Phd in COMPUTER SCIENCE

The PhD program offers a wide range of educational activities designed to support the academic development of doctoral students. The seminar offering is particularly rich and includes contributions from internationally renowned scholars in the field of computer science.

The proposed activities include courses specifically dedicated to PhD students, mainly focused on cross-cutting topics and soft skills, as well as advanced thematic seminars in which international experts deliver short courses on emerging topics of common interest. Overall, these initiatives encourage the exploration of different areas of computer science. PhD students also have the opportunity to participate in summer and winter schools, as well as in the major conferences in their respective research areas, as part of their training path. Finally, it is important to emphasize that students consistently achieve significant research results, such as the acceptance of papers at national and international scientific conferences, many of which are top-tier (rank A+/A++), and that they receive both financial and logistical support to attend them.

Courses Specifically Offered for the Academic Year 2025–2026

For students of all three years, courses on cross-cutting topics and soft skills are offered:

“The wild world of publications: a guide on how to survive. Good practices and useful tips for researchers”

Instructor: Prof. Marco Marini
When:March 3rd 9:00-13:00
Where:in-person in room S1, building E, viale Regina Elena 295; remotely at https://meet.google.com/hbj-puvt-pmfDescription: This seminar covers essential strategies for academic publishing, including deciding what to publish, selecting target journals and conferences, adhering to submission guidelines, navigating peer review and revisions, and handling post-acceptance tasks like proofs and ethics. The content emphasizes metrics (e.g., SJR, H-index, JIF), database comparisons (Scopus vs. Web of Science), and best practices for efficient, ethical publication within timelines. A useful how-to for beginners and a double-check for experts.

TBA

Instructors: Prof. Walter Quattrociocchi and Prof. Matteo Cinelli
When: March/April/May 2026
Where: Department of Computer Science
Description: this short series of micro-lectures will cover cutting-edge topics: Applications of Complex Systems · From Typicality to Criticality in Complex Systems · Data-Driven Models of Complex Systems · Micro, Meso and Macro level Dynamics

Grant Writing

Instructor: Prof. Emanuele Rodolà
When: April/May 2026
Where: Department of Computer Science
Description: the module will address the preparation, submission, and reporting of research proposals for competitive calls.

"Graph algorithms for real real-life problems"

Instructor: Prof. Tiziana Calamoneri
When:18 June 2026
Description: the module will provide an overview of real-world applications that leverage graph algorithms to design innovative solutions.

"Writing, Benchmarking, and Reproducibility of Research Papers"

Instructor: Prof. Daniele De Sensi
When: May 14 2026
Where: Department of Computer Science
Duration: 2 hours
Assessment method: Critical analysis of a paper
Description: This seminar presents some guidelines for writing a research paper. We will start discussing how to organize and present research ideas. We then analyze good and bad practices in benchmarking and results presentation with practical and interactive examples. We discuss some common mistakes that might impact the results’ meaningfulness and interpretability. Last, we conclude by discussing how to guarantee the reproducibility of research results.

"Challenges in Large-Scale High-Performance Computing"

Instructor: Prof. Daniele De Sensi
When: July 8 and 9 2026
Where: Department of Computer Science
Duration: 4-6 hours
Assessment method: Critical analysis of a paper
Description: This course will critically analyze the design of interconnection networks for large-scale datacenters and HPC systems, focusing on the trade-offs between topology metrics (e.g., diameter, bisection bandwidth) and the resulting network contention and latency for massive communication patterns. We will discuss collective operations, at the backbone of every large-scale application, and how their performance is affected by network topology.

"Experimental Design and Statistical Analyses"

Instructor: Prof. Beatrice Biancardi
When: April 16, 2026, 14-17
Where: Room S1, Building E and https://meet.google.com/ovp-kjro-ocg
This course is aimed at people with little or no experience using statistical analyses in research.
It introduces the methodology and tools to conduct experimental studies.
Students will discover the different steps of experimental methodology: formulating hypotheses, defining the experimental design (independent and dependent variables), hypothesis testing and basic statistical analyses (descriptive and inferential statistics). By the end of the course, participants will be able to critically evaluate and design effective research questions, linking experimental design concepts to subsequent statistical analyses. The course will also allow participants to make informed decisions on which statistical tests are most appropriate to their research questions. The course will provide a solid grounding for further development of applied statistical competencies.
The course will include a theory lecture and a hands-on session with practical examples.

Additional modules will be announced shortly.

Educational Tracks for Advanced Training in Computer Science

The PhD program in Computer Science organizes numerous seminars within highly specialized and advanced educational tracks on various hot topics, in collaboration with leading national and international faculty in the field.

The complete list of delivered seminars is available in the dedicated section.

Other Educational Activities

PhD students engage daily in development activities, reporting, and documentation production within projects both external and internal to the Department. It is noteworthy that some of them have also had the opportunity to gain experience in industrial or governmental contexts, strengthening their skills in managing systems that are not strictly academic.

Among teaching activities, PhD students often have the opportunity to supervise and guide bachelor’s and master’s students in the development of their projects and in the writing of their final thesis. This activity allows them to obtain the recognition of “Co-supervisor”.