PhD course in
Computer Science and Artificial Intelligence
The Department of Mathematics, Computer Science and Physics of the University of Udine hosts the PhD course in Computer Science and Artificial Intelligence in agreement with Fondazione Bruno Kessler. The course continues an outstanding tradition in computer science teaching and research at the University of Udine, and ideally links up with the best science education courses in Italy at master level, as stated by the 2020/21 official ranking made by CENSIS. This tradition is further enriched by the dynamic, project-oriented knowledge production running at Fondazione Bruno Kessler, generating an ideal environment where top students can meet excellence in both theoretical as well as applied research fields.
The course is active since the XXXVII cycle (2021/22), and originates from the splitting of the PhD course in Computer Science, Mathematics and Physics. It resumes the tradition of the previous PhD course in Computer Science, active across thirty years since the first national cycle (1983/84) until the XXX cycle (2012/13).
The PhD course in Computer Science and Artificial Intelligence will graduate students with top skills, in topics that are listed below with links to the involved scientists belonging to the PhD Board, also in the context of a multi-disciplinary research plan:
Fondazione Bruno Kessler Scholarships
Reconfigurable and trustworthy pandemic simulation
Simulation tools are fundamental to predict the evolution of pandemic, and to assess the quality of counter-measures, e.g. the effect of travel restrictions on the spread of the coronavirus. However, they come with two fundamental requirements. The first is the need for a fast reconfiguration of the simulation, in order to be able to describe the mutating scenarios of the pandemics. The second is the ability to produce correct and explainable results, so that they can be trusted and independently validated. The topic of this research is to devise a model-based approach that is able to represent at a high-level the features of a generic pandemic, from which an efficient simulator can be produced. Using formal methods, the results of the simulation are guaranteed to be correct by construction, with proofs that can be properly visualized and independently checked. The activity will be carried out as a collaboration of the Center for Health Emergencies (https://www.fbk.eu/it/health-emergencies/), that played a major role during the ongoing pandemics, and the Center for Digital Industry (https://dicenter.fbk.eu/), a leading centers in model-based design.
Reverse Engineering via Abstraction
Many artifacts in the development process (requirements, specifications, code) tend to become legacy, hard to understand and to modify. This results in lack of reuse and additional development costs. A reverse engineering activity is necessary to understand what the system is doing. Goal of the thesis is to provide automated techniques to analyse the inherent behavior of legacy artifacts, extract interface specifications, and to support re-engineering activities. The thesis will combine techniques from language learning, applicable to black-box artifacts, and formal techniques for the automated construction of abstractions in the form of extended finite state machines.
Epistemic Runtime Verification
Runtime verification is a light weight verification technique based on the analysis of system logs. A key factor is that the internal state of the system is not observable, but partial knowledge on its behaviour may be available. The thesis will investigate the use of temporal epistemic logics (i.e. logics of knowledge and believe over time) to specify and verify hyperproperties for runtime verification. Different logical aspects, like distributed knowledge and common knowledge, and the communication between reasoning agents, will be used to model hierarchical architectures for fault detection and identification, and for prognosis. Techniques for planning in belief space will be used for the design of fault reconfiguration policies.
Condition monitoring and predictive maintenance of complex industrial systems: Model-based reasoning meets Data Science
The advent of Industry 4.0 has made it possible to collect huge quantities of data on the operation of complex systems and components, such as production plants, power stations, engines and bearings. Based on such information, deep learning techniques can be applied to assess the state of the equipment under observation, to detect if anomalous conditions have arised, and to predict the remaining useful lifetime, so that suitable maintenance actions can be planned. Unfortunately, data driven approaches often require very expensive training sessions, and may have problems in learning very rare conditions such as faults. Interestingly, the systems under inspection often come with substantial background knowledge on the structure of the design, the operation conditions, and the typical malfunctions. The goal of this PhD thesis is to empower machine learning algorithms to exploit such background knowledge, thus achieving higher levels of accuracy with less training data.
Planning and scheduling with time and resource constraints for flexible manufacturing
Many application domains require the ability to automatically generate a suitable course of actions that will achieve the desired objectives. Notable examples include the control of truck fleets for logistic problems, the organization of activities of automated production sites, or the synthesis of the missions carried out by unmanned, autonomous robots. Planning and scheduling (P&S) are fundamental research topics in Artificial Intelligence, and increasing interest is being devoted to the problem of dealing with timing and resources. In fact, plans and schedules need to satisfy complex constraints in terms of timing and resource consumption, and must be optimal or quasi-optimal with respect to given cost functions. The Ph.D. activity will concentrate on the definition of an expressive, formal framework for planning with durative actions and continuous resource consumption, and on devising efficient algorithms for resource-optimal planning. The activity will explore the application of formal methods such as model checking for infinite-state transition systems, and Satisfiability and Optimization Modulo Theories, and will focus on practical problems emerging from the flexible manufacturing domain.
Meta-learning for efficient 3D representations
Learning-based algorithms for 3D object description, recognition and retrieval suffer from lack of annotated data, unbalanced classes, computationally inefficient processing pipelines and poor generalization ability across different application domains. All these factors together often hamper the employment of 3D processing pipelines in large-scale real-world applications in urban and environmental contexts.
The goal of this Ph.D. position is to conduct research on novel and efficient algorithms for 3D data semantic segmentation and classification using integrative AI approaches that can effectively replace traditional hand-crafted modules to ultimately improve performance, ease deployment and foster scalability. The research should integrate traditional 3D classification methods (RF, 3DCNN, MLP, etc.) with symbolic approaches (KBANN, LTN, etc.) in order to enhance learning capabilities, handle noisy and multi-modal data and deliver a hybrid method able to constraint predictions with a-priori knowledge expressed in terms of logical formulas. A research task should also be dedicated to investigate advanced solutions to handle unbalanced classes in 3D classification problems, considering e.g. oversampling and under-sampling techniques, uneven weight distribution, complex loss functions, etc.
Boosting Digital Heritage (DH) with advanced AI methods
The digital humanities (DH) sector, unlike others, has been little explored for advanced information technologies (IT), computer sciences and Artificial Intelligence (AI) applications. This is mainly due to the little availability of digital data and the inhomogeneity of contents, leading to generalization problems. Therefore DH is an excellent case study where new developments in the IT and AI fields can provide innovative solutions for the analysis, protection, conservation, communication and enhancement of Cultural Heritage 2D and 3D contents.
The goals of the PhD research are: (i) to study, develop and validate innovative solutions based on AI algorithms to extract geometric and semantic information from digital data (images and 3D models) of cultural heritage; (ii) to propose and test alternative methods that allow to apply machine / deep learning algorithms in contexts with little data availability and with noisy classes, by exploiting integrative AI methods; (iii) to analyze, realize and demonstrate new methods to improve the transparency, interpretability and explainability of AI methods applied to Cultural Heritage 3D data.
The research should tackle the problems with a holistic and integrative approach, considering multi-GPU approaches, increasing learning capabilities and allowing to handle data with noise. Predictive solutions will serve to better analyze, preserve and enhance the Cultural Heritage, as well as to develop VR / AR solutions to support the tourism sector.