1Scalable data processing for data science (16 hrs, 4 ECTS)
LECTURER: Paolo MISSIER, University of Birmingham, UK
SYLLABUS: The teaching offer is designed to fit within the context of Unimore’s PhD programs in Computer Science, Computer Engineering, and Math. It will consist of a series of topical seminars and practical lab sessions, for a total of about 16 hours, covering the following topics:
Part I: An overview of how Foundation Models and notably Language Models for Natural Language Understanding, are increasingly adapted and used in Healthcare applications. Relevant background on the core concepts in modern Deep Learning / AI methods will be provided (3 hours plus 1 hour “guided literature search”).
Part II:
– Data-Centric AI: concepts and examples including Data Cleaning in combination with model training; training set pruning; and finding explanations of model behaviour in the training data
– Towards transparency and accountability in Data-centric AI: a provenance-based approach (overview of work in progress)
(3 hours plus 1 hour “guided literature search”)
Part III: Data-centric and Responsible AI for health: concepts and aspirations. An overview of open challenges that are relevant in AI for healthcare (2 hours)

Wed April 3, 2024: Room M2.5 9:00 a.m. – 1:00 p.m.
Mon April 8, 2024: Room Laboratorio Zironi 9:00 a.m. – 1:00 p.m.
Tue April 9, 2024: Room M0.1 11:00 a.m. – 1:00 p.m.
Tue April 9, 2024: Room M2.5 2:00 p.m. – 4:00 p.m.
Wed April 10, 2024: Room M2.5 9:00 a.m. – 1:00 p.m.

More information available at this link.

2Introduction to Big Data Integration (4 hrs, 1 ECTS)
LECTURER: Donatella FIRMANI, Sapienza Università di Roma
SYLLABUS: Data Integration (DI) is the fundamental problem of building a unified view of data residing in different sources. Over the decades, different generations of DI techniques have succeeded. Nowadays, in the Big Data era, the scale at which data are produced and analyzed poses unprecedented challenges. Big Data Integration (BDI) is the set of techniques developed in response to those challenges and is different from traditional DI in 4 fundamental ways, related to the (i) volume, (ii) velocity, (iii) variety and (iv) veracity of the sources. This short course aims at providing an introduction to these methods and related notions.
DATES: To be announced soon

3Digital twins in Industry 4.0: from monitoring to automated synthesis (4 hrs, 1 ECTS)
LECTURER: Francesco LEOTTA, Sapienza Università di Roma
SYLLABUS: Digital twins (DTs) are generally meant as digital interfaces to physical entities. This concept naturally applies to smart manufacturing where DTs may provide a powerful tool for tasks ranging from monitoring the status of the manufacturing process to automatically synthesize production plans able to deal with short term changes (e.g., disruptions) and long term changes (e.g., evolution of the market). In this course we will discuss technological tools and state-of-the-art techniques aiming at a fruitful application of the DT paradigm to Industry 4.0.
DATES: To be announced soon

4Privacy-enhancing technologies (12 hrs, 3 ECTS)
SYLLABUS: The course offers an introduction to privacy-enhancing technologies (PET), which are security solutions that aim at minimizing information disclosure during data processing. The course outlines the most important system models and security guarantees related to PETs, including information sharing, collaborative and outsourced computation, transparency architectures, and discusses the most popular techniques based on applied cryptography and hardware-related technologies. The course especially focuses on practical solutions and include hands-on sessions based on existing software frameworks.
DATES: 10-14 June 2024

5Advanced GPU programming – libraries for data science (16 hrs, 4 ECTS)
SYLLABUS: This course will dive deep into advanced concepts of GPU Programming and architectures. In the first part, advanced CUDA programming concepts will be presented, focussing on the latest evolution of the CUDA programming model and architectural features such as scheduling, programming best practices and CPU – GPU interaction optimizations along with comparison discussions with other GPGPU APIs. In the second part of the course, widely used CUDA libraries for numerical calculus and data science will be presented through extensive examples, such as cuBLAS, cuSOLVER and cuSPARSE for numerical optimization, cuFFT for signal processing and NPP for image processing.
DATES: September 2024

6Internet and Web of Things at the Edge (8hrs, 2 ECTS)
SYLLABUS: The class will focus on modern smart IoT systems, which are found in many different scenarios. The course will present at first the Internet of Things and Web of Things scenario, and will then move to practical problems in such environments. The class is focused on the key challenges that low power devices have when processing information. This can be done directly on the device itself, if possible, or can be offloaded to close edge servers. Two reference scenarios pertaining e-Health and Industry 4.0 will be discussed and analyzed in detail, highlighting the key differences and practical issues which can be found. The class will conclude by discussing future research directions.
Math building, FIM departmenth 14:00 – 16:00
May 2nd, 2024 – Room 2.5
May 8th, 2024 – Room 2.2
May 9th, 2024 – Room 2.5
May 15th, 2024 – Room 2.2

7Vulnerability research (12 hrs, 3 ECTS)
SYLLABUS: This course will introduce students to the methodological and practical aspects of security vulnerability research in software. The involved activities are at the basis of identifying unknown and sophisticated flaws (typically resulting in a chain of more elementary ones) that current software solutions are not able to find automatically.
Classes are organized as a series of seminars and practical lab sessions discussing the following topics:
– static analysis
– dynamic analysis
– formal verification methods
– semi-automated testing
DATES: July 2024

8Efficient DL/ML models for embedded systems (12hrs, 3 ECTS)
SYLLABUS: The execution of sophisticated Artificial Intelligence (AI) workloads is no longer a prerogative of high-end, high-performance computing systems. Energy- and resource-constrained embedded devices, also called edge devices, are increasingly embracing this type of functionality, which is key to enabling the realization of smart, autonomous systems (unmanned aerial and terrestrial vehicles, robotic arms, etc.). This class will present an overview of the state-of-the-art methodologies for effective and efficient deployment of Deep Learning and Machine Learning (DL/ML) models on edge embedded systems. The topics presented include quantization, pruning and network-architecture-search (NAS) strategies targeting practical and realistic challenges of deploying state-of-the-art DL/ML tasks on edge systems (e.g. NVIDIA Tegra, Xilinx MPSoC,  MCU-class RISC-V and ARM SoCs). The class will then close by providing insights on near-to-come research directions in the field.
May 17/24/30, June 10, 2024
10:00 – 13:00
Room M2.5, Math building, FIM department

9Complexity Theory, On-Line and Approximation Algorithms (12 hrs, 3 ECTS)
SYLLABUS: The course will introduce students to theory of computational complexity. The first (shorter) part of the course will be dedicated to the introduction of the fundaments of complexity theory and the definition of the most important complexity classes. The  second part of the course will be dedicated to the study of approximation and on-line algorithms. The former are used to address difficult problems (NP-complete or NP-hard), the latter for those problems whose input is not completely available at the beginning of the execution of a solving algorithm. To this aim, problems with interesting applications in distributed/parallel system and data science scenarios will be selected.
May 8, 2024 – 09:00 – 13:00 (Prof. Mauro Leoncini) – Room M2.3
May 9, 2024 – 10:00 – 13:00 (Prof. Manuela Montangero) – Room M2.4
May 15, 2024 – 10:00 – 13:00 (Prof. Manuela Montangero) – Room M2.3
May 16, 2024 – 11:00 – 13:00 (Prof. Manuela Montangero) – Room M2.4
Math building, FIM department

10Energy efficiency for Real-time systems under sequential and parallel programming (12 hrs, 3 ECTS)
LECTURER: Houssam-Eddine ZAHAF, Audrey QUEUDET, Université de Nantes, France
SYLLABUS: This course will present  real-time systems programming  from energy efficiency and availability perspectives. Firstly,  theoretical fundamentals about  single-  and multi-processor real-time scheduling will be presented. Partitioned and global real-time multiprocessor scheduling will be deeply addressed. Secondly,  parallelization techniques for real-time systems, and their schedulability analysis will be presented. We will have a special focus of how parallelization can help to achieve timeliness, while reducing energy consumption as much as possible. Finally,  energy harvesting systems will be addressed. These systems are usually powered by intermittent energy sources, and must ensure timeliness even in the presence of  energy-sources fluctuation. For the second and third part, we present the current state of literature and open-issues related to these systems.
DATES: To be announced soon

11Introduction to complex systems (8hrs, 2 ECTS)
SYLLABUS: Many systems in nature, society and technology are composed of numerous parts that interact in non-linear ways. In these systems the emergence of intermediate structures is frequently observed. Paradigmatic examples are present in biology, but similar organizational aspects can be found in different kinds of systems. In social systems we can observe several intermediate bodies between the state and individuals: parties, associations, movements, trade unions, etc. At a bigger scale, alliances, federations and leagues of nations are present, intermediate organizations between the states and the whole of mankind. Likewise, we can observe the emergence of technological organizations based on the interaction between computers in artificial systems, or the presence of dynamic structures composed by computers, (semi)automatic systems and human beings in socio-technological systems. Intermediate-level structures, once formed, deeply affect the system as a whole, and therefore play a key role in understanding its behavior. The course aims to introduce the main issues of complex systems, and to present some of the approaches used in their study.
6, 7 February 2024 – 14:00 – 16:00
12, 13 February 2024 – 11:00 – 13:00
Room M1.6, Mathematics building, via Campi 213/B, Modena

12Arm Architectures for High-Performance Real-Time (12 hrs, 3 ECTS)
SYLLABUS: The evolution of computer systems is bringing them constantly closer to the physical world by making machines interact with their surrounding reality. Industrial automation, robotics, aerospace and automotive industries drive increasing demands on both deterministic capabilities and compute performance into the Arm computer systems architecture.
This course will introduce elements of the Arm systems architecture and current and future solutions Arm is adopting, together with its partners, to enable the next generation of high-performance real-time computing.
The audience will be introduced to the Arm real-time compute activities, and how those activities will significantly impact all market segments where both performance and determinism are requirements.
DATES: To be announced soon

13Multi-objective Optimization and Symbolic Regression (8hr, 2ECTS)
SYLLABUS: Multi-objective optimization (MOO) is a powerful computational technique employed to find optimal solutions when multiple objectives or criteria must be taken into account. These objectives often conflict, meaning that enhancing one objective may come at the expense of another. This course will delve into MOO’s background and historical development. We will explore various algorithms used in MOO, including evolutionary algorithms and heuristic search methods. Furthermore, we will examine available MOO libraries and discuss techniques for identifying single optimal solutions. The course will emphasize the practical implementation of MOO through symbolic regression examples that pertain to real-world applications. Symbolic regression involves utilizing genetic programming algorithms to solve regression problems. Through hands-on exercises, we will apply MOO to tackle problems in Economics and Healthcare.
DATES: (All classes will take place in the Mathematics building, via Campi 213/B, Modena)

Class 1
Room: Lab M0.2
Date and time: 14/02/2024 14:00-16:00
Class 2
Room: Lab M0.1
Date and time: 15/02/2024 16:00-18:00
Class 3
Room: Lab M0.2
Date and time: 21/02/2024 14:00-16:00
Class 4
Room: Lab M0.1
Date and time: 22/02/2024 14:00-16:00

14Computer Graphics in the Era of AI  (12 hrs, 3 ECTS)
SYLLABUS: Computer Graphics is at an inflection point. New techniques in AI are rapidly evolving and possibly shaking the very foundations of the field. In this course, we will choose a small set of problems in graphics and see how they can be solved with differentiable algorithms and neural networks. The course is modeled as a class discussion of recent papers, together with a short overview of the topic.
April 16/23/30, May 7, 2024
09:00 – 12:00
Room M2.3, Math building, FIM department

15Methods for social and economic science and data (24 hrs, 6 ECTS)
SYLLABUS: The course analyzes how socio-economic disciplines are influenced by data analytics methodologies. The course consists of two parts. The first part presents a historical excursus on traditional socio-economic research methodologies, both those of a qualitative nature and those of a quantitative nature. The second part of the course addresses the new frontier of digital methods for social and economic research. The objective of the second part of the course is not simply to reflect on digital methods as a mere adaptation of traditional methodologies, but rather to seize the new opportunities and new limits that digital environments offer to study social and economic changes. The course will provide a solid foundation for using qualitative and quantitative methods in socio-economic research by making the most of the opportunities of digital data science.
DATES: To be announced soon

16Quantitative and formal modeling of historical sciences (12 hrs, 3 ECTS)
LECTURER: Cristina GUARDIANO, Giuseppe LONGOBARDI, UNIMORE – DCE, University of York
SYLLABUS: The implementation of quantitative models, computational tools and automatic algorithms of data collection and analysis has brought into human sciences models, idealizations, and explanatory standards typical of natural sciences. This course explores how these tools are extended and applied to those human sciences that specifically deal with history and cultural transmission. Focusing on the historical investigation of language diversity, the course will show how the application of computational techniques for data processing and analysis, in combination with the adoption of abstract cognitive language structures as taxonomic characters for phylogenetic reconstruction (Irimia et al. 2022), brings about the possibility to address large-scope genealogical issues, to look for general principles on possible historical evolution which cannot be revealed by archeology or demography alone, and ultimately to contribute some of the required heuristics and tools for a deeper investigation of human history.
DATES: 18-22 March 2024
More info at this link

17Coding and Comparing Syntactic Data (24 hrs,  6 ECTS)
SYLLABUS: The implementation of quantitative models, computational tools and automatic algorithms of data collection and analysis has brought into human sciences models, idealizations, and explanatory standards typical of natural sciences.
This course explores how these tools are extended and applied to those human sciences that specifically deal with history and cultural
Major contents:
– introduction to formal models of human language structure and diversity: parameters, parameter systems, parameter setting
– application of computational techniques to code, annotate and parse linguistic data (syntactically annotated corpora)
– application of computational techniques for data processing and analysis to the quantitative assessment of language relatedness and to phylogenetic reconstruction
DATES: 18-22 March 2024
More info at this link

18Labour market, intelligence and digital organization (12hrs, 3 ECTS)
SYLLABUS: The course deals with key conceptual, methodological and organizational features in using Big Data both for the so-called labour market intelligence (LMI), and for human resource management. The aim is to explore opportunities and constraints in using refined (granular) Big Data analysis of labour market and occupational structure (for example, mapping skills, identifying discrepancies and obsolescence of skills and carrying out predictive analysis for new jobs in quasi-real time), with particular attention to the main structural changes such as organizational changes in companies, the reproduction of inequalities, the insider-outsider gap, the retraining and disqualification processes and the interaction between internal and external labor markets.
DATES: 16, 17 July 2024

19Sociology and Sociosemiotics of Data  (12 hrs, 3 ECTS)
SYLLABUS: The course analyzes the effects that data and technologies have on current social systems. Through studies in the sociology of sciences and technologies, the course provides a point of view that helps to balance technical-computational aspects with issues of social responsibility. The topics of the course concern the use of data from a technological and social point of view. The sociological and sociosemiotic areas of interest are STS (Science and technological studies) and ANT (Theory of actor networks).
DATES: June/July 2024

20Business Model Innovation (12 hrs, 3 CFU)
SYLLABUS: The course aims to evaluate how data-driven management, digitalization of internal and external processes and Artificial Intelligence (AI) applications in market relationships could impact on firms’ economic performance and innovation rates of business models, a new and relevant unit for analyzing strategic and organizational growth paths of firms. The course will introduce the main research streams and the most promising research avenues in the current debate, specifically focusing on relationships among  sustainability and circular economy, on one side, and digitalization, applied data science and AI, on the other side.
May 23, 2024 – Room 7 (D2.8) – Palazzo Dossetti – via Allegri 9, Reggio Emilia
May 28, 2024 – Room D2.2 – Palazzo Dossetti – via Allegri 9, Reggio Emilia
May 31, 2024 – Room 7 (D2.8) – Palazzo Dossetti – via Allegri 9, Reggio Emilia
09:00 – 13:00

21Corsi Di Formazione Complementare Per Dottorandi E Assegnisti Ediz. 2022/2023 (24 hrs, 6 CFU)
LECTURER: Barbara REBECCHI, Ferdinando DI MAGGIO, Federica MANZOLI, Nadja SEDING, Giulia CATELLANI, Valeria BERGONZINI, Valeria GOLDONI, UNIMORE – International Research Office
SYLLABUS: The course is composed of 4 modular sessions:
a.    Policies for research and innovation: this session explains where the fundings for research come from. Opportunities and practices for national and international funding for research and innovation are explained;
b.    Planning the research: In this session all the various phases of the planning of research are explained: the EU finding policies and calls; the project cycle, the structure of the action and cost plan, the actors involved; the negotiation and the management of the european projects;
c.    Exploitation of the research results;
d.    Intellectual property: IP rights, protection methods, patents; management and exploitation of the IP; patent databases.

This course is mandatory for all PhD students

DATES: 12-15 December 2023

22Bibliographic research, scientific writing and dissemination: tools, techniques and strategies (12 hrs, 3 CFU)
LECTURER: Michele POLA, Andrea SOLIERI, UNIMORE – Ufficio Bibliometrico – SBA
SYLLABUS: The course aims at teaching the skills and the knowledge for using the specific library services and resources for doctoral students; for being productive in information retrieval, in preparing a bibliography, in writing a scientific article, so to support the Ph.D. students in their path with an outlook at their post-doc career.
The course will provide an in-depth introduction to the following aspects:
• resources for basic information retrieval like OPACs, UNISTORE and OneClick discovery tool
• resources for advanced information retrieval like scientific databases like Scopus and Web of science.
• the scientific journal as the main vehicle for STEMM research dissemination.
• workflow of a scientific article; (copy)rights and dues of an author, plagiarism, citations and bibliographies.
• How to work with a reference manager software.
• Improving research impact: from bibliometric analysis to research evaluation; journals evaluation (from authoritative to predatory); differences among ahead of print, post print, editorial version of a publication;
• the Open Access initiative and what this means for the authors; what ASN and VQR are with examples and exercises; how the repository IRIS works; checking author profiles on Scopus, Publons and ORCID.

This course is mandatory for all PhD students

February 2024


May 29-30, 2024 – 09:00 -11:00, Reggio Emilia (Campus San lazzaro – Padiglione Buccola-Bisi – Room F1.6 [RE 07]) 
Online participation is also possible – Courses in ITALIAN

– 29/05: 2nd module (1,5h): Open access e dati della ricerca; 3rd module (0,5h): Risorse bibliografiche e servizi SBA UNIMORE
– 30/05: 1st module (2h): Bibliometria e valutazione della ricerca

November 28-29, 2024 – 09:00 -11:00, online – Courses in ENGLISH

– 28/11: 1st module (2h): Bibliometria e valutazione della ricerca
– 29/11: 2nd module (1,5h): Open access e dati della ricerca; 3rd module (0,5h): Risorse bibliografiche e servizi SBA UNIMORE

January 20-21, 2025 – 09:00 – 11:00, Modena (place to be defined) 
Online participation is also possible – Courses in ITALIAN

– 20/01: 1st module (2h): Bibliometria e valutazione della ricerca
– 21/01: 2nd module (1,5h): Open access e dati della ricerca; 3rd module (0,5h): Risorse bibliografiche e servizi SBA UNIMORE