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, applying formal methods to the modelling and analysis of problems using the model checker UPPAAL. 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.

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

Linear Models for Regression; Linear Models for Classification; "No Free Lunch" Theorems; Bias-Variance and Model Selection; Kernel Methods; Support Vector Machines; PAC-Learning and VC-Dimension; Neural 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

Transition systems and linear-time properties (traces, safety, liveness, fairness), Verifying regular linear-time properties (omega-regular languages, Buchi automata, nested depth-first search), LTL model checking (syntax, semantics, automata, model-checking algorithm), CTL model checking (syntax, semantics, CTL versus LTL, model-checking algorithm), Probabilistic model checking (Markov chains, probabilistic CTL, Markov decision processes)

Software Performance, Performance Modeling, Continuous-time Markov chains (CTMCs), numerical solution and stochastic simulation of CTMCs, Queuing systems, Stochastic process algebras, Layered queuing networks, Chemical Reaction Networks

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

Introduction to Autonomous systems, engineering the software of robotics systems, architectures and architecture description, Modeling and Architecting in automotive.

Service Oriented Design, Framing software sustainability, quality concerns in software design.

Fair division for indivisible goods

Experts, Bandits, and Online Learning