Unige Instructors

January-May 2025 (seminars)
Principles and tools for researchers: a series of seminars on FAIR principles, research assessment, conference and journal classifications/rankings, productivity tools such as LLMs, jupyter, etc.
Giorgio Delzanno, DIBRIS, Università degli Studi di Genova, giorgio.delzanno@unige.it

20-24 January 2025
Strategic Choices: Games and Team Optimization
Lucia Pusillo,  DIMA, Università degli Studi di Genova, pusillo@dima.unige.it
Marcello Sanguineti,  DIBRIS, Università degli Studi di Genova, marcello.sanguineti@unige.it

27 - 31 January 2025
Computer Science Education
Giovanna Guerrini, DIBRIS, Università degli Studi di Genova,  giovanna.guerrini@unige.it

17-21 February 2025
Computational models of visual perception 
Fabio Solari, DIBRIS, Università degli Studi di Genova,  fabio.solari@unige.it

3-7 March 2025
Introduction to Type Theory: from foundations to practice
Francesco Dagnino, DIBRIS, Università degli Studi di Genova, francesco.dagnino@dibris.unige.it

19-23 May 2025
High Performance Computing for heterogeneous accelerator architectures
Daniele D'Agostino, DIBRIS, Università degli Studi di Genova, daniele.dagostino@dibris.unige.it

4-6 June 2025
Theory and Practice of Runtime Monitoring
Davide Ancona, DIBRIS, Università degli Studi di Genova, davide.ancona@dibris.unige.it
Angelo Ferrando, Università Modena e Reggio Emilia, angelo.ferrando@unimore.it

10-14 June 2025
Introduction to discrete differential geometry
Claudio Mancinelli, DIBRIS, Università degli Studi di Genova, claudio.mancinelli@unige.it

16-20 June 2025
A journey through Deep Learning
Matteo Moro, Nicoletta Noceti, Francesca Odone,  and Vito Paolo Pastore, francesca.odone@unige.it
DIBRIS, Università degli Studi di Genova,

24-28 June 2025
Theoretical Foundations of Machine Learning
Lorenzo Rosasco, DIBRIS, Università degli Studi di Genova
Silvia Villa and Ernesto De Vito, DIMA, Università degli Studi di Genova, lorenzo.rosasco@unige.it

1-4 July 2025
Reliability, Availability, Maintainability, and Safety (RAMS) Engineering: Principles and Applications    
Enrico Zero, DIBRIS, Università degli Studi di Genova, enrico.zero@unige.it

July 2025, dates to be defined
Trustworthy Artificial Intelligence
Luca Oneto, DIBRIS, Università degli Studi di Genova, luca.oneto@unige.it

July 2025, dates to be defined
Optimization of Electric-Vehicle Charging: scheduling and planning problems
Michela Robba and Luca Parodi, DIBRIS, Università degli Studi di Genova, michela.robba@unige.it

7-11 July 2025
Mobile Security
Luca Verderame, DIBRIS, Università degli Studi di Genova
Alessio Merlo, CASD, Italy, alessio.merlo@unicasd.it

Mid/end July 2025, dates to be defined
Summer School in Data Visualization and AI
Annalisa Barla, DIBRIS, Università degli Studi di Genova, annalisa.barla@unige.it 

22-26 September 2025

Art-Led Research in Human Computer Interaction and Affective Computing 

Detailed information
 

Strategic Choices: Games and Team Optimization

 Duration:  20 hours

Where and When:

Via Dodecaneso, 35 - Room 216
Monday 20/1/25: h 14-18
Tuesday 21/1/25: h 9-13
Wednesday 22/1/25: h 9-13
Thursday 23/1/25: h 9-13
Friday  24/1/25: h 9-13

Abstract: Game and Team Theory study strategic interactions among two or more agents, which have to take decisions in order to optimize their objectives. They have various links to disciplines such as Economics, Engineering, Computer Science, Political and Social Sciences, Biology, and Medicine. These links provide incentives for interdisciplinary research and make the role of Game and Team Theory invaluable in a variety of applications. The main goal of this course consists in providing students with the basic mathematical tools to deal with interactive problems and illustrating them via case-studies.

Program

• Non-cooperative games
• Strategic games and extended-form games
• Incomplete-information games
• Well-posedness problems for Nash equilibria
• Repeated games
• Evolutionary stable strategies
• Multiobjective games and solution concepts
• Cooperative TU-games
• Solutions for cooperative games
• Partial cooperative games
• Team optimization with stochastic information structure.
• Examples of applications in contexts such as:
• environment models;
• nonverbal communication & social interactions;
• medicine and biology; 
• optimal production;
• telecommunication networks;
• transportation networks.

Interdisciplinarity. It is a methodological course, of interest to a large-spectrum audience. In particular, it can be offered in the other DIBRIS PhD Courses (“Security Risk and Vulnerability” - it is already included in the offer of this PhD - “Bioengineering and Robotics”, and “Robotics and Intelligent Machines”), in the DITEN PhD Course “Sciences and Technologies for Electrical Engineering and Complex Systems for Mobility”, in the DIME PhD Courses “Mechanical, Energy and Management Engineering” and “Engineering of modeling, machines and systems for energy, the environment and transportation”, and in the DIEC PhD Courses “Economics and Quantitative Methods”, “Strategic Engineering and Decision Methods”, and “Management and Security”. In previous years, we already had students from some of the above-mentioned PhD Courses.

References

• Course notes/slides.
• A. Dontchev, T. Zolezzi. ''Well-Posed Optimization Problems''. Lecture Notes in Math., vol. 1543. Springer, 1993. D. Fudenberg, J. Tirole. ''Game Theory'', MIT Press, 1991 
• G. Gnecco, M. Sanguineti. “Team Optimization Problems with Lipschitz Continuous Strategies”, Optimization Letters, vol. 5, pp. 333-346, 2011. 
• G. Gnecco, M. Sanguineti. “New Insights into Witsenhausen’s Counterexample”, Optim. Let. 6:1425-1446, 2012.

 G. Gnecco, Y. Hadas, M. Sanguineti, “Some Properties of Transportation Network Cooperative Games". Networks 74:161–173, 2019. 

• G. Gnecco, M. Sanguineti, G. Gaggero. “Suboptimal Solutions to Team Optimization Problems with Stochastic Information Structure”. SIAM J. on Optimization 22:212-243, 2012. 
• Y. Hadas, G. Gnecco, M. Sanguineti. "An Approach to Transportation Network Analysis ViaTransferable Utility Games". Transportation Res. Part B: Methodological, vol. 105, pp. 120-143, 2017. 
• K. Kolykhalova, G. Gnecco, M. Sanguineti, G. Volpe, A. Camurri, “Automated Analysis of the Origin of Movement: An Approach Based on Cooperative Games on Graphs". IEEE Trans. on Human-Machine Systems 50:550-560, 2020. 
• H. Peters. ''Game Theory- A Multileveled Approach''. Springer, 2008. 
• L. Pusillo. "Evolutionary Stable Strategies and Well Posedness Property", Appl. Math. Sc. 7:363-376, 2013. • L. Pusillo, S. Tijs. ''E-equilibria for Multicriteria Games ''. In: R. Cressman and P. Cardaliaguet. The Annals of the Int. Society of Dynamic Games (ISDG). vol. 12, pp. 217-228, Birkhauser, 2012. 
• R. Zoppoli, M. Sanguineti, G. Gnecco, T. Parisini. “Neural Approximations for Optimal Control and Decision". Springer, Communications and Control Engineering Series. London, 2020

Computational models of visual perception

Duration:  20 hours (+ final project)

Instructor(s): Fabio Solari – DIBRIS, University of Genoa – fabio.solari@unige.it

Abstract

This course introduces paradigms and methods that allow students to develop computational models of visual perception, which are based on hierarchical networks of interacting neural units, mimicking biological processing stages.   

Program

• Introduction to visual perception and to the cortical dorsal and ventral streams for action and recognition tasks.
• Hierarchical networks of functional neural units.  Computational models of the visual features estimation for action and recognition. Comparison among computational models and computer vision algorithms. Benchmark Datasets.  How to use computational models to improve virtual and augmented reality systems to allow natural perception and interaction.
• Case studies: models and algorithms of the literature.

References

• R, Hussain,  M. Chessa, F. Solari,  “Mitigating Cybersickness in Virtual Reality Systems through Foveated Depth-of-Field Blur”. Sensors, 21(12), p.4006, 2021
• G. Maiello, M. Chessa, P.J. Bex, F. Solari. Near-optimal combination of disparity across a log-polar scaled visual field. PLoS Computational  Biology 16(4): e1007699, 2020
• W.S. Grant, J. Tanner,  L. Itti. "Biologically plausible learning in neural networks with modulatory feedback." Neural Networks 88: 32-48, 2017
• F. Solari, M. Chessa, NK Medathati, P. Kornprobst. “What can we expect from a V1-MT feedforward architecture for optical flow estimation?”. Signal Processing: Image Communication. 1;39:342-54 ,2015
• G. Maiello, M. Chessa, F. Solari, P.J. Bex.  The (In) Effectiveness of Simulated Blur for Depth Perception in Naturalistic Images. PLoS one, 10(10), pp. e0140230, 2015
• A.F. Russell, S. Mihalaş, R. von der Heydt, E. Niebur, R. Etienne-Cummings. "A model of proto-object based saliency." Vision research 94: 1-15, 2014
• P. Bayerl, H. Neumann.  “Disambiguating visual motion by form-motion interaction—a computational model”. International Journal of Computer Vision. 72(1):27-45, 2007

• R.S. Zemel, P. Dayan, A.  Pouget. “Probabilistic interpretation of population codes”. Neural Computation, 10(2), pp.403-430, 1998

  Interdisciplinarity: PhD program in Bioengineering and Robotics.

Introduction to Type Theory: from foundations to practice

Duration:  ~24 hours  

Instructor(s): 

Francesco Dagnino – DIBRIS, Università di Genova – francesco.dagnino@dibris.unige.it

Abstract

Proof assistants are tools designed to write formal proofs and automatically check their correctness. They are increasingly used  in many different domains, from software verification to formalized mathematics. Most popular proof assistants, such as Agda, Coq or Lean, implement a constructive logic based on a (dependent) type theory. This means that they are strongly typed functional programming languages where types and programs are seen as logical formulas and proofs, respectively, and then the correctness of a proof is ensured just by typechecking a program. 

In the course, we will  study fundamental notions and results on type theories, explaining their connection with logic, and we will experiment formal reasoning in a type theory, using Agda as a concrete system. 

Program

Below we report a tentative program. It will be adapted depending on the audience. 

• Introduction, Constructive reasoning 
• Untyped Lambda-Calculus: terms, reduction, confluence, normalisation
• Typing a la Curry vs Typing a la Church 
• Simple Types and Intuitionistic Propositional Logic
• Strong Normalisation and Consistency 
• Dependent Types and Quantifiers, Identity Types and Equality 
• Advanced Agda Features (Inductive Types, Universes, Record Types, …) 

Interdisciplinary:  PhD in Mathematics, PhD in Security, Risk and Vulnerability, PhD in Philosophy

References

[1] J.Y. Girard, Y. Lafont, P. Taylor. Proofs and Types. Cambridge University Press, 1989. 

[2] M.H.B. Sorensen, P. Urzyczyn. Lectures on the Curry-Howard Isomorphism. Elsevier, 2006.

[3] B. Nordstrom, K. Petersson, J.M. Smith. Programming in Martin-löf’s type theory : an introduction. Clarendon Press, 1990.

[4] M. Hofmann. Syntax and Semantics of Dependent Types. Cambridge University Press, 1997

[5] The Univalent Foundation Program. Homotopy Type Theory. Institute for Advanced Study, Princeton, 2013.

[6] Agda (https://agda.readthedocs.io/en/v2.6.4/) 

High Performance Computing for heterogeneous accelerator architectures

Duration:  20 hours 

Instructor: Daniele D’Agostino – DIBRIS Unige

When: 6th - 10th May 2024

Where: Via Dodecaneso 

Abstract For most scientists the abstract fact of the existence of an algorithm solving a problem is enough, while its efficient implementation in terms of exploitation of the available computational capabilities is mostly disregarded. But with the end of Moore law for sequential computing architectures and the advents of multi and many cores era, managing parallelism is no longer the goal of a restricted ICT community, it becomes a need for everybody who is interested in exploiting an adequate fraction of available performance provided by widespread modern computing architectures. The aim of the course is to provide a glance of the different aspects involved in efficient and effective programming of current heterogeneous computing systems equipped with manycore x86 architectures and accelerators, in particular graphics cards (GPUs). Therefore, it conveys the required knowledge to develop a thorough understanding of the interactions between software and hardware at the core, socket, node and cluster level. In particular it will be presented, with practical cases, how the design and implementation of programs can exploit available computational resources through a suitable selection of programming paradigms, compiling and profiling tools. The course includes a hands-on part that the student may dedicate to a general case study or to a personalized case depending on specific interests. 

 

With respect to the past editions this course will focus on OneAPI, an open, cross-industry, standards-based, unified, multi-architecture, multi-vendor programming model adopted by Intel, for a unified application programming interface (API) intended to be used across different computing accelerator architectures, including GPUs and field-programmable gate arrays (FPGAs).

The programming languages will be C/C++/Data Parallel C++

Program

• Introduction to complex heterogeneous parallel systems: from workstations to High Performance clusters and supercomputers.
• The von Neumann architecture then versus now, features and bottlenecks.
• Introduction to parallel architectures.
  • Single Instruction Multiple Data (SIMD)
  • Single Program Multiple Data (SPMD)
• The roofline performance model.
  • Profiling and performance analysis
• The compiler, one of the most important software tools for HPC.
  • Intel oneAPI 
  • Nvidia HPC SDK 
• Optimal use of parallel resources – on the basis of students’ interests one or more of the following topics
  • SYCL Programming for Accelerated Computing (CPUs, GPUs and FPGAs) 
  • Parallel programming for x86 nodes: OpenMP and MPI
  • Parallel programming for GPUs: openACC and CUDA
  • Parallel programming for HPC systems: MPI+X
• Designing parallel applications and practical experiences.

References

• Slides and references will be provided to students

Theory and Practice of Runtime Monitoring

Duration:  about 20 hours

Instructor(s):  Davide Ancona - University of Genoa (DIBRIS) - davide.ancona@unige.it, Angelo Ferrando - University of Genoa (DIBRIS) - angelo.ferrando@unige.it

Abstract

The course provides a general introduction to Runtime Monitoring and Verification (RM&V), and the theoretical and practical aspects of RML (Runtime Monitoring Language), a system agnostic domain specific language for RM&V. Use cases will be considered in the context of distributed, Internet of Things and robotic systems.

• An introduction to RM&V.
• Theory and practice of RML, a domain specific language for RM&V.
• RM&V of IoT applications based on Node.js.
• RM&V of Robotic systems based on ROS.
• Hands-on labs with RML.

Interdisciplinarity: PhD Program on Security, Risk and Vulnerability

References

• Davide Ancona, Angelo Ferrando, Viviana Mascardi. Runtime Verification of Hash Code in Mutable Classes. FTfJP@ECOOP 2023: 25-31
• Davide Ancona, Luca Franceschini, Angelo Ferrando, Viviana Mascardi.  RML: Theory and practice of a domain specific language for runtime verification. Science of Computer Programming, 205:102610 (2021).
• Angelo Ferrando, Louise A. Dennis, Rafael C. Cardoso, Michael Fisher, Davide Ancona, Viviana Mascardi.Toward a Holistic Approach to Verification and Validation of Autonomous Cognitive Systems. ACM Trans. Softw. Eng. Methodol. 30(4): 43:1-43:43 (2021)
• Angelo Ferrando, Rafael C. Cardoso, Michael Fisher, Davide Ancona, Luca Franceschini, Viviana Mascardi. ROSMonitoring: A Runtime Verification Framework for ROS. TAROS 2020: 387-399
• Luca Franceschini, RML: Runtime Monitoring Language, Ph.D. thesis, DIBRIS - University of Genova, URL http://hdl.handle.net/11567/1001856, March 2020.
• Davide Ancona, Francesco Dagnino, Luca Franceschini. A formalism for specification of Java API interfaces. ISSTA/ECOOP Workshops 2018: 24-26
• Davide Ancona, Luca Franceschini, Giorgio Delzanno, Maurizio Leotta, Marina Ribaudo, Filippo Ricca. Towards Runtime Monitoring of Node.js and Its Application to the Internet of Things. ALP4IoT@iFM 2017: 27-42
• Davide Ancona, Angelo Ferrando, Viviana Mascardi. Comparing Trace Expressions and Linear Temporal Logic for Runtime Verification. Theory and Practice of Formal Methods 2016: 47-64
• Angelo Ferrando, Davide Ancona, Viviana Mascardi. Decentralizing MAS Monitoring with DecAMon. AAMAS 2017: 239-248
• Davide Ancona, Angelo Ferrando, Viviana Mascardi. Parametric Runtime Verification of Multiagent Systems. AAMAS 2017: 1457-1459
• Y. Falcone, S. Krstic, G. Reger, D. Traytel, A taxonomy for classifying runtime verification tools, in: Runtime Verification – 18th International Conference, Proceedings, RV  2018, pp. 241–262.
• E. Bartocci, Y. Falcone, A. Francalanza, G. Reger, Introduction to runtime verification, in: Lectures on Runtime Verification – Introductory and Advanced Topics, 2018, pp. 1–33.
• Yliès Falcone, Klaus Havelund, Giles Reger. A Tutorial on Runtime Verification. Engineering Dependable Software Systems 2013: 141-175
• Martin Leucker, Christian Schallhart. A brief account of runtime verification. J. Log. Algebr. Program. 78(5): 293-303 (2009)
• RML: https://rmlatdibris.github.io
• Node.js: https://nodejs.org/en

Introduction to discrete differential geometry 

Duration:  20 hours (about 20 hours)

Instructor(s): 

Claudio Mancinelli – DIBRIS, University of Genoa – claudio.mancinelli@unige.it

When: 17th -21st June 2024

Where: DIBRIS, Via Dodecaneso. Attendance via Teams is also possible. 

Abstract The course has the purpose of introducing how basic concepts in differential geometry can be brought into the discrete setting, focusing on triangle meshes. Several applications in geometry processing in which these concepts play a pivotal role are presented as well.

Program Continuous setting: differentiable manifolds, Riemannian metric, affine connection, geodesics and exponential map. Discrete setting: Tangent space, metric, parallel transport, differential operators, geodesic paths and distances. Applications to geometry processing: smoothing, the vector heat method, vector graphics on discrete surfaces.

Interdisciplinarity: This course could be offered to PhD students in both Computer Science and Mathematics.

References 

[1]  do Carmo M. P., Riemannian Geometry, 1992 

[2]  Botsch M., Kobbelt L, Pauly M., Alliez P, Lévy B., Polygon Mesh Processing, 2010 

[3]  Crane K., Livesu M., Puppo E., Qin Y., A Survey of Algorithms for Geodesic Paths and Distances, 2020

[4]  Sharp N., Crane K., The Vector Heat Method , 2019

[5] Mancinelli C., Nazzaro G., Pellacini F., Puppo E., B/Surf: Interactive Bézier Splines on Surfaces, 2022 

Title: Optimization of Electric-Vehicle Charging: scheduling and planning problems

CFU: 6

Instructors: Michela Robba– University of Genova – michela.robba@unige.it; Luca Parodi– University of Genova – luca.parodi@edu.unige.it; 

Where: Teams and in presence

Abstract

The concept of a dynamic and highly distributed Smart Grid which can intelligently integrate all connected users in an efficient, sustainable, economic and secure way, has opened new challenges in the application of Energy Management Systems (EMSs) and optimization techniques in the various research areas related to planning, management and control of power generation, distribution systems, and demand response. In fact, the electrical grid is characterized by different components (distributed generation and production plants from renewables, storage systems, buildings, microgrids, distributed electric vehicles (EVs), etc.) and actors (microgrids’ owners, distribution systems operators, aggregators, owners of charging stations and islands of recharge, etc.) that must be coordinated in order to respect technical requirements, minimize costs and environmental impacts.

 In this course, attention is focused on the application of control and optimization methods and approaches to energy systems in which EVs are present. In particular, the aim is to provide models and methods for the optimal management of EVs through an interdisciplinary approach that brings together knowledge from sectors of transportation, manufacturing and smart grids. After a brief introduction to the state of the art and technologies, first of all, the scheduling of EVs in a smart grid is presented through the formalization of a discrete-time optimization problem in which also fossil fuel production plants, storage systems, and renewables are considered to satisfy the electrical load of the grid. Then, a discrete-event formalization is presented. Finally, optimal planning of charging stations over a territory will be shown, as well as energy demand assessment based on traffic user equilibrium conditions. Some basic concepts on routing and charging approaches will be presented too.

Program

• Introduction to energy management systems and electric vehicles

• Optimal scheduling of electric vehicles in a discrete time framework

• Optimal scheduling of electric vehicles in a discrete event framework

• Traffic user equilibrium conditions for transportation networks with electric vehicles

• Optimal planning of charging stations over a territory

• Basic concepts on routing and charging

References

Ferro, R. Minciardi, L. Parodi, M. Robba. A user equilibrium model for electric vehicles: Joint traffic and energy demand assignment, Energy 198, 2020.

G. Ferro, R. Minciardi, L. Parodi, M. Robba. Optimal Planning of Charging Stations in Coupled Transportation and Power Networks Based on User Equilibrium Conditions. IEEE Transactions on Automation Science and Engineering, 2022.

G. Ferro, M. Robba, M. Paolucci. Optimal charging and routing of electric vehicles with power constraints and time-of-use energy prices. IEEE Transactions on Vehicular Technology, 69 (12), 14436-14447, 2020.

G. Ferro, R. Minciardi, L. Parodi, M. Robba. Discrete event optimization of a vehicle charging station with multiple sockets. Discrete Event Dynamic Systems: Theory and Applications, 31 (2)

G. Ferro, F. Laureri, R. Minciardi, M. Robba. An optimization model for electrical vehicles scheduling in a smart grid. Sustainable Energy, Grids and Networks 14, pp. 62-70, 2018

Theory and Practice of Learning from Data

Duration:  20 hours

Instructor(s):  Luca Oneto, UNIGE, luca.oneto@unige.it 

Webpage: https://www.lucaoneto.it/teaching/tpld-phd 

Abstract

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 DataMining, Statistics, Computational Intelligence, Machine Learning, and PatternRecognition. 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.

Program

• Inference: induction, deduction, and abduction
• Statistical inference
• Machine Learning
• Deep Learning
• Model selection and error estimation
• Implementation and Applications

Interdisciplinarity: Yes, any engineering/science PhD.

References

• C. C. Aggarwal "Data Mining - The textbook" 2015
• T. Hastie, R.Tibshirani, J.Friedman "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" 2009.
• S. Shalev-Shwartz, S. Ben-David "Understanding machine learning: From theory to algorithms" 2014
• I. Goodfellow, Y. Bengio, A. Courville "Deep learning" 2016
• L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020