Research Areas – Corso di dottorato in "Computer and Data Science"

Autonomous systems, Agents and Middleware

Methodologies and tools for modeling, developing and managing collective adaptive systems, swarm robotics, software agents, autonomic systems, self-organizing systems. This research areas covers three main topics: (1) software engineering techniques that support the development of distributed systems; (2) the coordination techniques of the involved components and (3) the interdisciplinary study of emerging structures in biological, artificial and socio-technological systems. At the application level, different domains will be considered, e.g. autonomous vehicles, intelligent manufacturing systems, e-health, social sciences.

Internet of Things

Design, development and performance evaluation of Internet of Things and Web of Things devices, autonomous and self-organizing. The IoT area can be addressed from many points of view: (i) communication, pertaining low-power and efficient data exchange between devices; (ii) Artificial Intelligence, related to deciding how and where AI algorithms need to be run; (iii) Collective awareness, which studies the services and insights multiple devices can bring, such as Crowdsensing systems.

Application of AI Techniques and Data Science to Real-World Data (RWD)

The term real-world data (RWD) refers to routinely collected data relating to real-world entities coming from different application contexts, from medical to Human Resource Management, from cultural heritage to social network analysis, from game science to smart cities. Real-world data are usually heterogeneous in their structures and sources, noisy, often incomplete/limited in number, and partially inconsistent. This research area is about the following research activities: (i) massive data management and access to unconventional data (stream, textual, semistructured XML and graph), also in an approximate and/or personalized way; information sharing, interoperability and Semantic Web in large heterogeneous and distributed data sources; (ii) scalable data science, information extraction, data analytics; (iii) AI technique for processing data in-the-wild, making AI performance adequate and stable in these challenging contexts.

High Performance Embedded Computing

Modern cyber physical systems execute data-hungry and latency-sensitive applications that exhibit increasingly stringent performance requirements. Hardware platforms deployed in autonomous systems, industrial robots or 5G Base Stations – to name a few – require the highest performance-per-watt. To meet such demands modern embedded computers are architected as massively parallel and heterogeneous on-chip systems (HeSoC) integrating multi-core CPUs, graphics processing units (GPUs), programmable logic (FPGA), digital signal processors (DSP) and application-specific accelerators (e.g., CNN/DNN). This research area studies means of efficiently designing and programming HeSoCs, optimizing various metrics such as performance, timing predictability, energy efficiency, security. The approach is fully vertical, ranging from architectural and implementation aspects to the design of programming models, compilers, APIs and related tools.

Quantitative modelling of cultural diversity of language and cognition

Goals. Apply quantitative modeling on abstract linguistic data; create dedicated algorithms to extract historical signals from syntactic parameters (www.parametricomparison.unimore.it); implement computer-assisted strategies and quantitative tools to the study of language transmission and diversification through time, space, and society; develop data-driven computational models of the structure of human grammars.
Research activities: (a) Implementing tools and methods of cognitive and quantitative sciences to the investigation of the structure of human history and cognitive diversity; (b) investigation and application of quantitative algorithms (Bayesian techniques and statistical algorithms) to historical sciences and to the explanation of language diversity; (c) devising and applying machine learning and data driven algorithms to model the structure of human language and its variation: networks and structural implications; (d) implementing computational tools to the treatment of cognitive data.
Background required: (a) Students with background in computer science (e.g., Machine Learning, algorithms, deep learning, AI, …) who want to exploit novel applications of computational skills and tools to problems addressed by formal and historical investigation in linguistics; (b) students with background in linguistics (e.g., computational linguistics, formal linguistics, historical linguistics, psycholinguistics, neurolinguistics, language acquisition) who want to learn how to apply computational methods and quantitative treatments to the analysis of human language.

Systems and Information Security

The “Systems and Information Security” research area deals with methodologies, techniques and technologies for assessing and improving security of data, systems infrastructures and software applications. It covers the following topics.
1. Applied cryptography for security protocols and architectures: design of security solutions for novel computer system settings (e.g., constrained networks, cyber-physical systems) and for improved security guarantees (e.g., transparency architectures, privacy-preserving protocols).
2. Cryptographic engineering: implementation of cryptographic schemes and protocols, study and evaluation of attacks to implementations.
3. Offensive and defensive security: modeling and automated orchestration of cyber attack and defense tasks, algorithms and tools for active defense, design of scores for the assessment of cyber security students, network anomaly detection through machine learning.
4. Secure software assurance: design and evaluation of metrics for the assessment of source code bases.
Methods and techniques for enhancing the Secure Software Development Life Cycle (including DevSecOps).

Computer Graphics

The Computer Graphics research at UniMoRe focuses on the development of new algorithms for the creation, manipulation and visualization of 3d environments. We focus on making 3d content creation simpler, more controllable, and more interactive. We target applications in industrial design, visual effects and games. In the past, we made significant contribution in the area of real-time rendering, lighting and appearance design, and collaborative 3d systems. Our work blends techniques from graphics systems, realistic rendering, computational geometry, numerical optimization, and machine learning.