus will be on the refinement method which permits to link abstract models\u00a0(specifications) to code in a provably correct way.<\/p>\n

\n

a.y. 2016\/2017<\/h2>\n

Introduction to Network Science<\/h5>\n

Lecturer:\u00a0<\/strong>Prof. Janos Kertesz (Central European University, Budapest)<\/p>\n

Syllabus:\u00a0Introduction: Complex systems and complex networks. Percolation model, scale invariance. Basic notions of graph theory. Erd\u0151s-R\u00e9nyi graphs, Watts Strogatz model, configuration model, network growth models. Weighted and signed networks. Motifs and modules. Error tolerance and vulnerability against intentional attacks. Spreading problems.<\/p>\n

Categories and Quantum Informatics<\/h5>\n

Lecturer:\u00a0<\/strong>Prof.Chris Heunen (LFCS Univeristy of Edinburgh)<\/p>\n

The course begins by introducing the idea behind category theory and the breadth of its scope. Why would it be a good idea to abstract away from specific hard-coded set-theoretic structures, and have compositional denotational semantics, in general? Illustrations from functional programming and categorical methods in logic are given.<\/p>\n

We then focus more specifically on monoidal categories. Specific attention is paid to the graphical calculus, which makes the topic visually apparent. The student learns to graphically manipulate algebraic objects such as monoids and Frobenius structures. This allows perfectly rigorous proofs of correctness, and shows the information flow of a protocol that is often hidden behind superfluous details.<\/p>\n

Throughout the course, the abstract material is linked to quantum informatics. We will categorically model notions typically thought to belong to quantum theory, such as entanglement, no-cloning, teleportation, and complementarity. But it will turn out some of these notions also make perfect sense in other settings. For example, the very same categorical description of quantum teleportation also describes classical encryption with a one-time pad. We identify characteristics of classical and quantum information, aiming to equip students to choose the right tools and techniques for future problems they may encounter.<\/p>\n

One day of Distributed Algorithms and Graph Mining<\/h5>\n

Lecturer:\u00a0<\/strong>Pierre Fraignaud, IRIF Universit\u00e9 Paris Diderot \u2013 Paris 7<\/p>\n

Design and analysis of secure systems<\/h5>\n

Lecturer:<\/strong>\u00a0Joshua Guttman, Worcester Polytechnic Institute<\/p>\n

First part. The focus will be on the analysis,\u00a0the design, and the refinement of cryptographic\u00a0protocols.<\/p>\n

Second part. The focus will be on information\u00a0flow and non-interference, I want to\u00a0talk about the semantics of limited\u00a0information flow, both in a non\u00a0quantitative and a quantitative\u00a0context, explain how these can be matched with a natural model for\u00a0 distributed systems, and how this\u00a0promotes a compositional view of\u00a0secure system development and\u00a0refinement.\u00a0 I\u2019d also like to indicate\u00a0how the same ideas may apply to\u00a0software via object capability models.<\/p>\n

Distributed Computing and Large Graph Mining<\/h5>\n

Lecturer:<\/strong>\u00a0Pierre Fraignaud, IRIF Universit\u00e9 Paris Diderot \u2013 Paris 7 e PierLuigi Crescenzi<\/p>\n

Distributed computing is a field of computer science that studies distributed systems, i.e., systems in which \u00a0a collection of autonomous computing entities coordinate their actions via some communication medium. The objective of the first part of this course is to introduce the main techniques of algorithm design and analysis for distributed computing, enabling the computing entities aiming at collectively solve a common task. This part of the course will focus on the main computing models, capturing different aspects of distributed computing, including network computing, asynchrony, fault-tolerance, etc. For each model, the course will present the main techniques for (deterministic and probabilistic) algorithm design, and for deriving lower bounds and impossibility results.<\/p>\n

In the second part of the course, we will take a graph mining roundtrip, from theory to practice and back. In particular, we will focus on the computation of several graph topological measures, for which polynomial-time algorithms are available that in \u201cpractice\u201d are not useful, due to the huge size of the networks to be analysed. Switching to \u201ctheory\u201d, we will show that, unfortunately, for these measures no best algorithm exists (under reasonable complexity theory assumptions). This will lead us back to \u201cpractice\u201d: we will describe heuristics that, in the case of real-world graphs, allow us to compute these measures in linear time. These results will finally motivate our return to \u201ctheory\u201d in order to understand the reason why, in practice, these heuristics work so well.<\/p>\n

The Internet of Everything, Everywhere: Methods and Technologies for Internetworking Land, Air and Sea<\/h5>\n

Lecturer:<\/strong>\u00a0Stefano Basagni, Northeastern Univ. Boston<\/p>\n

This course provides an introduction to the latest research directions on methodologies and technologies for the emerging paradigm of the Internet of Things (IoT).<\/p>\n

In particular, we will explore networking challenges and solutions for the three main scenarios where key IoT applications are being defined and developed:<\/p>\n

    \n
  1. Terrestrial networks for IoT, including challenges and trends for smart cities and connected communities;<\/li>\n
  2. Multi-modal communications for underwater wireless networkings, and<\/li>\n
  3. Flying ad hoc networking: Coordinating drones and other form of mechanical birds.<\/li>\n<\/ol>\n
    \n

    a.y. 2015\/2016<\/h2>\n

    Sensor networks, internet of things and smart environments<\/h5>\n

    Lecturer:<\/strong>\u00a0Michele Girolami (CNR Pisa), Alexander Kocian (Dipartimento di Informatica), Stefano Chessa (Dipartimento di Informatica)<\/p>\n