Director: Mario Zanon
The massive amount of data generated by sensors, smartphones, computers, and web platforms, inside home automation systems and intelligent devices, artifacts such as vehicles and robots, production processes, smart energy grids, and many others have made our lives increasingly data-driven. This availability of data, combined with the possibility of having increasingly powerful and pervasively distributed computing units both within the devices (embedded) or in multiprocessor boards connected to them and in the cloud, now makes new skills necessary to know how to use the data to predict the behavior of the system that generates them and to make decisions autonomously based on the information contained therein, preferably in an efficient and robust way from a computational point of view. These skills are necessary for engineering industrial automation systems and robots and in various other contexts, such as critical infrastructures (energy networks, urban mobility, water networks), self-driving vehicles, financial systems, biomedical systems, home automation, etc.
The doctoral track in Learning and Control (LC) offers interdisciplinary doctoral training for graduate students who wish to specialize in the research and development of algorithms for machine learning of models starting from data and for the control of dynamic systems based on numerical optimization. These methodologies allow understanding the system's dynamics that generate the data by learning mathematical models that give the ability to analyze its behavior, predict possible future evolution scenarios, and diagnose malfunctions. Moreover, they allow improving its overall behavior using real-time control algorithms, making the system autonomous in acting optimally and safely to pursue pre-established objectives. These methodologies are independent of the physical nature of the system under study. Therefore, they apply to a myriad of real problems, such as allowing both a vehicle to drive autonomously and avoid obstacles or a satellite to change its attitude and a smart electricity grid to make the most of energy from renewable energy sources.
The curriculum includes some basic courses capable of providing solid training on machine learning techniques, numerical optimization, analysis and control of dynamical systems, computer programming. In particular:
- Model predictive control
- Machine learning algorithms
- Numerical optimization
- Reinforcement learning
In addition to the basic courses, there will be specialized research seminars on frontier research topics and the possibility of attending thematic doctoral schools. The doctoral program also allows spending a research period abroad, generally lasting between 3 and 12 months.
Input and Output Profiles
Prospective students should preferably have training in engineering, mathematics, computer science, physics, statistics, or a related field. Potential students are offered frontier research topics or are free to propose a research topic of interest to them.
The LC track prepares researchers and professionals capable of analyzing and proposing solutions to various real problems of industrial, economic, and social interest, making them qualified to work in high-profile professional roles within universities, research centers, and in the private sector, such as in the automotive, aerospace, chemical, manufacturing, infrastructure, energy, urban mobility, biomedical, and various other sectors. Professional figures able to manipulate data using mathematical algorithms are also particularly sought after in emerging sectors such as electronic commerce, social networks, finance, and many others. These Ph.D. figures are particularly appreciated for their extreme versatility, mastering methodologies for approaching the formulation and resolution of problems, and very general algorithmic and computer skills.
Ph.D. students have the opportunity to collaborate with other institutions and companies with which the Research Units of the IMT School have established partnerships.
For more information regarding the research activities and the researchers related to the LC track, please refer to the link http://dysco.lab.imtlucca.it.