PhD Courses 2024/25 academic year

Service Orchestration and Industrial IoT Platforms for Industry 4 and 5.0 environments

This doctoral course offers an in-depth exploration of Service Orchestration and Industrial Internet of Things (IIoT) platforms, which are fundamental to the next wave of industrial revolution marked by Industry 4.0 and Industry 5.0. As the industrial landscape evolves, the shift from Industry 4.0 to Industry 5.0 marks a significant transition towards more intelligent, interconnected, and human-centric systems. This course aims to provide a profound understanding of these advancements and their practical applications in real-world industrial environments. The course "Service Orchestration and Industrial IoT Platforms for Industry 4.0 and 5.0 Environments" aims to provide to the students the key concepts of Industry 4.0 and 5.0 along with their differences, the principles and techniques of service orchestration. In addition, this course will provide a comprehensive knowledge of IIoT architectures, protocols, and platforms, including the roles of Cloud Continuum in enhancing industrial applications. Finally, it will explore real industrial practical use cases based on the topics delved in the course.

More informations incoming.

Multi-platform Programming for Research-Oriented Software

Programming is becoming a central activity in research, far beyond the realm of computer science. Mainstream programming languages are flourishing around well-identifiable communities and well-established programming platform. For instance, i) Python is the language of Data Science, ii) JavaScript targets the Web, and iii) the JVM is often the primary choice among multi-agent systems, logic-based technologies, as well as backend and mobile development. Along this line, in order to maximize the reach of research-oriented software, it is of paramount importance to write code supporting as much platforms (and languages) as possible. Of course, maintaining multiple codebases is a no-go, and this is why researchers often focus on particular platforms—hence limiting their potential audience. Accordingly, in this course we present approaches and best-practices for multi-platform programming, where the same codebase is made available on multiple platforms, minimizing rewriting of code while maximizing portability.

More informations incoming.

Containerisation and Orchestration for Research Reproducibility

Guaranteeing reproducibility of results is quintessential for quantitative research.
Nowadays, more and more research fields require writing software as part of the experimental setup.
In Computer Science and AI, in particular, software is not only a tool for Science, but most commonly the subject of the study too.
To guarantee reproducibility of software experiments, it is paramount for researchers to make it as simple as possible to restore the computational environment in a deterministic way.
Containerisation and orchestration — as supported by the Docker technology — are powerful abstractions to serve this purpose.
Accordingly, in this course, we present both the theory and practice of containerization and orchestration, with a focus on how to make data-science experiments automatically reproducible.

More informations incoming.

Toward Next-Generation Networks - 5G and O-RAN implementations

The doctoral course "Toward Next-Generation Networks - 5G and O-RAN implementations" provides an advanced understanding of contemporary mobile networks. It covers 3GPP standards, deployment models for the 5G Core network, and the architecture of the Radio Access Network (RAN), including its disaggregation. The course explores the O-RAN model, which promotes flexibility and interoperability in radio networks. Through theoretical lessons, students will acquire skills to design, implement, and optimize advanced mobile networks, preparing them to become experts in the telecommunications sector.

More informations incoming.

How to Write and Publish a Research Paper in Computer Science and Engineering

This is an introductory level course suitable for junior Ph.D. students who are new to scientific publishing in computer science and engineering. The aim is to give practical advice on writing about research work and getting it published in today’s competitive scientific world. After introducing the necessary backgroud on publising, the course will cover the key issues around structuring a paper and deciding the contents, the writing, and the publication phase. The course is meant to be higly interactive. Students requiring an exam will be asked to put some advice into practice and get involved in drafting a short research paper and in its reviewing process.

More informations incoming.

Human-AI Interaction

This course aims to present the main concepts related to Human-AI interaction. With its ever-growing capabilities to perceive, understand, react, and learn, AI is being employed in domains that used to be exclusive to humans, blurring the boundaries between humans and IT artifacts. After presenting some background information (e.g., human-computer interaction notions, xAI approaches, ...), the course will cover the possible issues about how the interaction between users and a general AI system should be designed, taking into account several key factors.

More informations incoming.

Robust and Reproducible Research

The number of scientific articles published in Computer Science (and similar fields) increases
steadily every year. This is mainly due to breakthroughs like Deep Learning, and, more
recently, Large Language Models.
Paradoxically, researchers are struggling even more to reproduce published research. This
issue affects all possible aspects of research, including methodology, data curation, approach
comparison, and implementation.
In this course, we’ll introduce and discuss the concept of ’reproducibility’ in research. In
particular, we’ll overview current issues in research and existing attempts to address them.
We’ll focus on data curation, experimental setup, model comparison, and programming best
practices.
This course is recommended for all types of researchers, from those who have just embarked
on their journey to those who have always wondered how certain research managed to get
published.

Data Visualization for Researchers: theory and new approaches to enhance communication and dissemination

This course aims to presents the fundamentals of Data Visualization to empower students with the skills and insights necessary for creating effective visualization able to enhance communication and disseminations of data and scientific results. The course will cover also the principles, techniques and new approaches of Data Visualization, combining theoretical lessons with hands-on activities. Finally, the course will present some ethical consideration about Data Visualization.

More informations incoming.

Cutting-Edge Text Mining: from Consolidated Natural Language Processing Methods to Advanced Large Language Models

The course presents the fundamentals of text mining to empower students with the skills of knowing and performing the most important downstream tasks in natural language processing. To ensure the content is easily accessible, the course methodically introduces a selection of the most pivotal models, methods, techniques, and algorithms that have shaped the discipline, along with ongoing breakthroughs that have culminated in the development of current large language models (LLM). The course examines the latest generative model innovations, highlighting their functionalities, limits, applications and the theoretical concepts that could fuel future tech advancements. By the end, students will know both the essential principles of the discipline and advanced efficient techniques to employ and train modern compressed large language models in both practical settings and academic research. The course will also provide an overview of the most promising current research perspectives in the field, offering insights into the frontier of advancements and emerging trends, from knowledge-enhanced NLP to differentiable reasoning and explainability, including the emergent cognitive capabilities of the new LLMs.

More informations incoming.