Ph.D. Program

Basic notions of algorithms and complexity; Divide-and-Conquer; Dynamic Programming; Basic data structures: heaps, hash tables; Greedy; Local Search; Network Flow; All-pairs Shortest Paths; NP-complete problems and intractability; PSPACE

Basics on formal models of (concurrent) computations, operational semantics and (labelled) transition systems, regular expressions, basic process algebras. Preliminary concepts of software verification, typical verification flow, common program analysis techniques, decision procedures. Basic concepts of software modelling and analysis with stochastic processes, discrete-time markov processes, and petri nets.

Basic concepts of software development paradigms, application domains, the software life cycle, design of software systems and modeling, software archtiectures. The course also provide some basic knowledge of the tools used to generate software programs from models.

How to write a paper. How to give a scientific talk. How to referee a scientific paper.

General concepts and techniques of software testing. Main approaches for dependability and security validation. Most interesting research challenges in software testing.

This course analyses different kind of analysis for software: static, dynamic and hybrid. Introduce the theoretical principles of static analysis and present some of the approaches and tools used in software practice

Software Quality is defined as a field of study and practice that describes the desirable attributes of software products. In this course we explore some of the methodologies to keep track, monitor and improve software quality with specific application to development and modeling.

This course introduces students to data processing in Python. It first builds the necessary toolset by introducing popular Python libraries for data manipulation/visualization (NumPy, Pandas), applied to simple applications. The toolset is then applied to a more complex case study on the classification of benign and malignant breast cancer. The course concludes presenting KNIME, a popular python-integrated workflow-based language for data analysis.

Basics on process algebras, Basics of behavioural types: the dyadic case, Correctness-by-construction, The Multi-party case, Choreographic approaches, Practical repercussions

Modelling of reactive systems, review of the model checking approach, temporal logics (LTL, CTL, and CTL*), safety and liveness properties of reactive systems, fairness conditions.

Context-bounded analysis and bounded model checking of concurrent programs. Decision procedures and propositional satisfiability.

Modelling, Analysis, and Testing of Probabilistic Systems: Stochastic Process Algebras, Markov Decision Processes, Timed Automata, Queueing Networks

Principles of Parallel and Concurrent Programming, Introduction to Concurrency, Paradigms for Parallel Programming, Interference and Atomicity, Mutual Exclusion, Semaphores, Monitors, Elements of Distributed Programming, Concurrent Programming Languages

Introduction to linear programming; the set cover problem; greedy algorithms and local search; rounding data and dynamic programming; deterministic rounding of linear programs; random sampling and randomized rounding of linear programs; the primal-dual method; techniques to prove the hardness of approximation

Algorithmic Game Theory (Equilibria: dominant versus Nash equilibria; Computational issues of equilibria); Congestion Games; Mechanism design; Computational social choice

Experimental design; measures in algorithmic experiments; algorithm and code tuning; test environment; data analysis

Basics in probability theory; coupon collector and contention resolution; Yao’s principle and the secretary problem; the probabilistic method; randomized min-cut and quicksort; Chernoff bounds; random graphs; smoothed analysis

Graph topological measures. Conditional lower bounds. Heuristics for huge real-world graphs. Probabilistic analysis of some heuristics.

Basic knowledge of the risks run when surfing the web and of the main tools for defending assets and privacy.

Abstract:

This is an introductory course that would be beneficial for any student and does not assume any prior technical knowledge. We will discuss cybersecurity in general and present techniques and tools for navigating securely and for defending assets and privacy.

How to do systematic literature review.