Research projects 2020-21

In the following the list of projects offered for the next academic year is shown. For futher information contact the director of PhD program.

Research projects for academic year 2020-21 (cycle 36, starting date November 1st, 2020)

TITLE	TUTOR	ABSTRACT
Mining, retrieval e analisi di processi di business /Mining, retrieval and analysis of business process models	Stefania Montani	Process mining techniques to learn process models from business process traces; definition of proper similarity metrics for business process model comparison; exploitation of these metrics within proper retrieval and ordering algorithms to support process analysis; classification/prediction on process traces through deep learning techniques; testing in real world domains (e.g. stroke management).
Interazione adattiva con i robot sociali ed educativi - Adapted Interaction with Social and Educational Robots	prof. Cristina Gena	Human Robot Interaction (HRI) is a field of study dedicated to understanding, designing, and evaluating robotic systems for use by, or with, humans. In HRI there is a consensus about the design and implementation of robotic systems that should be able to adapt their behaviour on the basis of user actions and behaviour. The robot should adapt to emotions, personalities, and it should also have memory of past interactions with the user in order to become believable. This is of particular importance in the field of social robotics and social HRI. Our research focus is on social, assistive and educational robots able to adapt to user's features and and interact with the user in an intelligent, affective and social modality. We are at the moment leading in 2 projects, Empathy PRIN 2017 (as local coordinator) and Google Educator Grants Awards 2019 – EMEA, aimed at investigating the role of assistive and educationl roles in the context of ambient assisted living, education and learning, cultural heritage.
Crowdmapping/Crowdmapping	prof. Guido Boella	Examining the impact of IT on the evolution of participatory cartography into crowdmapping. Possible focuses are: georeference by means of GPS (outdoor maps) or RFID (indoor mapping) in order to collect environment and user data, integration of map semiotics with user interaction principles, analysis of the social aspect by means of social networks and incentive mechanisms, cognitive (and possibly ontology-based) models for map-based knowledge representation, analysis of the temporal dimension for the map-based representation of fluents. Projects: FirstLife
Visione artificiale e deep learning per immagini multidimensionali/Computer vision and deep learning for multi-dimensional imaging	prof. Marco Grangetto	The way we capture visual information from reality is changing significantly in many fields, ranging from consumer camera sensor, to satellite remote sensing and biomedical imaging. On the one hand, novel and cheap technologies support unprecedented amount of imaging information by means of depth and 360-degree cameras, multispectral sensors, event cameras, etc. On the other hand, new multidimensional processing and computer vision techniques aided by the flexibility of deep learning pave the way to unparalleled opportunities for understanding and modeling of visual information. This project will be focused on advancing the state of the art in the area of computer vision with potential impact in many application areas ranging from biomedical image processing, to 3D rendering and augmented reality. The project will blend both theoretical activities in the areas of computer vision and machine learning and deployment of practical applications in the context of collaborative international and industrial projects. More information at http://di.unito.it/eidoslab
Applicazioni di blockchain e smart contract/Blockchain applications and smart contracts	prof. Claudio Schifanella	The success of Bitcoin chryptocurrency has shown the potentialities of the distributed ledger technologies such as the blockchain. This project aims at studying these technologies and in particular of smart contracts. This project will benefit from the connection with the Co-city European project, where a blockchain for social purposes is developed
Sistemi avanzati di Ontology Learning e Open Information Extraction basati su tecniche di Natural Language Processing, Machine Learning ed integrazione di risorse semantiche / Advanced Ontology Learning and Open Information Extraction systems based on Natural Language Processing, Machine Learning and integration of semantic resources	Luigi Di Caro	Ontology Learning and Open Information Extraction were born to solve the problems of extracting semantic knowledge from large corpora in automated ways. The project involves tasks such as 1) the advancement of semantic extraction methods, 2) the creation of multilingual datasets and resources, 3) the application of Machine (and Deep) Learning algorithms in this context, and 4) the development of end-user applications such as chatbots.
Analisi e Visualizzazione di Reti Complesse per le Scienze Sociali Computazionali/Complex Networks Analysis and Visualization for Computational Social Science	Giancarlo Ruffo	Nowdays data scientists deals with the understanding and forecasting of real world phenonema adopting a data-driven approach to the analysis of complex systems. One of the most successful representations that allowed researchers to uncover non trivial properties from large data sets is the network (or graph) that have led to the breakdown of standard theoretical frameworks and models. In fact, a vast number of real world systems, from the socio-economic domain to power grids and the Internet, can be represented as large scale complex networks. Measuring and visualizing structural characteristics and the dynamics of a network may lead to new level of undersanding of complex processes such as virality of information, diffusion, synchronization, and resilience. New computational and algorithmic challanges arise to deal with large datasets, and novel applications must be designed according to natural self organization of such complex systems.
Algoritmi di apprendimento automatico con garanzie di privacy in domini complessi/Privacy-preserving machine learning in complex domains	Ruggero G. Pensa	Today's systems produce a rapidly exploding amount of data, and the data further derives more data, forming a complex data propagation network. In such a scenario, citizens’ privacy is constantly put at risk, so that the need for data protection is at the very centre of the public debate. Hence, machine learning and data science algorithms cannot ignore privacy constraints. Privacy-preserving machine learning is the ability of learning from data without disclosing any private information of the users. Among all proposed approaches, differential privacy has gained popularity due to its effectiveness and robustness in many application domains. Many privacy-preserving algorithms have been proposed in structured domains (databases, tables, statistical data), however, preserving privacy in unstructured domains (e.g., free text, images, videos and so on), is a novel and challenging task. This project concerns the study, design, development and test of new privacy preserving machine learning algorithms as well as the design of machine learning methodologies for privacy risk assessment in complex structured and unstructured domains.
Cyber-Physical Systems	Enrico Bini	Cyber-Physical Systems (CPS) are physical processes controlled by a pervasive network of embedded computers. In CPS, computation, communication, and control of the physical process are tightly coupled and constrained by the environment. Hence, the design of each dimension in isolation introduces cost-inefficient solutions. Examples of CPS are: smart grids for energy supply and demands, smart transportation in smart cities, autonomous vehicles. In this research proposal, the theoretical foundations of CPS will be investigated, in accordance with the background of the candidate.
Foundations for Emerging Computing Paradigms	Luca Paolini	The success of classical digital computer is unquestionable because they pervasiveness. However, it is little known that digital computers have been in competition for many decades with analogue computers. The main reason that driven the present predominance of the former are their solid theoretical foundations. Today, many alternative computating models are challenging this egemony in a decisive way: quantum, reversible, biological, analogic computation, etc ... The study of the theoretical foundations of these models is a pioneering and fascinating research that requires broad spectrum mathematical notions: logical, categorical, combinatorial, probabilistic, algebraic ones and not only. Indeed, this research-lines often originates in the studies that refine our comprehension of the classical computing and pioneer to hybrid solutions (in terms of computing models) for already in use technologies.
Modellazione, Verifica e Riuso di Sistemi / System Modelling, Verification and Reuse	Ferruccio Damiani	The main research goal of the MoVeRe (System Modelling, Verification and Reuse, http://di.unito.it/movere) group is to contribute to an effective seamless integration of Formal Methods into software and system development methodologies. The research interests of the group span from foundational aspects to tools for supporting rigorous engineering of industrial systems. PhD projects on the following topics are curently available: * Computational models and languages, Domain specific languages; * Concurrent, distributed, and mobile systems; * Cyber-Physical Systems, Internet of Things, Smart Cities, Wireless Sensor Networks; * Edge/Fog/Cloud Computing; * Self-organization, Swarm intelligence; * Static and dynamic analysis techniques; * System evolution and dynamic software updates; * Variability modeling and software product lines. The reseach group has well-established collaborations with research institutions based in Europe, Japan and USA.
Data Science for Social Good	Rossano Schifanella	New technologies have made the collection and the analysis of data – by governments, private companies, or innovative researchers – possible, making available large-scale collections of digital traces of human behaviour at an unprecedented breadth, depth and scale. This project aims at blending machine learning, spatial analysis, network science, text+image analysis, and data visualization, to model human dynamics at scale and to tackle real-world societal challenges. In this context, three main sub-themes are suggested: (1) The New Science of Cities: social media feeds, transit cameras, mobile phones, mapping services and sensors, provide a real-time picture of how cities work and enable the study of a wide range of issues affecting the everyday lives of citizens. This theme aims at modeling multi-modal mobility and human-centered approaches to explore the city, liveability and sustainability of urban areas, the relation between space, social life and well-being. (2) Computational Social Science: this theme aims at studying socio-cultural phenomena through large-scale digital datasets and social media platforms, and at proposing innovative applications to issues of societal interest, e.g., spreading of misinformation, bias towards minorities, migrations. It involves projects in the field of social innovation, philanthropy, international development and humanitarian action. Particular attention will be drawn on important aspects like data ethics, privacy and algorithmic transparency. (3) Digital Health: digital technologies have huge potential to improve health and wellbeing of citizens, enabling new approaches for patients, clinicians and researchers to manage healthcare more effectively. This theme focuses on the development of mathematical and computational tools to model social, cultural, and economic determinants that affect health. Particular focus will be drawn on the relation between eating habits, drugs consumption and daily habits.
Ottimizzazione nei sistemi sanitari / Operational Research Applied to Health Services	Roberto Aringhieri e Andrea Grosso	Among the many fields where operational research and computers science meet, health care is surely one of the more vital nowadays. Health care is a very relevant research topic also for the impact on public opinion and for fuelling large discussions and debates. The most challenging aspect in health care stems from the high complexity of the system itself, its intrinsic uncertainty and its dynamic nature. This project will be focused on advancing the state of the art in the area of operational research methodologies applied to health services. Possible topics are the large and complex field of planning and scheduling health care activities, resources and personnel, and the management of the emergency medical service, emergency department, and humanitarian logistics.
Modellazione di bias personali in modelli supervisionati / Bias-aware Supervised Machine Learning	Valerio Basile	Modern Artificial Intelligence is often based on large datasets created by humans, used to train supervised machine learning models. When applied to highly sensitive tasks such as automatic content moderation, abusive language detection, or hate speech monitoring, the individual perception of the human annotators given by their cultural and demographic background has a significant impact on the quality and fairness of the AI prediction. This project focuses on developing computational techniques and machine learning models that incorporate such personal bias, and leverage it as additional knowledge in order to provide more accurate, ethical, and explainable predictions.
Utilizzo della qualita' dei dati nei sistemi di suggerimento personalizzati basati su trust / Leveraging information quality in trust-based recommender systems	Liliana Ardissono	To overcome information overloading on the web, recommender systems have become an important component of most current online services, because they assist the users to retrieve the data relevant to their needs. However, recommender systems rely on evaluations provided by users on experienced items, and this information suffers from reliability issues because users might provide low-quality evaluations, or even voluntarily provide biased evaluations. The concept of “trusted user” is important to overcome these issues, which are crucial for Responsible AI. This project aims at advancing the state of the art in trust-based recommender systems by analyzing heterogeneous types of information which can be exploited to assess the quality of the evaluations provided by users, e.g., through an analysis of review helpfulness and of other types of public information about users.
Artificial Intelligence and Machine Learning for Common Sense Reasoning	Luigi Portinale	Modern AI systems need to exploit suitable knowledge bases as well as to follow reasoning patterns based on common-sense reasoning. Even if this ideas were already at the core of some "historical" AI systems and approaches, they are becoming of paramount importance now in the era of big-data. We have now access to a potentially unlimited amount of data sources, but some approaches based on this like Deep Learning, while being very successful in several application tasks, still exhibit great limits, especially in providing semantic explanations to the underlying reasoning pattern. Knowledge Graph (KG) approaches and techniques start from seminal ideas in AI stemming from semantic networks, to pursue the goal of supporting common sense reasoning, by exploiting machine learning from big-data sources as well as natural language interpretation. A PhD project is available in the area on Knowledge Graphs, with particular attention to techniques for learning and representing KGs, reasoning on KGs potentially combining different form of reasoning patterns, and to the exploitation of such techniques for building recommender and personalized intelligent systems.
Tecnologie semantiche per i beni culturali, rappresentazione e disseminazione/Semantic technologies for cultural heritage representation and dissemination	Rossana Damiano e Antonio Lieto	The paradigm of Linked Data has brought about dramatic changes to cultural heritage, thanks to a long tradition of standardization and interoperability. Cultural heritage resources can now be automatically interconnected and described through established, unambiguos vocabularies, with benefits for preservation, study and dissemination. The benefits of this approach for dissemination, however, have not been explored to depth. The project will explore the use of semantic representations of cultural heritage for the creation of an interpretation layer targeted at cultural heritage professionals and citizens, with the goal of developing tools that exploit interpretive patterns inspired by narratology and media to support the process of interpreting and communicating cultural heritage resources. The project will be carried out in cooperation with the EU H2020 project SPICE (Social Participation and Inclusion through Cultural Engagement, 2020-22) where the Computer Science is partner.
Artificial Intelligence for Dependable and Critical Systems	Luigi Portinale	In order to model and analyse dependable and critical systems, knowledge about the system behavior, as well as the operating environment should be suitably exploited. AI techniques, especially those based on Probabilistic Graphical Models for reasoning under uncertainty, can be really useful in such a context. PhD projects are available aimed at studying new formalisms based on the PGM paradigm and on Machine Learning for applications in FDIR (Fault Detection Identification and Recovery), cyber-security, predictive maintenance, IoT
Adaptive Cyber Security	Francesco Bergadano	Cyber Security systems and methodologies must continuously and automatically adapt to context, regulations, and new threats. Defense systems should then be able to change automatically, and use previous attack examples to improve their effectiveness. Yet, they should also hide their changing strategies from adversaries, and avoid adversarial manipulation. These considerations spawn a wide range of technologies in diverse application areas of Cyber Security, including continuous user authentication, malware and anomaly detection, compliance monitoring and security intelligence.
Intellgent Conversational Agent	Federica Cena	As intelligent assistants such as Siri,Amazon Alexa, Google Assistant, enter the daily life of users, research on conversational information systems is becoming increasingly important. On the other hand, despite much recent success in natural language processing and dialogue research, communication between a human and a machine is still in its infancy. To improve user satisfaction, users and systems need to know each other well since it is important to build relationships of trust. Thus, dialogue personalization is an important issue: if such systems could consider users’ experiences and interests when engaging them in a conversation, it would greatly improve user satisfaction. In this line, we propose to exploit Artificial Intelligence, in form of Machine Learning and Natural Language Processing, as well as User Modeling techniques, to enhance an existing chatbot, in order to be able to show an intelligent behavior in relation to the specific user.
Model checking quantitativo/Quantitative Model Checking	Jeremy Sproston	Model checking is a system verification technique for establishing whether a model of a system satisfies a number of formally-specified correctness properties. Model checking has been extended so that quantitative information, for example regarding continuous, timed or probabilistic behavior, can be represented both in system models and in the correctness properties. The project concerns the development of model-checking techniques for quantitative aspects of embedded software and cyber-physical systems in general, with particular focus on timed and probabilistic issues
Modelli di programmazione paralleli per applicazioni moderne: AI and BigData/Parallel programming models for modern applications: AI and BigData	Marco Aldinucci	The main challenge for parallel execution of algorithms is to expose sufficient concurrency in the first place. Interestingly, most if not all, mathematical models can be solved using a number of different algorithms — some of which expose much more concurrency than others. Traditionally, computational scientists have chosen algorithms according to desired mathematical properties or total computational complexity, but not concurrency. PhD projects are available aiming at wil looking at new algorithms and methods which are highly suited for parallel execution, also looking at novel ground-breaking methods such as data-centric approaches (e.g. bigdata analytics and machine learning).
Explainable and Trustable Machine Learning	Rosa Meo	Today, many popular Machine Learning models, such as Deep Neural Networks, are like black boxes that provide outcomes and predictions that are difficult to understand and therefore are not trustable. We therefore need to provide explainations for the outcomes. The answers could come from the use of adversarial learning and the explanations could be constructed thanks to methodologies that make use of counterfactual analysis.
Analisi del Sentimento con Impatto Sociale: Trattamento Computazionale degli Stereotipi / Sentiment Analysis applied to Societal Challenges: Computational Investigation of Stereotypes	Cristina Bosco e Valerio Basile	Recent advances in sentiment analysis applied to societal challenges (like detection of abusive language, aggressiveness, toxicity, mysoginy, ecc.) raise several new issues for the researchers, as highlighted by the evaluation campaigns for this area (see e.g. SemEval, IberEval and Evalita series). For addressing them, novel approaches must be developed by carefully taking into account different text genres (social media, newspapers) and targets (immigrants, women or LGTB community members), but also by providing an in-depth analysis of linguistic devices (e.g. irony, metaphor or sarcasm) and of cognitive and behavioral mechanisms acting beyond them (e.g. stereotypes, discredit, ...). The PhD project will be focused on studying and modelling communication mechanisms from the theoretical point of view and on developing computational approaches to deal with them. Particular attention will be given to the detection and explanation of stereotypes, by means of hybrid techniques including formal logic (a stereotype entails a generalization) and machine learning.
Improving model checking algorithms and data structures	Elvio Amparore	Formal verification has become viable for large scale systems with the use model checking techiniques based on Decision Diagrams for the representation of the state space. Decision Diagrams are a data structure for the encoding of sets of structured elements, and its efficiency is critically controlled by the order of the variables in those structured elements. The project will be focused on studying and developing of new techniques to improve the efficiency of model checking using decision diagrams. Topics tha will be the focus of the project include algorithmic optimization of data structures, application of model checking logics to the verification of systems.
Research on safe and explainable AI-based components for decision making in automotive systems	Elvio Amparore, Marco Botta	The intelligent systems that are progressively being used in the automotive field for decision making and traffic control work by collecting and processing data from a heterogeneous set of networked sensors. Sensor information needs to be processed, fused and rigorously interpreted, using a toolbox of techniques ranging from signal processing to advanced black-box AI techniques. Research on automotive-grade AI components focuses both on a high level of control, and on the inspectability of the decision process logics. The candidate will investigate the topics of safe and explainable AI components in the automotive field, applied on innovative sensing solutions to monitor driver state and behaviour. The research will focus on novel techniques based on eXplainable AI methods that will allow to inspect whether recognition AI components meet accuracy and safety requirements, as well as research on real-time driver state and environment monitoring. EU ECSEL project: Next Perception, and Innovative Training Network (ITN): Assured-AI.
Computational Linguistics and Interactive Storytelling Against Hate: Supporting Creative Counter Narratives Against Hate Speech Online	Rossana Damiano, Viviana Patti	Stories own the unique quality of conveying information in an easily processed, compact and appealing format, suitable to trasmit social and cultural values in a way that engages the audience with identification emotional participation. Computational modelling of stories aims at developing tools and resources to extract narrative content from media, and to generate engaging, effective narratives for communication purposes. The generation of counter narratives can help to contrast hate speech online, and provide a space to support diverse viewpoints that can question haters' simplified generalisations. The main aim of the project is to explore the possibility to combine AI methods from computational linguistics and interactive storytelling to analyse and deconstruct the narratives on which hate messages are based, and to support the development of alternative and creative narratives based on human rights and democratic values such as openness, respect for difference and equality.
Conversational Agents for Behavior Change	Amon Rapp	Novel conversational agents, such as chatbots, training assistants, Amazon Alexa, etc., open new opportunities for changin people's behavior. There is an increasing research interest on how we can effectively modify individuals' habits toward, for instance, healthier, more sustainable, safer, lifestyles. Conversational interfaces could bring more effective interventions based on language, making people reflect on their behavior, or persuading them to change it. This interdisciplinary project brings together psychology, human-computer interaction, AI, machine learning and Natural Language Processing, to design and develop intelligent conversational assistants able to "understand" the user and establish a dialogue with her in order to improve her psychological and physical wellness.
Commonsense Reasoning for Dynamic Knowledge Bases and Computational Creativity Applications	Antonio Lieto, Gian Luca Pozzato	Inventing novel concepts by combining the typical knowledge of pre-existing ones is one the most creative cognitive abilities exhibited by humans. Dealing with this problem requires, from an AI perspective, the harmonization of two conflicting requirements that are hardly accommodated in symbolic systems: the need of a syntactic compositionality (typical of logical systems) and that one concerning the exhibition of typicality effects. The main aim of this project is to provide a logical framework able to account for this type of human-like and human-level concept combination based on a nonmonotonic Description Logic with a probabilistic semantics. The applications of such a logical framework may impact on a wide variety of fields in the areas of creative industry, ranging from the creation of new characters in the movie industry (where, for example, the creation of a new protagonist for a cartoon can be obtained by combining some typical features from previous characters in a novel, surprising, way) to the automatic generation of novel and cognitively-plausible landscape scenarios, narrative story-lines in videogames, or the design of innovative products in fast-moving domains, such as fashion. The proposed logical framework can be generally thought as a problem solving heuristic applied by artificial agents (including robots) in any situation where there is the need of inventing a novel solution by leveraging from a repertoire of pre-existing knowledge to be recombined. We also would like to start to investigate possible relationships and mutual influences of other AI techniques exploited in similar settings, in particular, Machine Learning.
Conversational Interfaces and Natural Language Generation for Artificial Intelligence / Interfacce conversazionali e generazione automatica del linguaggio naturale per l'intelligenza artificiale	Alessandro Mazzei, Luca Anselma	The natural language is the most sophisticated technique that machines can use in order to communicate with humans. Three fields of artificial intelligence that can be productively coupled with Conversational Interfaces (CI) and Natural Language Generation (NLG) are (1) Automatic Reasoning explanation, (2) Social Robot Interaction, (3) Assistive Technologies. Therefore, the project will consist in the study, the design and the application of CI and/or NLG to one of these fields.
Accountability computazionale/Computational accountability	Matteo Baldoni, Cristina Baroglio, Roberto Micalizio	Business processes represent an important tool for rationalizing business resources and cross-business relationships. Business processes are mainly based on a procedural and prescriptive representation of activities. Their main disadvantages are: the concerned parties cannot take advantage from occasions nor adapt to adverse situations; there is no representation of accountability; they are not suitable to realize socio-technical systems. The proposed research aims at studying social relationships as an alternative for realizing business processes. Social relationships capture dependencies among the actors, capture in a natural way a notion of accountability, and they evolve exclusively after the observation of social behaviour. Due to their declarative nature they are suitable to describe business processes in a minimally descriptive way.
Interazione e coordinazione di sistemi multiagente basata su relazioni sociali /Interaction and coordination based on social relationships for Multiagent Systems	Matteo Baldoni, Prof. Cristina Baroglio	Commitments and, more generally, social relationships, are the emerging paradigm for modeling interactions and coordination in multiagent systems. This research will investigate the use of social relationships both form a theoretical and practical point of view. A special attention will be posed on data-awareness and norm-awareness.
Efficient state space exploration for simulation	Susanna Donatelli	SImulation is a technique used for evaluating the performance indicators of a system (like throughput and reliability) whenever analytical solution is not possible. For systems described with queueing networks, stochastic Petri nets or stochastic process algebras this typically happens when the state space of the system is too large or even infinite. Techniques for model-checking based on efficient data structures like decision diagrams are nowdays able to deal with very large state spaces. This project will investigate the synergic use of model-checking techniques for stochastic simulation, with the goal of exploiting the knowledge of the state space structure to increase the quality of simulation results.
Natural Language Processing and text understanding: dealing with context and events/NLP e text understanding: il trattamento del contesto e degli eventi	Daniele Radicioni	AI applications are today facing unprecedented requests: dealing with manyfold unstructured text documents (as diverse as news reports, product reviews, scientific papers, social media communications, magazines, etc.) to extract factual information, specific information and trends, opinions and latent biases. While in the last few years many successful lexical resources have been proposed to deal with many traditional NLP tasks, two influential areas of investigation are emerging. The former has to do with the contextual nature of lexical meaning, which is at the base of semantic composition; the latter involves considering verbal semantics, and extending it in order to account for events. This doctoral proposal is aimed at targeting such problems. Various applicative domains (amongst which documents categorization, keywords extraction, open domain question answering, text summarization, etc.) will be considered, and manyfold applications can be envisioned, also in accord with the interest of candidates, to tackle, e.g., figurative uses of language, fake detection in social media, knowledge graphs induction and embedding, events detection and extraction. Full opportunity will be given to the PhD candidates to pursue their interests in choosing the application field(s), such as legal domain, medical domain, cultural heritage, physical sciences. Further information on broader scientific interests and recent work can be found at http://ls.di.unito.it.
Leveraging Big Data Analysis to understand emerging phenomena in complex systems	Maria Luisa Sapino	Big data analysis is increasingly critical for understanding spatio-temporal dynamics of emerging phenomena. The key characteristics of data sets and models relevant to big data analysis of complex events often include the following: (a) noisy, (b) multi-variate, (c) multi-resolution, (d) spatio-temporal, and (e) inter-dependent. Because of the volume and complexity of the data, the varying spatial and temporal scales at which relevant observations are made, today domain experts lack the means to adequately and systematically interpret these observations and understand the underlying events and processes. We will address computational challenges that arise from the need to process, index, search, and analyze, in a scalable manner, large volumes of multivariate data.
Tecniche Avanzate di Intelligenza Artificiale e di Basi di Dati Temporali in Medicina: Teoria ed Applicazioni / Advanced Artificial Intelligence and Temporal Database Techniques in Healthcare: Theory and Applications	Paolo Terenziani	The project is part of a long-term cooperation started in 1997 with ASU San Giovanni Battista in Turin (one of the major hospitals in Italy), and aims at developing intelligent computer-based techniques to support physicians in decision making and in the treatment of patients through clinical guidelines. The overall project, known as “GLARE” (Guideline Acquisition, Representation and Execution), is a collector of many different sub-projects, each one typically involving the development and\or application of advanced Artificial Intelligence techniques and methodologies. Typical areas of interest include knowledge acquisition, knowledge representation, knowledge-based verification, conformance analysis, temporal reasoning. Other sub-projects focus on the development and application of advanced techniques to cope with time in relational DBs, to manage the temporal dimension of patients’ data. Applications include (support to) medical decision making, medical education, treatment of patients with multiple diseases.
Aggregate Computing for IOT	Apprenticeship at Reply S.p.A Contact: Prof. Ferruccio Damiani	The increase of the number of devices in the Internet of Things (IoT) and swarm robotics will soon make it infeasible to deploy a global-level software functionality by following the practice of individually programming every single device. Aggregate computing is an innovative approach for programming the IoT—the key idea is to simplify the software development by programming a large system as a whole: the desired system-level behavior is expressed by a declarative, mathematically and formally tractable global specification; then individual devices are automatically bounded to play the corresponding local behavior of that specification. In this project we will: design and implement API to support using the aggregate computing programming model in the context of mainstream IoT platforms, identify paradigmatic case studies, delineate the requirements that need to be satisfied by a solution to them, design aggregate computing algorithms which satisfies these requirements, and finally implement those algorithms in order to validate the approach experimentally on real systems.