Phd courses 2017

Introduction to Network Science

Title: Introduction to Network Science

Lecturer: Prof. Janos Kertesz (Central European University, Budapest)

Period: January 24 to February 22

Syllabus: Introduction: Complex systems and complex networks. Percolation model, scale invariance. Basic notions of graph theory. Erdős-Rényi 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.

All classes will start at 10 am in Aula Seminari Ovest, Dipartimento di Informatica

Jan 24, Introduction: Complex systems and complex networks
Jan 26 Percolation model and scale invariance
Jan 31 Basic notions for network characterization
Feb 2 Erdős-Rényi model and Watts-Strogatz model
Feb 7 Configuration model
Feb 9 Network growth models
Feb 14 Weighted and signed networks
Feb 17 Motifs and modules
Feb 21 Error tolerance and vulnerability
Feb 22 Spreading on networks

For grade the students have to solve the home work problems and prepare a network analysis on data collected (or downloaded) by themselves.

Categories and Quantum Informatics

Title: Categories and Quantum Informatics

Lecturer: Prof.Chris Heunen (LFCS Univeristy of Edinburgh)

Period: July 10-22

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.

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.

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. 

One day of Distributed Algorithms and Graph Mining

Title: One day of Distributed Algorithms and Graph Mining

Lecturer: Pierre Fraignaud, IRIF Université Paris Diderot – Paris 7

Period: July 6

Nell’ambito del corso di dottorato con lo stesso nome, il giorno 6 luglio si terrà una giornata di lavoro con il seguente programma:

  • 10.00-Seminario: Pierre Fraignaud, IRIF Université Paris Diderot – Paris 7
  • Coffee-break
  • 11.20-Presentazioni dei dottorandi
  • Pausa pranzo
  • 15.00-Seminario: Geppino Pucci, Dipartimento di Ingegneria dell’Informazione — Padova
  • Coffe-break
  • 16.20-Presentazioni dei dottorandi

Design and analysis of secure systems

Title: Design and analysis of secure systems

Lecturer: Joshua Guttman, Worcester Polytechnic Institute

Period: 11-22 september 2017 (from monday to friday SEMINAR ROOM WEST at 11:00 to 13:00)

First part. The focus will be on the analysis, the design, and the refinement of cryptographic protocols.  

Second part. The focus will be on information flow and non-interference, I want to talk about the semantics of limited information flow, both in a non quantitative and a quantitative context, explain how these can be matched with a natural model for  distributed systems, and how this promotes a compositional view of secure system development and refinement.  I’d also like to indicate how the same ideas may apply to software via object capability models.

Distributed Computing and Large Graph Mining

Title: Distributed Computing and Large Graph Mining

Lecturer: Pierre Fraignaud, IRIF Université Paris Diderot – Paris 7 e PierLuigi Crescenzi

Period: 

April 26: 15-17 Sala Riunioni Ovest

April 27: 9-11 Sala Seminari Ovest

April 28: 9-11 TBA

May 2, 3, 4, 5: 9-11 Sala Seminari Ovest

Distributed computing is a field of computer science that studies distributed systems, i.e., systems in which  a 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.

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 “practice” are not useful, due to the huge size of the networks to be analysed. Switching to “theory”, we will show that, unfortunately, for these measures no best algorithm exists (under reasonable complexity theory assumptions). This will lead us back to “practice”: 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 “theory” in order to understand the reason why, in practice, these heuristics work so well.

Title: The Internet of Everything, Everywhere: Methods and Technologies for Internetworking Land, Air and Sea

Lecturer: Stefano Basagni, Northeastern Univ. Boston

Period: June 26, 27, 28, 29 (14:30-16:30), 30 (10:00-14:00) — July 3, 4, 5, 6 (14:30-16:30), 7 (10:00-14:00) always in SALA SEMINARI OVEST

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

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

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