02 Robust AI systems for data-limited applications (Prof. Santa Di Cataldo)
03 Artificial Intelligence applications for advanced manufacturing systems (Prof. Santa Di Cataldo)
04 Digital Wellbeing by Design (Prof. Alberto Monge Roffarello)
05 Goal-Oriented Adaptive Learning for 6G (Prof. Claudio Ettore Casetti)
06 Security of Linux Kernel Extensions (Prof. Riccardo Sisto)
07 Local energy markets in citizen-centered energy communities (Prof. Edoardo Patti)
08 Simulation and Modelling of V2X connectivity with traffic simulation (Prof. Edoardo Patti)
13 Privacy-Preserving Machine Learning over IoT networks (Prof. Valentino Peluso)
14 Data-Driven and Sustainable Solutions for Distributed Systems (Prof. Guido Marchetto)
15 Single-cell Multi-omics for Understanding Cellular Heterogeneity (Prof. Stefano Di Carlo)
19 Safety and Security of AI in Space and Safety Critical Applications (Prof. Stefano Di Carlo)
20 Non-invasive and low-cost solutions for health monitoring (Prof. Massimo Violante)
22 Innovative technologies for infrastructures and buildings management (Prof. Valentina Gatteschi)
24 Video Retrieval-Augmented Generation (Prof. Luca Cagliero)
25 Human-Centered AI within Internet-of-Things Ecosystems (Prof. Luigi De Russis)
26 Preference models for multimodal annotations (Prof. Luca Cagliero)
28 Spatio-Temporal Data Science (Prof. Paolo Garza)
30 AI4CTI - ARTIFICIAL INTELLIGENCE FOR CYBER THREAT INTELLIGENCE (Prof. Marco Mellia)
Evaluating work-induced stress and cognitive decline using wearables and AI algorithms | |
| Proposer | Gabriella Olmo, Luigi Borzì, Marco Ghislieri |
| Topics | Data science, Computer vision and AI, Life sciences |
| Group website | https://www.smilies.polito.it/ https://www.biomedlab.polito.it/ |
| Summary of the proposal | The level of emotional activation is recognized to affect both a person's work performance and his/her safety at work, as well as general health. On the other hand, the work environment is one of the major sources of dysfunctional stress. This proposal refers to the development and implementation of a protocol for objective quantification of work-related stress conditions, and to the identification of possible correlation with the decline in cognitive abilities caused by work-related stress. |
| Research objectives and methods | The primary objective of this proposal is to design and implement a prototypal BAN (Body Area Network) made of low-cost, low-impact commercial wearables, to evaluate working-related distress and its correlation with cognitive decline. Specific objectives consist in the implementation of Artificial Intelligence (AI) algorithms working on heterogeneous and multidimensional health data, with attention to interpretability and generalizability of the results. It will be possible to validate the prototype against gold standard instrumentation available at PolitoBIOMedLab, and to discuss the clinical implications of the results, increasing the clinical and psychological knowledge on the correlation between stress and cognitive decline. We plan to have at least a journal paper published per year. |
| Required skills | Expertise in the fields of Signal Processing, Data Analysis, Statistics and Machine Learning (e.g. feature selection and ranking, supervised and unsupervised learning). Basic knowledge of bio-signal data processing (EEG, ECG, EMG, EOG). Good knowledge of C, Python, Matlab, Simulink programming languages. - Good relational abilities and knowledge of the Italian language, to effectively manage interactions with participants during the evaluation trials. |
Robust AI systems for data-limited applications | |
| Proposer | Santa Di Cataldo, Francesco Ponzio |
| Topics | Data science, Computer vision and AI |
| Group website | https://www.linkedin.com/company/edagroup-polito/ |
| Summary of the proposal | Artificial Intelligence is driving a revolution in many important sectors in society. Deep learning networks, and especially supervised ones such as Convolutional Neural Networks, remain the go-to approach for many important tasks. Nonetheless, training these models typically requires massive amount of good-quality annotated data, which makes them impractical in many real-world applications. This PhD program seeks answers to such problems, targeting important use-cases in today's society. |
| Research objectives and methods | The main goal of this PhD program is the investigation of robust AI-based decision making in data-limited situations. This includes three possible scenarios, which are typical of many important real-world applications:- the training data is difficult to obtain, or it is available in limited quantity.- obtaining the training data is not difficult. Nonetheless, it is either difficult or economically impractical to have human experts labelling the data.- the training data/annotations are available, but the quality of such data is very poor. Possible solutions involve different approaches, from classic transfer learning and domain adaptation techniques, data augmentation with generative modelling, or semi- and self-supervised learning approaches, where the access to real data of the target application is either minimized or avoided altogether. In addition, the use of probabilistic approaches (e.g., Bayesian inference) can be of help to properly quantify the uncertainty level both at training and inference time, making the decision process more robust both to noisy data and/or inconsistent annotations. This research proposal aims to investigate and advance the state of the art in such areas. The outline can be divided into 3 consecutive phases, one per each year of the program:- In the first year, the candidate will acquire the necessary background by attending PhD courses and surveying the relevant literature and will start experimenting on the available state-of-the-art techniques. A seminal conference publication is expected at the end of the year.- In the second year, the candidate will select and address some relevant use-cases, well-representing the three data-limited scenarios mentioned before. Stemming from the supervisors' collaborations and current research activity, these use-cases may involve industry 4.0 applications (for example: smart manufacturing and industrial 3D printing) as well as biomedicine and digital pathology. There is some scope to shape the specific focus of such use-cases with the interests and background of the prospective student, as well as with the ones of the various collaborators that could be involved in the project activity: research centers such as the Inter-departmental Center for Additive Manufacturing in PoliTO, the National Institute for Research in Digital Science and Technology (INRIA, France) as well as industries such as Prima Industrie, Stellantis, Avio Aero, etc. At the end of the second year, the candidate is expected to target at least a paper in a well-reputed conference in the field of applied AI, and possibly another publication in a Q1 journal of the Computer Science sector (e.g., Pattern Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, etc.)- In the third year, the candidate will consolidate the models and approaches that were investigated in the second year, and possibly integrate them into a standalone architecture. The candidate will also finalize this work into at least another major journal publication, as well as into a PhD thesis to defend at the end of the program. |
| Required skills | The ideal candidate to this PhD program has: - positive attitude to research activity and working in team - solid programming skills - solid basics of linear algebra, probability, and statistics - good communication and problem-solving skills - some prior experience in the design and development of machine learning and deep learning architectures. |
Artificial Intelligence applications for advanced manufacturing systems | |
| Proposer | Santa Di Cataldo, Francesco Ponzio |
| Topics | Data science, Computer vision and AI |
| Group website | https://eda.polito.it/ https://www.linkedin.com/company/edagroup-polito/ |
| Summary of the proposal | Industry 4.0 refers to digital technologies designed to sense, predict, and interact with production systems, to make decisions that support productivity, energy-efficiency, and sustainability. While Artificial Intelligence plays a crucial role in this paradigm, many challenges are still posed by the nature and dimensionality of the data, and by the immaturity and intrinsic complexity of some of the processes involved. The aim of this PhD program is to successfully tackle these challenges. |
| Research objectives and methods | The main goal of this PhD program is the investigation, design and deployment of state-of-the-art Artificial Intelligence approaches in the context of the smart factory, with special regards with new generation manufacturing systems. These tasks include: - quality assurance and inspection of manufactured product via heterogeneous sensors data (e.g., images from visible range or IR cameras, time-series, etc.) - process monitoring and forecasting - anomaly detection - failure prediction and maintenance planning support While the Artificial Intelligence technologies able to address such tasks may already exist and be successfully consolidated in other real-world applications, the specific domain of manufacturing systems poses severe challenges to the effective deployment of these techniques. Among the others: - the immaturity of the involved technologies - the complexity of the underlying physical/chemical processes - the lack of effective infrastructures for data collection, integration, and annotation - the necessity to handle heterogeneous and noisy data from different types of sensors/machines - the lack of annotated datasets for training supervised models - the lack of standardized quality measures and benchmarks This PhD program seeks solutions to these challenges, with specific focus on new generation manufacturing systems involving complex processes. For example: Additive Manufacturing (AM) and semiconductor manufacturing (SM). - AM includes many innovative 3D printing processes, which are rapidly revolutionizing manufacturing in the direction of higher digitalization of the process and higher flexibility of production. AM involves a fully digitalized process from design to product finishing, and hence it is a perfect candidate for the deployment of Artificial Intelligence. Nonetheless, it is a very complex and still immature technology, with tremendous room for improvement in terms of production time and product defectiveness. Specific use-cases in this regard will stem from the supervisors' collaborations with the Inter-departmental Center for Additive Manufacturing in Politecnico di Torino, as well as with several major industrial partners such as Prima Additive, Stellantis, Avio Aero, etc. - SM is another highly complex process, entailing a wide array of subprocesses and diverse equipment. Driven by the Industry 4.0 revolution and European Chips Act, the semiconductor industry is investing heavily in the digitalization of its production chain. As a result of these investments, the chip production process has been equipped with multiple sensors that constantly monitor the evolution of each manufacturing phase, from oxidation to testing and packaging, thus collecting a tremendous amount of heterogeneous data. To fully unveil the potential and hidden knowledge of such data, Artificial Intelligence is widely acknowledged to have a fundamental role. Use-cases in this regard will stem from the supervisors' collaborations with important industrial players in this sector, such as STMicroelectronics. The outline of the PhD program can be divided into 3 consecutive phases, one per each year of the program. - In the first year, the candidate will acquire the necessary background by attending PhD courses and surveying the relevant literature and will start experimenting state-of-the-art techniques on the available datasets, either from public sources or from past projects of the supervisors. A seminal conference publication is expected at the end of the year. - In the second year, the candidate will select and address some relevant use-cases, with real data from the industrial partners, and will seek solutions to the technological and computational challenges posed by the specific industrial application. At the end of the second year, the candidate is expected to target at least a second conference paper in a well-reputed industry-oriented conference (e.g. ETFA), and possibly another publication in a Q1 journal of the Computer Science sector (e.g. IEEE Transactions on Industrial Informatics, Expert Systems with Applications, etc.). In the third year, the candidate will consolidate the models and approaches that were investigated in the second year, and possibly integrate them into a standalone framework. The candidate will also finalize this work into at least another major journal publication, as well as into a PhD thesis to defend at the end of the program. |
| Required skills | The ideal candidate to this PhD program has: - positive attitude to research activity and working in team - solid programming skills - solid basics of linear algebra, probability, and statistics - good communication and problem-solving skills - some prior experience in the design and development of machine learning and deep learning architectures. - some prior knowledge/experience of manufacturing processes is a plus, but not a requirement. |
Digital Wellbeing by Design | |
| Proposer | Alberto Monge Roffarello, Luigi De Russis |
| Topics | Computer graphics and Multimedia, Data science, Computer vision and AI, Software engineering and Mobile computing |
| Group website | https://elite.polito.it/ |
| Summary of the proposal | Tools for digital wellbeing allow users to self-control their habits with distractive apps and websites. Yet, they are ineffective in the long term, as tech companies still adopt attention-capture designs, e.g., infinite scroll, that compromise users' self-control. This PhD proposal investigates innovative strategies for designers and end users to consider digital wellbeing in user interface design, recognizing the need to foster healthy digital experiences without depending on external support. |
| Research objectives and methods | In today's attention economy, tech companies compete to capture users' attention, e.g., by introducing visual features and functionalities - from guilty-pleasures recommendations to content autoplay - that are purposely designed to maximize metrics such as daily visits and time spent. These Attention-Capture Damaging Patterns (ACDPs) [1] compromise users' sense of agency and self-control, ultimately undermining their digital wellbeing. As of now, the HCI research community has traditionally considered digital wellbeing an end-user responsibility, enabling them to self-monitor their usage of apps and websites through tools for digital self-control. Nevertheless, studies have shown that these external interventions - especially those that are overly dependent on users' self-monitoring capabilities - are often ineffective in the long term. Taking a complementary perspective, the main research objective of this PhD proposal is to explore how to make digital wellbeing a top-design goal in user interface design, establishing a fruitful collaboration between designers and end users and recognizing the critical necessity to foster healthy online experiences and address potential negative impacts of ACDPs on users' mental health without depending on external support. The PhD student will study, design, develop, and evaluate proper models and novel technical solutions (e.g., tools and frameworks) to support designers and end users in fostering the creation of user interfaces that preserve and respect user attention by design, starting from the relevant scientific literature and performing studies involving designers and end users. In particular, possible areas of investigation are:- Innovating frameworks that define and educate designers on novel theoretically grounded processes that prioritize digital wellbeing. These processes will build upon existing design guidelines and best practices, providing clear guidance on their application and providing tech companies and designers with actionable insights to transition away from the contemporary attention economy.- Creating a validated taxonomy of positive design patterns that respect and preserve the user's attention. These patterns will promote users' agency by design and support reflection by offering the same functionality as ACDPs. - Developing design tools to support designers in prioritizing users' digital wellbeing in real-time. Using artificial intelligence and machine learning models, these tools may detect when a designed interface contains ACDPs and/or fails to address digital wellbeing guidelines, suggesting positive design alternatives.- Developing strategies that empower end users to actively participate in designing technology that prioritizes digital wellbeing. This may include the development of platforms for co-designing user interfaces, as well as mechanisms for evaluating existing user interfaces against ACDPs and giving feedback. The proposal will adopt a human-centered approach, and it will build upon the existing scientific literature from different interdisciplinary domains, mainly from Human-Computer Interaction. The work plan will be organized according to the following four phases, partially overlapped:- Phase 1 (months 0-6): literature review at the intersection of digital wellbeing, design, and ACDPs; focus groups and interviews with designers, practitioners, and end users; definitions of a set of use cases and promising strategies to be adopted.- Phase 2 (months 3-24): research, definition, and evaluation of design frameworks and models of positive design patterns. Here, the focus will be on the design of user interfaces for the most commonly used devices, i.e., the smartphone and the PC.- Phase 3 (months 12-36): research, definition, and experimentation of design tools to support designers in prioritizing users' digital wellbeing in real-time, integrating frameworks, design guidelines, and positive design patterns explored and defined in the previous phase.- Phase 4 (months 24-36): extension and possible generalization of the previous phases to include additional devices; evaluation in real settings over long period of times of the proposed solutions; development and preliminary evaluation of strategies for end-user collaboration. It is expected that the results of this research will be published in some of the top conferences in the Human-Computer Interaction field (e.g., ACM CHI, ACM CSCW, and ACM IUI). Journal publications are expected on a subset of the following international journals: ACM Transactions on Computer-Human Interaction, ACM Transactions on the Web, ACM Transactions on Interactive Intelligent Systems, and International Journal of Human Computer Studies. [1] A. Monge Roffarello, K. Lukoff, L. De Russis, Defining and Identifying Attention Capture Deceptive Designs in Digital Interfaces, CHI 2023, https://dl.acm.org/doi/abs/10.1145/3544548.3580729 |
| Required skills | A candidate interested in the proposal should ideally: - be able to critically analyze and evaluate existing research, as well as gather and interpret data from various sources; - be able to communicate research findings through writing and presenting; - have a solid foundation in computer science/engineering and possess relevant technical skills; - have a good understanding of HCI research methods, especially around needfinding. |
Goal-Oriented Adaptive Learning for 6G | |
| Proposer | Claudio Ettore Casetti, Marco Rapelli |
| Topics | Software engineering and Mobile computing, Data science, Computer vision and AI |
| Group website | |
| Summary of the proposal | 6G will connect autonomous systems, requiring a shift from traditional data delivery to goal-oriented communication. This AI-driven approach prioritizes relevant data for decision-making, optimizing efficiency. Key areas include intelligent routing, semantic exchange, and intent-based models. This PhD research aims to develop AI-orchestrated data exchange, explore causal inference and contrastive learning for relevance extraction, and design adaptive, task-driven networking frameworks. |
| Research objectives and methods | Outline Goal-oriented communication can transform networking into an intelligent, context-aware, and task-driven system by focusing on:- Intelligent Routing and Data Prioritization: Instead of treating all packets equally, goal-oriented communication prioritizes and routes information based on its relevance to an ongoing process. For example, in an autonomous traffic management system, real-time hazard notifications should take precedence over general telemetry data.- Semantic and Task-Aware Information Exchange: Networks will no longer transmit all available data but instead extract and share only the information necessary for AI models or human users to make a decision. For example, in industrial automation, rather than sending thousands of sensor readings per second, a machine could communicate only when an anomaly is detected, significantly reducing bandwidth and computation costs.- Intent-Based and Goal-Driven Communication Models: Goal-oriented networks move beyond conventional request-response models to intention-based data exchange, where AI-driven entities anticipate what information is needed to complete a task and optimize communication accordingly. For instance, in autonomous vehicle coordination, a vehicle does not need to continuously broadcast its speed and position but only shares critical updates when approaching intersections or hazards. The main objectives of this PhD research are:- Define AI-Orchestrated Data Exchange Models, by developing AI-driven approaches to filter, prioritize, and exchange only task-relevant information between connected devices and infrastructures. The PhD candidate will be required to investigate techniques such as semantic communication, federated meta-learning for adaptive network intelligence, and goal-oriented routing to optimize network resource utilization. - Investigate innovative AI techniques to be used with goal-oriented communication, such as Dynamic Causal Inference for Relevance Extraction (models that leverage causal reasoning to identify the true cause-effect relationships within network data, thus enabling the system to determine which pieces of information are causally relevant to a given goal, rather than merely correlational) or Multi-Modal Contrastive Learning for Unified Semantic Representation (using contrastive self-supervised learning to fuse data from multiple modalities, e.g., sensor data, localization information, communication signals, into a cohesive semantic representation that emphasizes task-specific features.)- Develop Context-Aware and Task-Driven Networking Frameworks, by designing adaptive, scalable and goal-oriented communication models that dynamically adjust information exchange based on real-time context and application needs. - Implement AI-based decision-making frameworks to ensure that communication serves system-wide objectives rather than individual data requests. Year 1: Theoretical Foundations & Initial Models Year 2: Model Implementation & Performance Evaluation Year 3: System Integration & Testing |
| Required skills | The ideal candidate should have a strong understanding of networks and advanced communication technologies, including RAN, 5G, and 6G systems. They should be proficient in protocol design and optimization and have a grasp of methodologies for the integration of AI-driven solutions in next-gen networking. Proficiency in programming (Python, TensorFlow/PyTorch) and network simulation tools is required. Analytical skills, and experience with predictive modelling and analysis are highly valued. |
Security of Linux Kernel Extensions | |
| Proposer | Riccardo Sisto |
| Topics | Cybersecurity, Software engineering and Mobile computing, Parallel and distributed systems, Quantum computing |
| Group website | https://netgroup.polito.it |
| Summary of the proposal | eBPF (extended Berkeley Packet Filters) and XDP (eXpress Data Path) are technologies recently introduced in Linux to enable the execution of user-defined plugins in the Linux kernel with the purpose of processing network packets at highest speed. This research aims to perform a deep study of the security of these technologies, enriching the still limited literature in this field, and to propose code development techniques that avoid the related most dangerous vulnerabilities by construction. |
| Research objectives and methods | Today, there is a growing interest in eBPF and XDP in the networking field because such technologies allow ultra-high-speed monitoring of network traffic in real time. However, the security of such techniques has not yet been studied adequately. Moreover, as witnessed by several new related vulnerabilities that have been recently discovered, eBPF/XDP security is not yet satisfactory despite eBPF code is statically analyzed by a bytecode verifier before being accepted for execution by the Linux kernel. The main objective of the proposed research is to improve the state of the art of secure coding for eBPF/XDP code. This will be done by first studying the state of the art and the attack surface of the eBPF/XDP technologies. Then, new techniques will be proposed to produce code that is provably free from the most dangerous vulnerabilities by construction. In this research work, the candidate will exploit the expertise about formal methods available in the proposer's research group. The research activity will be organized in three phases: Phase 1 (1st year): The candidate will analyze and identify the main security issues and attack surfaces of eBPF/XDP code, going beyond the limited studies available today in literature on the topic. This will be done by also applying new formal modeling approaches developed by the candidate with the tutor's help to look for new classes of possible eBPF/XDP vulnerabilities in a systematic way. At this phase's end, some preliminary results are expected to be published, such as a survey of the state of the art and the findings of the systematic search for new classes of vulnerabilities. During the first year, the candidate will also acquire the background necessary for the research. This will be done by attending courses and by personal study. Phase 2 (2nd year): The candidate will develop techniques to support the programmer in developing eBPF/XDP code that is provably free from the most important classes of vulnerabilities. This will be done by leveraging the knowledge about eBPF/XDP code security acquired in the first year, and by developing a formal secure-by-construction approach for the development of eBPF code. Particular emphasis will also be given to the experimental evaluation of the developed approach. The results of this work will also be submitted for publication, aiming at least at a journal publication. Phase 3 (3rd year): based on the results achieved in the previous phase, the proposed approach will be further refined, to improve its precision and relevance, and the related dissemination activity will be completed. The work will be done in synergy with the European Project ELASTIC, which started in 2024 with the goal of developing a software architecture for extreme-scale analytics based on recent programming technologies like eBPF/XDP and Wasm and characterized by high security standards. The proposer's group participates as one of the ELASTIC partners and is involved in the study of the security of eBPF/XDP, which is strictly related to the proposed research. The contributions produced by the proposed research can be published in conferences and journals belonging to the areas of Cybersecurity (e.g. IEEE S&P, ACM CCS, NDSS, ESORICS, IFIP SEC, DSN, ACM Transactions on Information and System Security, or IEEE Transactions on Secure and Dependable Computing), and networking applications (e.g. INFOCOM, ACM/IEEE Transactions on Networking, or IEEE Transactions on Network and service Management). |
| Required skills | To successfully develop the proposed activity, the candidate should have a background in cybersecurity, software engineering and networking. Some knowledge of formal languages and formal methods can be useful, but it is not strictly required: the candidate can acquire this knowledge and related skills as part of the PhD Program, by exploiting specialized courses. |
Local energy markets in citizen-centered energy communities | |
| Proposer | Edoardo Patti, Enrico Macii, Lorenzo Bottaccioli |
| Topics | Software engineering and Mobile computing, Parallel and distributed systems, Quantum computing, Computer architectures and Computer aided design |
| Group website | www.eda.polito.it |
| Summary of the proposal | Energy communities will enable citizens to participate actively in local energy markets by exploiting new digital tools. Citizens will need to understand how to interact with smart energy systems, novel digital tools and local energy markets. Thus, new complex socio-techno-economic interactions will take place in such systems which need to be simulated to evaluate future impacts. A novel co-simulation framework is needed, which combines agent-based modelling techniques with external simulators. |
| Research objectives and methods | The diffusion of distributed (renewable) energy sources poses new challenges in the underlying energy infrastructure, e.g., distribution and transmission networks and/or within micro (private) electric grids. The optimal, efficient and safe management and dispatch of electricity flows among different actors (i.e., prosumers) is key to supporting the diffusion of the distributed energy sources paradigm. The goal of the project is to explore different corporate structures, billing and sharing mechanisms inside energy communities. For instance, the use of smart energy contracts based on Distributed Ledger Technology (blockchain) for energy management in local energy communities will be studied. A testbed comprising of physical hardware (e.g., smart meters) connected in the loop with a simulated energy community environment (e.g., a building or a cluster of buildings) exploiting different Renewable Energy Sources (RES) and energy storage technology will be developed and tested during the three-year program. Hence, the research will focus on the development of agents capable of describing:- the final customer/prosumer beliefs desires and intentions and opinions.- the local energy market where prosumers can trade their energy and or flexibility- the local system operator that has to provide the grid reliability All the software entities will be coupled with external simulators of the grid and energy sources in a plug-and-play fashion. Hence, the overall framework has to be able to work in a co-simulation environment with the possibility of performing hardware in the loop. The final outcomes of this research will be an agent-based modelling tool that can be exploited for:- Planning the evolution of future smart multi-energy systems by taking into account the operational phase- Evaluating the effect of different policies and related customer satisfaction- Evaluating the diffusion of technologies and/or energy policies under different regulatory scenarios- Evaluating new business models for energy communities and aggregators During the 1st year, the candidate will study state-of-the-art solutions of existing agent-based modelling tools in order to identify the best available solution for large-scale smart energy system simulation in distributed environments. Furthermore, the candidate will review the state of the art in prosumers/aggregators/market modelling in order to identify the challenges and identify possible innovations. Moreover, the candidate will focus on the review of possible corporate structures, billing and sharing mechanisms of energy communities. Finally, it will start the design of the overall platform starting with the requirements identification and definition. During the 2nd year, the candidate will terminate the design phase and will start the implementation of the agent intelligence. Furthermore, it will start to integrate agents and simulators together in order to create the first beta version of the tool. During 3rd year, the candidate will ultimate the overall platform and test it in different case studies and scenarios in order to show all the effects of the different corporate structures, billing and sharing mechanisms in energy communities. Possible international scientific journals and conferences:- IEEE Transaction Smart Grid,- IEEE Transactions on Evolutionary Computation,- IEEE Transactions on Control of Network Systems,- Environmental modelling and Software,- JASSS,- ACM e-Energy,- IEEE EEEIC internatational conference- IEEE SEST internatational conference- IEEE Compsac internatational conference. |
| Required skills | Programming and Object-Oriented Programming (preferable in Python). Frameworks for Multi Agent Systems Development (preferable). Development in web environment (e.g. REST web services). Computer Networks |
Simulation and Modelling of V2X connectivity with traffic simulation | |
| Proposer | Edoardo Patti, Enrico Macii, Lorenzo Bottaccioli |
| Topics | Data science, Computer vision and AI, Parallel and distributed systems, Quantum computing, Software engineering and Mobile computing |
| Group website | www.eda.polito.it |
| Summary of the proposal | The development of novel ICT solutions in smart-grids has opened new opportunities to foster novel services for energy management and savings in all end-use sectors, with particular emphasis on Electric Vehicle connectivity, such as demand flexibility. Thus, there will be a strong interaction among transportation, traffic trends and energy distribution systems. New simulation tools are needed to evaluate the impact of Electric Vehicles in the grid by considering citizens behaviors. |
| Research objectives and methods | This research aims at developing novel simulation tools for smart cities/smart grid scenarios that exploit the Agent-Based Modelling (ABM) approach to evaluate novel strategies to manage the V2X connectivity with traffic simulation. The candidate will develop an ABM simulator that will provide a realistic and virtual city where different scenarios will be executed. The ABM should be based on real data, demand profiles and traffic patterns. Furthermore, the simulation framework should be flexible and extendable so that i) It can be improved with new data from the field; ii) it can be interfaced with other simulation layers (i.e. physical grid simulators, communication simulators); iii) It can interact with external tools executing real policies (such as energy aggregation). This simulator will be a useful tool to analyse how V2X connectivity and the associated services impact both social behaviours and traffic. It will also help the understanding of the impact of new actors and companies (e.g., sharing companies) in both the marketplace and the society, again by analysing the social behaviours and the traffic conditions. In a nutshell, ABM simulator will simulate both traffic variation and the possible advantages of V2X connectivity strategies in a smart grid context. This ABM simulator will be designed and developed to span different spatial-temporal resolutions. All the software entities will be coupled with external simulators of grid and energy sources in a plug-and-play fashion to be ready for being integrated with external simulators and platforms. This will enhance the resulting AMB framework unlocking also hardware in the loop features. The outcomes of this research will be an agent-based modelling tool that can be exploited for:- Simulating V2X connectivity considering traffic conditions- Evaluating the effect of different policies and related customer satisfaction- Evaluating the diffusion and acceptance of demand flexibility strategies- Evaluating the new business model for future companies and services During the 1st year, the candidate will study the state-of-the-art solution of existing agent-based modelling tools to identify the best available solution for large-scale traffic simulation in distributed environments. Furthermore, the candidate will review the state of the art of V2X connectivity to identify the challenges and identify possible innovations. Moreover, the candidate will focus on the review of Artificial Intelligence algorithms for simulating traffic conditions and variation for estimating EV flexibility and users' preferences. Finally, he/she will start the design of the overall ABM framework and algorithms starting with the requirements identification and definition. During the 2nd year, the candidate will terminate the design phase and will start the implementation of the agents' intelligence and test the first version of the proposed solution. During the 3rd year, the candidate will ultimate the overall ABM framework and AI algorithms and test it in different case studies and scenarios to assess the impact of V2X connection strategies and novel business models. Possible international scientific journals and conferences:- IEEE Transaction Smart Grid,- IEEE Transactions on Evolutionary Computation,- IEEE Transactions on Control of Network Systems,- Environmental modelling and Software,- JASSS,- ACM e-Energy,- IEEE EEEIC international conference- IEEE SEST international conference- IEEE Compsac international conference |
| Required skills | Programming and Object-Oriented Programming (preferable in Python), Frameworks for Multi Agent Systems Development (preferable) Development in web environment (e.g. REST web services), Computer Networks |
Machine Learning techniques for real-time State-of-Health estimation of Electric Vehicles batteries | |
| Proposer | Edoardo Patti, Enrico Macii, Alessandro Aliberti |
| Topics | Data science, Computer vision and AI, Software engineering and Mobile computing, Computer architectures and Computer aided design |
| Group website | https://eda.polito.it/ |
| Summary of the proposal | This Ph.D. research proposal aims at studying novel software solutions based on Machine Learning (ML) techniques to estimate the State-of-Health (SoH) of batteries in Electric Vehicles (EV) in near-real-time. This research area is gaining a strong interest in the last years as the number of EVs is constantly rising. Knowing this SoH can unlock different possible strategies i) to reuse EVs' batteries in other contexts, e.g. stationary energy storage systems in Smart Grids, or ii) to recycle them. |
| Research objectives and methods | In the last years, the number of Electric Vehicles (EVs) increased significantly and it is expected to grow in the upcoming years. Due to the use of high-value materials, there is a strong economic, environmental and political interest in implementing solutions to recycle EV's batteries for example by reusing them in stationary applications to become useful energy storage systems in Smart Grids. To achieve it, novel tools are needed to estimate the battery State-of-Health (SoH), i.e. the battery capacity measurement, in near-real-time. Currently, SoH is determined by bench discharging tests taking several hours and making this process time-consuming and expensive. The objective of this Ph.D. proposal consists of the design and development of models based on Machine Learning (ML) techniques that will exploit both synthetic and real-world datasets. The synthetic dataset is needed to train and test a generic ML model suitable for any EV independently from a specific brand and/or model. Whilst, the real-world dataset, given by monitoring real EVs, is needed to fine-tune the ML models, for example, by applying transfer learning techniques, customizing them more and more on the specific brand and model of the real-world EV to monitor. During the three years of the Ph.D., the research activity will be divided into four phases:- Study and analysis of both state-of-the-art solutions and datasets of real-world EV monitoring.- Design and develop a realistic simulator of an EV fleet to generate the synthetic and realistic dataset. Starting from both datasheet information of different EVs (in terms of brand and model) and information provided by the Italian National Institute of Statistics (ISTAT), the simulator will simulate different routes in terms of length, altitude and travel speed, impacting battery wear differently, thus making the resulting dataset realistic and heterogeneous.- Design and development of ML-based models trained and tested with the synthetic dataset to estimate the SoH of EV's batteries.- Application of transfer learning techniques to the ML-based models (from the previous bullet #3) to fine-tune them by exploiting datasets of real-world EV monitoring (result of the previous bullet #1). Possible international scientific journals and conference:- IEEE Transaction Smart Grid,- IEEE Transaction on Vehicular Technology,- IEEE Transaction on Industrial Informatics,- IEEE Transactions on Industry Applications,- Engineering Applications of Artificial Intelligence,- Expert Systems with Applications,- ACM e-Energy- IEEE EEEIC international conference- IEEE SEST international conference- IEEE Compsac international conference. |
| Required skills | Programming and Object-Oriented Programming (preferable in Python). Knowledge of Machine Learning and Neural Networks. Knowledge of frameworks to develop models based on Machine Learning and Neural Networks. Knowledge of development of Internet of Things Applications. |
Natural Language Processing e Large Language Models for source code generation | |
| Proposer | Edoardo Patti, Enrico Macii, Lorenzo Bottaccioli |
| Topics | Data science, Computer vision and AI, Software engineering and Mobile computing |
| Group website | https://eda.polito.it/ |
| Summary of the proposal | This Ph.D. research is focused on revolutionizing source code generation by harnessing the capabilities of Natural Language Processing by exploring novel methodologies to facilitate the creation of high-quality code through enhanced human-machine collaboration. By leveraging advanced language models, like Generative Pretrained Transformer models, the research seeks to optimize the process, leading to more efficient, expressive, and context-aware source code generation in software development. |
| Research objectives and methods | The integration of Artificial Intelligence, especially Machine/Deep Learning, in industrial processes promises swift changes. Companies stand to benefit in the short term with improved production quality, efficiency, and automated routine tasks, fostering positive impacts on work environments. In addition to Natural Language Processing, Large Language Models (LLMs) have already demonstrated significant progress in healthcare, education, software development, finance, journalism, scientific research, and customer support. The future entails optimizing LLMs for widespread use, enhancing the competitiveness of the industrial system and streamlining collaborative supply chain management. The objective of this Ph.D. proposal consists of the design and development of AI-assisted models based on Natural Language Processing (NLP) and Large Language Models (LLMs) to optimize the AI-assisted source code generation in the context of software development by enhancing the process, leading to more efficient, expressive, and context-aware. During the three years of the Ph.D., the research activity will be divided into five phases:- Survey existing literature on NLP applications in software engineering and analyze methodologies and challenges in source code generation using language models.- Design and develop Large Language Models for improved programming language understanding by investigating techniques for domain-specific customization of language models.- Develop algorithms and strategies for context-aware source code generation by implementing prototype systems for evaluation and refinement.- Design and implement a collaborative framework that seamlessly integrates developer input with language model suggestions.- Evaluate the effectiveness of the collaboration framework through user studies and real-world projects. Possible international scientific journals and conference:- IEEE Transactions on Audio, Speech, and Language Processing- IEEE Transactions on Software Engineering- IEEE Transaction on Industrial Informatics,- IEEE Transactions on Industry Applications,- Engineering Applications of Artificial Intelligence,- Expert Systems with Applications,- IEEE NLP-KE internat. conf.- IEEE ICNLP internat. conf.- IEEE Compsac internat. conf. |
| Required skills | Programming and Object-Oriented Programming (preferable in Python), Knowledge of Natural Language Processing and Large Language Models Knowledge of frameworks to develop models based on Natural Language Processing and Large Language Models |
Advanced ICT solutions and AI-driven methodologies for Cultural Heritage resilience | |
| Proposer | Edoardo Patti, Enrico Macii, Alessandro Aliberti |
| Topics | Data science, Computer vision and AI, Software engineering and Mobile computing, Parallel and distributed systems, Quantum computing |
| Group website | https://eda.polito.it/ |
| Summary of the proposal | This Ph.D. research leverages on cutting-edge technologies to preserve Cultural Heritage (e.g., monuments, historical sites, etc.) against natural disasters, climate change, and human-related threats. The interdisciplinary approach integrates ICT tools, Machine Learning, and Data Analytics to develop proactive strategies for risk assessment, monitoring, and preservation of cultural assets by addressing challenges through innovative solutions for sustainable conservation and resilience |
| Research objectives and methods | Recent crises and disasters have affected the European citizens' lives, livelihoods, and environment in unforeseen and unprecedented ways. They have transformed our very understanding of them by reshaping hitherto unchallenged notions of the ?local? and the ?global? and putting into question well-rehearsed conceptual distinctions of ?natural? and ?man-made? disasters. Modern and high-performance ICT solutions need to be deployed in order to prevent and mitigate the effects of disasters and climate change events by enabling critical thinking and framing a holistic approach for better understanding of catastrophic events. The objective of this Ph.D. proposal consists of the design and development of ICT-driven solutions to develop proactive strategies for risk assessment, monitoring, and preservation of Cultural Heritage. The candidate will adopt a comprehensive interdisciplinary approach, seamlessly integrating modern techniques rooted in IoT, Machine/Deep Learning, and Big Data paradigms within the realm of cultural heritage resilience. This approach transcends purely technical facets, encompassing social and cultural dimensions to provide a holistic understanding and effective solutions. During the three years of the Ph.D., the research activity will be divided into five phases:- Survey existing literature on modern Ai-driven ICT solutions and applications in software engineering and analyze methodologies and challenges Cultural Heritage resilience.- Design and develop a data-driven digital ecosystem - i.e., distributed IoT platform - for the collection and harmonization of heterogeneous data from the real world to enable on-top advanced visualization and analysis services (e.g., Digital Twins). A multidisciplinary approach ranging from IoT paradigms to the application of Machine/Deep Learning methodologies for Big Data analysis is required in order to allow the development of proactive strategies for risk assessment, monitoring, and preservation of Cultural Heritage.- Develop algorithms and strategies for a context-aware Cultural Heritage resilience by implementing prototype systems for evaluation and refinement.- Design and implement continuous improvement and fine-tuning strategies for the development of increasingly effective and high-performing prevention strategies.- Evaluate the effectiveness of the data-driven digital ecosystem and developed strategies through user studies and real-world projects. Possible international scientific journals and conference: - IEEE Transactions on Computational Social Systems- IEEE Transactions on Industrial Informatics- Journal on Computing and Cultural Heritage- Journal of Cultural Heritage- Engineering Applications of Artificial Intelligence,- Expert Systems with Applications,- IEEE CoStProgramming and Object-Oriented Programming (preferable in Python).- Knowledge of web application programming.- Knowledge of IoT paradigms.- Knowledge of Machine Learning and Deep Learning.- Knowledgeof frameworks to develop models based on Machine Learning and Deep Learning Model- internat. Conf.- IEEE SKIMA internat. Conf. |
| Required skills | Programming and Object-Oriented Programming (preferable in Python). Knowledge of web application programming. Knowledge of IoT paradigms. Knowledge of Machine Learning and Deep Learning. Knowledge of frameworks to develop models based on Machine Learning and Deep Learning Models |
Embedded Cybersecurity Solutions for Enhanced Resilience in Smart City Environments | |
| Proposer | The advent of smart cities heralds a new era of urban development, characterized by the pervasive integration of digital technologies and Internet-of-Things (IoT) devices into the fabric of urban infrastructure. While these advancements promise enhanced efficiency, sustainability, and quality of life for urban residents, they also introduce a myriad of cybersecurity challenges that necessitate immediate attention. The interconnected nature of smart city systems renders them vulnerable to diverse cyber threats, ranging from data brehttps://eda.polito.it/aches and ransomware attacks to potential disruptions in critical services. As such, the imperative to fortify the cybersecurity resilience of smart cities has become a pressing concern in urban planning and infrastructure development. At the heart of addressing the cybersecurity vulnerabilities inherent in smart city environments lies the innovative integration of advanced cryptographic techniques, anomaly detection algorithms, and secure communication protocols into the core of embedded systems. This research endeavours to harness the power of machine learning and data analytics to develop intelligent cybersecurity solutions capable of real-time threat detection and adaptive response mechanisms. By delving into the intricate interplay between network security principles, IoT protocols, and system architecture, this research aims to craft resilient cybersecurity frameworks specifically tailored to the dynamic and interconnected nature of smart city ecosystems. The technical underpinnings of this research encompass a multidisciplinary approach that converges the realms of cybersecurity, embedded systems, machine learning, and data analytics. By amalgamating these diverse disciplines, this research seeks to construct a robust cybersecurity framework that safeguards critical infrastructure and services and fosters a culture of cyber resilience within smart city environments. The deployment of embedded cybersecurity solutions fortified with advanced cryptographic algorithms and anomaly detection mechanisms represents a paradigm shift in fortifying the digital fortresses of smart cities against the ever-evolving landscape of cyber threats. The research will commence with an exhaustive examination of prevailing cybersecurity frameworks and protocols pertinent to smart city settings. Subsequently, novel embedded cybersecurity solutions will be meticulously designed and implemented to cater to the unique requisites of smart cities. These tailored solutions will consider resource constraints, scalability, and real-time threat identification. Rigorous testing and validation in simulated smart city environments will be conducted to assess the efficacy and performance of the developed cybersecurity mechanisms. Furthermore, the research will delve into the socio-technical aspects of embedded cybersecurity in smart cities, exploring privacy, governance, and societal trust implications. Collaboration with industry partners and stakeholders will be integral to validating the practicality and viability of the proposed cybersecurity solutions in real-world deployment scenarios. The objectives of this PhD fellowship span three years, beginning with an extensive assessment of existing cybersecurity frameworks tailored to smart city environments in the first year. This initial phase involves identifying vulnerabilities and challenges specific to interconnected urban systems, which will inform the design of innovative cybersecurity solutions that incorporate advanced cryptographic techniques and anomaly detection algorithms. In the second year, the focus shifts to the implementation of these solutions in simulated smart city environments, where rigorous testing will evaluate their effectiveness in real-time threat detection and adaptive responses, allowing for iterative refinements based on performance metrics. The final year will concentrate on the optimization and scalability of the developed cybersecurity frameworks, with an emphasis on their integration into existing smart city infrastructure.
Edoardo Patti, Enrico Macii, Luca Barbierato |
| Topics | Computer architectures and Computer aided design, Cybersecurity |
| Group website | https://eda.polito.it/ |
| Summary of the proposal | This research aims at developing advanced embedded cybersecurity solutions tailored to smart city environments' unique challenges. The increasing interconnectivity of devices within smart cities exposes them to cybersecurity threats, necessitating the integration of robust security measures into the very fabric of these systems.By leveraging cutting-edge technologies, this research aims to enhance the resilience of smart cities vs cyber threats, ensuring the secure operation of critical services |
| Research objectives and methods | The advent of smart cities heralds a new era of urban development, characterized by the pervasive integration of digital technologies and Internet-of-Things (IoT) devices into the fabric of urban infrastructure. While these advancements promise enhanced efficiency, sustainability, and quality of life for urban residents, they also introduce a myriad of cybersecurity challenges that necessitate immediate attention. The interconnected nature of smart city systems renders them vulnerable to diverse cyber threats, ranging from data brehttps://eda.polito.it/aches and ransomware attacks to potential disruptions in critical services. As such, the imperative to fortify the cybersecurity resilience of smart cities has become a pressing concern in urban planning and infrastructure development. At the heart of addressing the cybersecurity vulnerabilities inherent in smart city environments lies the innovative integration of advanced cryptographic techniques, anomaly detection algorithms, and secure communication protocols into the core of embedded systems. This research endeavours to harness the power of machine learning and data analytics to develop intelligent cybersecurity solutions capable of real-time threat detection and adaptive response mechanisms. By delving into the intricate interplay between network security principles, IoT protocols, and system architecture, this research aims to craft resilient cybersecurity frameworks specifically tailored to the dynamic and interconnected nature of smart city ecosystems. The technical underpinnings of this research encompass a multidisciplinary approach that converges the realms of cybersecurity, embedded systems, machine learning, and data analytics. By amalgamating these diverse disciplines, this research seeks to construct a robust cybersecurity framework that safeguards critical infrastructure and services and fosters a culture of cyber resilience within smart city environments. The deployment of embedded cybersecurity solutions fortified with advanced cryptographic algorithms and anomaly detection mechanisms represents a paradigm shift in fortifying the digital fortresses of smart cities against the ever-evolving landscape of cyber threats. The research will commence with an exhaustive examination of prevailing cybersecurity frameworks and protocols pertinent to smart city settings. Subsequently, novel embedded cybersecurity solutions will be meticulously designed and implemented to cater to the unique requisites of smart cities. These tailored solutions will consider resource constraints, scalability, and real-time threat identification. Rigorous testing and validation in simulated smart city environments will be conducted to assess the efficacy and performance of the developed cybersecurity mechanisms. Furthermore, the research will delve into the socio-technical aspects of embedded cybersecurity in smart cities, exploring privacy, governance, and societal trust implications. Collaboration with industry partners and stakeholders will be integral to validating the practicality and viability of the proposed cybersecurity solutions in real-world deployment scenarios. The objectives of this PhD fellowship span three years, beginning with an extensive assessment of existing cybersecurity frameworks tailored to smart city environments in the first year. This initial phase involves identifying vulnerabilities and challenges specific to interconnected urban systems, which will inform the design of innovative cybersecurity solutions that incorporate advanced cryptographic techniques and anomaly detection algorithms. In the second year, the focus shifts to the implementation of these solutions in simulated smart city environments, where rigorous testing will evaluate their effectiveness in real-time threat detection and adaptive responses, allowing for iterative refinements based on performance metrics. The final year will concentrate on the optimization and scalability of the developed cybersecurity frameworks, with an emphasis on their integration into existing smart city infrastructure.
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| Required skills | Programming and Object-Oriented Programming (preferable in C/C++) Knowledge of operative system (e.g. UNIX) Knowledge of embedded system Knowledge of driver design Knowledge of cybersecurity Knowledge of IoT paradigms |
Privacy-Preserving Machine Learning over IoT networks | |
| Proposer | Valentino Peluso, Andrea Calimera, Enrico Macii |
| Topics | Computer architectures and Computer aided design, Cybersecurity, Data science, Computer vision and AI |
| Group website | www.eda.polito.it www.linkedin.com/company/edagroup-polito/ |
| Summary of the proposal | Distributed Machine Learning strategies, like split learning and federated learning, enable decentralized intelligence but are vulnerable to data theft and manipulation, raising privacy and security concerns. Existing defenses often degrade performance and introduce overhead, limiting their adoption in resource-constrained IoT devices. This project aims to develop hardware-aware software optimization techniques for efficient, privacy-preserving ML in distributed IoT systems. |
| Research objectives and methods | Research objectives. This project aims to develop and evaluate optimization techniques that address the challenges of privacy and security in distributed machine learning (ML) while ensuring efficiency in resource constrained IoT environments. Specifically, the objectives include: - Acquire competences in ML and deep learning training and deployment, distributed computing architectures, and existing privacy-preserving techniques. - Develop optimization strategies to make privacy-preserving strategies compatible with the limited resources of low-power end-nodes and off-the-shelf devices, enabling their implementation feasible in real-world networks and infrastructures. - Identify the evaluation metrics to assess the quality, security and efficiency of privacy-preserving ML frameworks. - Develop an emulation framework for rapid assessment of different optimization strategies and techniques. - Develop multi-objective optimization techniques and algorithms that enhance accuracy, energy efficiency, and communication costs while maintaining privacy protection. The proposed solutions should also be compatible with security defenses against adversarial attacks, such as data and model poisoning, which are notoriously difficult to integrate with standard privacy-preserving techniques. Outline of research work plan. 1st year. The candidate will conduct a comprehensive review of the state-of-the-art in distributed ML, focusing on: (i) existing approaches such as federated learning, split learning, split inference; (ii) vulnerabilities, threats, and attacks in distributed ML systems; (iii) privacy-preserving techniques, including differential privacy, multi-party computation, and homomorphic encryption; (iv) key performance indicators (KPIs) to evaluate distributed ML strategies and their applicability in IoT systems. The candidate will also develop an initial version of an emulation framework for distributed ML (leveraging existing open-source projects), which will serve as testbed to evaluate novel optimization strategies. 2nd year. The candidate will design, develop, and validate novel optimization strategies, working across multiple layers: (i) at the software layer, with algorithmic solutions that concurrently optimize accuracy efficiency; (ii) at the hardware layer, investigating compiler-level optimization and specialized architectures for acceleration. Rather than treating these optimization strategies as isolated solutions, the candidate will explore their interactions to maximize efficiency. 3rd year. The candidate will test and consolidate the developed methodologies on real applications. The focus will be on emerging applications that could benefit most from privacy-preserving ML, assessing feasibility, robustness, and efficiency in practical scenarios. Possible venues for publications: - IEEE Internet of Things Journal - IEEE Transactions on Parallel and Distributed Systems - IEEE Transactions on Privacy - IEEE Transactions on Information Forensics and Security - IEEE Transactions on Dependable and Secure Computing - ACM Transactions on Embedded Computing Systems - ACM Transactions on Internet of Things - ACM Transactions on Privacy and Security - ACM/IEEE Design Automation Conference (DAC) |
| Required skills | Knowledge of standard Machine Learning and Deep Learning and basic model compression strategies (e.g., pruning, quantization). Background in embedded systems programming. Proficiency in Python, including ML frameworks like scikit-learn and PyTorch. Strong communication and writing skills. |
Data-Driven and Sustainable Solutions for Distributed Systems | |
| Proposer | Guido Marchetto, Alessio Sacco |
| Topics | Parallel and distributed systems, Quantum computing, Data science, Computer vision and AI |
| Group website | http://www.netgroup.polito.it |
| Summary of the proposal | Recent advances in cyber-physical systems are expected to support advanced and critical services incorporating computation, communication, and intelligent decision making. The research activity aims to leverage advanced analytics, machine learning, and optimization techniques, to enhance the efficiency, resilience, and sustainability of distributed systems. Key focus areas include reducing energy consumption while using distributed learning techniques and optimizing resource allocation. |
| Research objectives and methods | Two research questions (RQ) guide the proposed work: RQ1: How can we design and implement on local and larger-scale testbeds effective autonomous solutions that integrate the network information at different scopes using recent advances in supervised and reinforcement learning? RQ2: To scale the use of machine learning-based solutions in cyber-physical systems, what are the most efficient distributed machine learning architectures that can be implemented at the edge of such systems? The final target of the research work is to answer these questions, also by evaluating the proposed solutions on small-scale emulators or large-scale virtual testbeds, using a few applications, including virtual and augmented reality, precision agriculture, or haptic wearables. In essence, the main goals are to provide innovation in decision, planning, responsiveness, using centralized and distributed learning integrated with edge computing infrastructures. Both vertical and horizontal integration will be considered. By vertical integration, we mean considering learning problems that integrate states across hardware and software, as well as states across the network stack across different scopes. For example, the candidate will design data-driven algorithms for planning the deployment of IoT sensors, tasks scheduling, and resources organization. By horizontal learning, we mean using states from local (e.g., physical layer) and wide area (e.g., transport layer) as input for the learning-based algorithms. The data needed by these algorithms are carried to the learning actor by means of newly networking protocols. Aside from supporting resiliency with the vertical integration, solutions must offer resiliency across a wide (horizontal) range of network operations: from close-edge, i.e., near the device, to the far-edge, with the design of secure data-centric resource allocation (federated) algorithms. The research activity will be organized in three phases: Phase 1 (1st year): the candidate will analyze the state-of-the-art solutions for cyber-physical systems management, with particular emphasis on knowledge-based network automation techniques. The candidate will then define detailed guidelines for the development of architectures and protocols that are suitable for automatic operation and (re-)configuration of such deployments, with particular reference to edge infrastructures. Specific use-cases will also be defined during this phase (e.g., in virtual reality, smart agriculture). Such use cases will help identifying ad-hoc requirements and will include peculiarities of specific environments. With these use cases in mind, the candidate will also design and implement novel solutions to deal with the partial availability of data within distributed edge infrastructures. Results of this work will likely result in conference publications. Phase 2 (2nd year): the candidate will consolidate the approaches proposed in the previous year, focusing on the design and implementation of mechanisms for vertical and horizontal integration of supervised and reinforcement learning. Network, and computational resources will be considered for the definition of proper allocation algorithms, with the objective of energy efficiency. All solutions will be implemented and tested. Results will be published, targeting at least one journal publication. Phase 3 (3rd year): the consolidation and the experimentation of the proposed approach will be completed. Particular emphasis will be given to the identified use cases, properly tuning the developed solutions to real scenarios. Major importance will be given to the quality offered to the service, with specific emphasis on the minimization of latencies in order to enable a real-time network automation for critical environments (e.g., telehealth systems, precision agriculture, or haptic wearables). Further conference and journal publications are expected. The research activity is in collaboration with Saint Louis University, MO, USA and University of Kentucky, KY, USA, also in the context of some NSF grants. The contributions produced by the proposed research can be published in conferences and journals belonging to the areas of networking and machine learning (e.g. IEEE INFOCOM, ICML, ACM/IEEE Transactions on Networking, or IEEE Transactions on Network and Service Management) and cloud/fog computing (e.g. IEEE/ACM SEC, IEEE ICFEC, IEEE Transactions on Cloud Computing), as well as in publications related to the specific areas that could benefit from the proposed solutions (e.g., IEEE PerCom, ACM MobiCom, IEEE Transactions on Industrial Informatics, IEEE Transactions on Vehicular Technology). |
| Required skills | The ideal candidate has good knowledge and experience in networking and machine learning, or at least in one of the two topics. Availability for spending periods abroad (mainly but not only at Saint Louis University and/or University of Kentucky) is also important for a profitable development of the research topic. |
Single-cell Multi-omics for Understanding Cellular Heterogeneity | |
| Proposer | Stefano Di Carlo, Savino Alessandro, Bardini Roberta |
| Topics | Life sciences, Data science, Computer vision and AI |
| Group website | |
| Summary of the proposal | Single-cell multi-omics analysis integrates data from multiple molecular layers (e.g., transcriptomics, epigenomicsm, proteomics) within individual cells to provide a deeper understanding of cellular heterogeneity. This project will develop computational methods for integrating and analyzing single-cell sequencing data, supporting disease modeling and therapy optimization. The proposed algorithms will be applied to open datasets to uncover novel insights into cell identity and lineage evolution. |
| Research objectives and methods | Single-cell technologies have transformed biology by enabling the analysis of individual cells within heterogeneous populations. These methods allow researchers to study cellular diversity, track cell lineage, and identify molecular signatures underlying disease progression. However, most computational tools have been developed for analyzing individual omic layers, failing to leverage the full potential of multi-omics integration. This project aims to develop novel computational frameworks for integrating single-cell multi-omics data, improving our ability to interpret complex biological systems. The candidate is expected to publish in high-impact journals (e.g., BMC Bioinformatics, IEEE/ACM Transactions on Bioinformatics) and present findings at leading conferences (IEEE BIBM, BIOSTEC). |
| Required skills | Strong programming skills (Python, R) Nice-to-have skills: |
Cybersecurity of RISC-V-based Cyber-Physical Systems in Embedded Scenarios | |
| Proposer | Stefano Di Carlo |
| Topics | Computer architectures and Computer aided design, Cybersecurity |
| Group website | |
| Summary of the proposal | This Ph.D. project aims to enhance the security of RISC-V-based Cyber-Physical Systems (CPS) and embedded computing by addressing vulnerabilities in hardware, memory protection, debugging, and runtime manipulation detection. The research will develop novel CPU-centric architectures and integrate security monitoring and countermeasures to protect against attacks in embedded and high-performance computing environments, ensuring trust throughout the hardware supply chain. |
| Research objectives and methods | Background and Motivation Embedded computing systems (ECS) are the backbone of critical applications, including IoT, transportation, autonomous vehicles, and industrial automation. However, as these systems become increasingly interconnected, they are exposed to significant security risks. Traditional cybersecurity measures focus on software-level protections, but with the emergence of hardware-level attacks, it is crucial to design secure architectures from the ground up. Among the key vulnerabilities in embedded systems are:- Embedded hardware vulnerabilities (e.g., side-channel attacks, fault injection)- Memory access protection issues (via Memory Management Units (MMU) and Memory Protection Units (MPU))- Secure debugging mechanisms (Hardware Security Modules and Host systems)- Runtime attack detection- Secure feature activation mechanisms Given that hardware serves as the root of trust, any compromise at this level endangers the entire system. This project will investigate and develop security mechanisms to protect RISC-V-based embedded architectures, ensuring trust across the supply chain and during system operation. The main objective of this Ph.D. project is to design and implement security-enhanced RISC-V-based architectures by addressing hardware and runtime security challenges. The research will focus on: This project will be structured into three key phases: The proposed research will contribute to:- More secure RISC-V architectures, enhancing their adoption in CPS, IoT, and automotive industries.- Improved protection against hardware and runtime attacks.- Better integration of security features across the hardware supply chain.- The development of open-source security frameworks for RISC-V systems.- By addressing security challenges across different computing layers, this research aims to create a new generation of trustable RISC-V-based embedded systems. |
| Required skills | Mandatory Skills: Preferred Skills (Nice-to-have) |
Challenges and Advancements in Spiking Neural Networks for Neuromorphic Computing | |
| Proposer | Stefano Di Carlo, Alessandro Savino |
| Topics | Computer architectures and Computer aided design, Data science, Computer vision and AI |
| Group website | |
| Summary of the proposal | Spiking Neural Networks (SNNs) are a promising alternative to traditional deep learning, offering energy-efficient computation inspired by biological neurons. However, challenges such as training complexity, hardware efficiency, and scalability limit their adoption. This Ph.D. will develop novel training algorithms, efficient SNN architectures, and hardware acceleration techniques to improve SNN performance in edge AI, robotics, and neuromorphic computing applications. |
| Research objectives and methods | Background and Motivation Spiking Neural Networks (SNNs) represent a biologically inspired paradigm for computing, where neurons communicate using discrete spikes instead of continuous values, mimicking real brain activity. These networks hold great potential for:- Low-power neuromorphic computing- Event-driven processing in edge AI applications- Energy-efficient robotics and real-time decision-making However, despite their theoretical advantages, SNNs face major challenges, limiting their widespread adoption. These challenges include:- Training difficulties: Traditional deep learning techniques do not directly apply to SNNs due to non-differentiable spike-based activation functions.- Hardware inefficiencies: Current neuromorphic chips (e.g., Loihi, SpiNNaker) struggle with memory constraints and real-time processing scalability.- Scalability issues: Large-scale SNNs require optimized architectures to handle thousands to millions of spiking neurons efficiently. The primary objective of this Ph.D. research is to address the fundamental challenges in Spiking Neural Networks by proposing innovative training algorithms, scalable architectures, and efficient neuromorphic hardware solutions. 2nd Year: Algorithm Development & Model Optimization- Develop hybrid training methods that integrate biological learning principles with gradient-based techniques.- Optimize SNN architectures for real-time processing.- Apply the proposed methods to benchmark datasets (MNIST, DVS Gesture Recognition, Speech Processing). 3rd Year: Hardware Acceleration & Validation- Implement and test optimized SNN models on FPGA/ASIC platforms.- Evaluate SNN performance in neuromorphic processors (Loihi, SpiNNaker).- Publish findings in leading AI and neuromorphic computing journals and conferences.Expected Impact |
| Required skills | Mandatory Skills Preferred Skills (Nice-to-have) |
Artificial Intelligence for Intelligent Biofabrication in Regenerative Medicine | |
| Proposer | Stefano Di Carlo, SAVINO Alessandro, BARDINI Roberta |
| Topics | Data science, Computer vision and AI, Life sciences |
| Group website | |
| Summary of the proposal | Advancements in biofabrication enable the precise engineering of tissues and organs for regenerative medicine. However, achieving real-time control, optimization, and scalability remains a major challenge. This Ph.D. will develop AI-driven biofabrication techniques, integrating machine learning, computer vision, and process optimization to enhance bioprinting accuracy, cell viability, and functional tissue development for next-generation biomedical applications. |
| Research objectives and methods | Background and Motivation Biofabrication is revolutionizing tissue engineering and regenerative medicine by enabling the controlled assembly of cells, biomaterials, and growth factors to create functional biological structures. Bioprinting technologies, such as extrusion-based, inkjet, and laser-assisted bioprinting, allow precise deposition of cells to mimic native tissue architectures. However, current biofabrication methods face key challenges, including:- Variability in printing resolution and cell distribution- Real-time monitoring and adaptation to ensure tissue viability- Scalability and reproducibility for clinical applications- Automated quality control in tissue fabrication- The main objective of this Ph.D. research is to develop AI-powered biofabrication frameworks that improve precision, efficiency, and scalability in 3D bioprinting and intelligent tissue engineering. How can AI improve real-time control in biofabrication?Develop AI-driven feedback loops for adaptive bioprinting parameter optimization.Use deep learning models to adjust extrusion rates, layer deposition, and environmental conditions dynamically. This research will be structured into three main phases: 1st Year: AI Model Development & Data Collection- Develop a bioprinting simulation environment for AI training.- Collect high-resolution imaging data from biofabrication experiments.- Train CNN and transformer models to analyze cell growth, scaffold integrity, and printing precision. |
| Required skills | Mandatory Skills Preferred Skills (Nice-to-have) |
Safety and Security of AI in Space and Safety Critical Applications | |
| Proposer | Stefano Di Carlo, SAVINO Alessandro |
| Topics | Computer architectures and Computer aided design, Cybersecurity, Data science, Computer vision and AI |
| Group website | |
| Summary of the proposal | The increasing deployment of Artificial Intelligence (AI) in space and other safety-critical applications introduces unique challenges in security, reliability, and fault tolerance. This Ph.D. will focus on developing robust AI models that can withstand extreme environmental conditions, adversarial attacks, and system failures. The research will integrate machine learning, cybersecurity, and hardware-based security to enhance the safety and resilience of AI systems in critical applications. |
| Research objectives and methods | Background and Motivation AI-driven systems are revolutionizing space exploration, aviation, autonomous vehicles, and other safety-critical domains by enabling real-time decision-making, anomaly detection, and autonomous operations. However, the adoption of AI in these fields introduces significant challenges, including:- Reliability under extreme conditions: Space environments expose AI systems to radiation, temperature fluctuations, and hardware degradation, leading to system failures and unpredictable behaviors.- Security threats and adversarial attacks: AI models deployed in critical infrastructure and space missions are vulnerable to cyber threats, adversarial perturbations, and data manipulation.- Fault tolerance and self-repair: AI systems must be capable of detecting failures, adapting to changing conditions, and recovering from faults autonomously.- Secure and efficient communication: AI-based systems in space require resilient communication protocols to ensure secure and reliable data transmission. This Ph.D. will address key challenges in AI safety and security by developing robust, fault-tolerant, and cyber-secure AI architectures for space-based and mission-critical applications.Key Research Questions: How can AI models be designed for robustness against environmental and cyber threats?Develop AI models with radiation-tolerant architectures.Implement resilient deep learning techniques to mitigate adversarial attacks.Enhance error correction and self-repair mechanisms in AI inference. The research will be structured in three main phases:Phase 1: AI Security and Fault Tolerance Analysis (Year 1)- Conduct a comprehensive review of AI safety and security vulnerabilities in space and safety-critical applications.- Develop an AI security assessment framework for space-based and autonomous AI systems.- Analyze real-world case studies of AI failures in mission-critical environments.Phase 2: Development of Secure and Robust AI Architectures (Year 2)- Design error-detection and mitigation techniques to improve AI fault tolerance.- Implement adversarial-resistant AI models with enhanced cybersecurity features.- Develop hardware-accelerated AI solutions for deployment in radiation-prone and resource-constrained environments.Phase 3: Validation, Testing, and Deployment (Year 3)- Validate AI security frameworks using simulation-based attacks and fault injection techniques.- Deploy and test AI security solutions in aerospace testbeds, real-time autonomous systems, and cybersecurity platforms.- Publish research findings in top-tier AI, cybersecurity, and aerospace journals and conferences.Expected Impact This research will contribute to:- Improving AI safety and reliability in space exploration, aviation, and critical infrastructure.- Enhancing cybersecurity for AI-based autonomous systems in mission-critical environments.- Developing fault-tolerant AI architectures that ensure continuous and safe operation under extreme conditions.- Bridging AI, cybersecurity, and aerospace engineering to create a trustworthy AI framework for safety-critical applications.Active Collaborations- Thales Alenia Space- AVIO GE- Space-IT-up project |
| Required skills | Mandatory Skills - Strong background in machine learning, deep learning, and AI security. - Experience with cybersecurity principles, adversarial AI, and anomaly detection. - Proficiency in Python, C/C++, and AI frameworks. - Familiarity with embedded AI, fault-tolerant systems, and real-time computing. Preferred Skills (Nice-to-have) Experience with secure AI architectures and trusted execution environments. Knowledge of radiation-resistant hardware and space computing. |
Non-invasive and low-cost solutions for health monitoring | |
| Proposer | Massimo Violante, Gabriella Olmo |
| Topics | Life sciences, Data science, Computer vision and AI |
| Group website | www.cad.polito.it |
| Summary of the proposal | The PhD program focuses on the development of low-cost solutions for health monitoring. Different sensors will be analyzed (wearable such as smart rings, contact-based such as ballistocardiograph sensors, contact-less such as radars), and algorithms will be developed to detect pathologies such as Sleep Apnea (SA) and Heart arrhythmia. The main target application is digital health with particular emphasis of constant monitoring of elderly persons at home or in elderly houses. |
| Research objectives and methods | Sleep Apnea is a potentially serious sleep disorder in which breathing repeatedly stops and starts, whose most evident side effects are loud snoring, and tiredness after a full night's sleep. The research program will be performed in collaboration with Sleep Advice Technologies Srl, Ospedale Regina Margherita, and Istituto di Ricovero e Cura a Carattere Scientifico (I.R.C.C.S.), NEUROMED ? Istituto Neurologico Mediterraneo. |
| Required skills | MATLAB or Python or C/C++ programming |
Developing methods and techniques for estimating the value of open-source software in public sector | |
| Proposer | Antonio Vetro' |
| Topics | Software engineering and Mobile computing |
| Group website | https://nexa.polito.it/ https://nexa.polito.it/pilot-study-on-estimating-the-value-of-open-source-software/ |
| Summary of the proposal | The project aims to develop an innovative methodology for estimating the economic value of the Open-Source software in the public-owned company PagoPA, considering both its internal development and its dependencies on already existing Open-Source libraries. The novel software analysis methodology should mine information from code repositories and integrate it with company-specific data and external sources. In addition, the PhD candidate shall build a software pipeline to automate the valuation. |
| Research objectives and methods | Background Open Source Software (OSS) is a cornerstone of technological innovation and global economic development. The European Commission estimates that OSS investments generate an economic impact between ?65 and ?95 billion annually, while research from the Harvard Business School values the global OSS supply at $4.15 billion, with demand reaching $8.8 trillion. List of possible venues for publications The PhD project is in collaboration with PagoPA S.p.A. |
| Required skills | The candidate should have: The candidate should also possess good communication and presentation skills. |
Innovative technologies for infrastructures and buildings management | |
| Proposer | Valentina Gatteschi, Valentina Villa, Marco Domaneschi |
| Topics | Parallel and distributed systems, Quantum computing, Data science, Computer vision and AI, Cybersecurity |
| Group website | http://grains.polito.it/ https://siscon.polito.it |
| Summary of the proposal | Infrastructures and buildings management has become very complex in terms of regulations, documentation, and technology, and it is increasingly difficult to govern assets using traditional methods. The objective of this proposal is to investigate how cutting-edge technologies such as IoT, AI, blockchain, and smart contracts could be used to improve the efficiency of asset management, and to support activities like damage detection, predictive maintenance, and process certification/automation. |
| Research objectives and methods | This research aims to revolutionize the construction and infrastructure management industry by combining cutting-edge technologies like IoT, AI, blockchain, and smart contracts to improve active monitoring, damage detection, predictive maintenance, and process certification/automation. This Ph.D. proposal will be in collaboration with DISEG Department (Department of Structural, Geotechnical and Building Engineering) of Politecnico di Torino. The activities carried out in this Ph.D. proposal will aim at investigating existing approaches, devising, and testing novel ones for: a) automatizing assessment, maintenance, and efficiency procedures of infrastructures; b) enhancing security, transparency, and privacy in the context of public infrastructures; c) improving the resilience of infrastructural assets. The research work plan for the three-year Ph.D. programme is the following: - First year: the candidate will perform an analysis of the state-of-the-art available methodologies/tools for the storage and certification of large amounts of data with distributed technologies. Part of the candidate's research activities will be devoted to analyzing how oracles could be designed and used to integrate, in the blockchain, data acquired from the real world, as well as to inspecting existing distributed solutions to efficiently store sensors' data. The candidate will also analyze the type of data required and the algorithms that are available for predictive maintenance. - Second year: during the year, the candidate will design and develop methodologies and tools for active monitoring, damage detection, predictive maintenance and processes certification/automation, starting from use cases proposed by companies working in the sector of private/public infrastructures. - Third year: the third year will be devoted to refining the tools developed during the second year (eventually by exploiting other blockchain frameworks), and to testing them, with a focus on privacy, transparency and automation, as well as on metrics such as latency/throughput, and service costs. Expected target publications are: - IEEE Transactions on Services Computing - IEEE Transactions on Knowledge and Data Engineering - IEEE Access - Intelligent Systems with Applications - Future Generation Computer Systems - IEEE International Conference on Decentralized Applications and Infrastructures - IEEE International Conference on Blockchain and Cryptocurrency - IEEE International Conference on Blockchain - Automation in Construction - Structure and Infrastructure Engineering - Buildings - European Conference on Computing in Construction - International Association for Bridge Maintenance And Safety |
| Required skills | The candidate should have the following characteristics: - Ability to research and understand theoretical and applied research topics - Good programming skills in commonly used programming languages (e.g., Python, Java, C, Node.js, PHP) and in blockchain-related programming languages (e.g., Solidity) - Good knowledge of existing blockchain frameworks - Ability to autonomously develop decentralized applications Knowledge of cryptography, and involvement in previous research projects are a plus. |
Secure Artificial Intelligence: Enhancing IT Infrastructure and Online Services | |
| Proposer | Luca Cagliero, Francesco Tarasconi |
| Topics | Data science, Computer vision and AI |
| Group website | https://www.polito.it/personale?p=luca.cagliero https://smartdata.polito.it |
| Summary of the proposal | This scholarship explores the role of Artificial Intelligence in optimizing IT infrastructure and online services while addressing security, privacy, and adaptability challenges. Key objectives include AI-driven predictive maintenance, anomaly detection, providing real-rime support, as well as fine-tuning domain-specific AI models, evaluating open-source vs. closed-source architectures, and developing secure, scalable AI frameworks for diverse industries. |
| Research objectives and methods | Context Research objectivesAI-Driven IT Infrastructure Optimization, including predictive maintenance, anomaly detection, automatic resource balancing.Development of Agentic AI or innovative approaches to integrate Generative AI in the diverse landscape of online services and tools. Exploration of new domain-specific Transformer models for industry-specific applications.Fine-tuning of pretrained large generative models on specific domains to improve accuracy and relevance for business use cases.Evaluation of the benefits and limitations of open-source vs. closed-source AI models and architectures across different industries.Developing frameworks to protect AI models from adversarial attacks and data poisoning and integrate them into the IT Infrastructure Optimization strategies.Development of scalable and modular AI frameworks to meet the needs of companies of different sizes. Tentative work plan List of possible publication venues |
| Required skills | The PhD candidate is expected to - Have the ability to critically analyze complex systems, model them and identify weaknesses; - be proficient in Python programming; - know data science fundamentals; - have a solid background on machine learning and deep learning; - have natural inclination for teamwork; - be proficient in English speaking, reading, and writing; - proficiency with Docker and Kubernetes software is a plus. |
Video Retrieval-Augmented Generation | |
| Proposer | Luca Cagliero, Elena Baralis |
| Topics | Data science, Computer vision and AI |
| Group website | https://dbdmg.polito.it/ https://smartdata.polito.it/ |
| Summary of the proposal | Retrieval-Augmented Generation is an established cost-effective approach to extend the capabilities of LLMs to specific domains and to leverage proprietary data without the need to retrain LLMs. To improve the performance of Video LLMs, existing RAG frameworks incorporate visually aligned auxiliary texts (e.g., OCR, ASR). The PhD scholarship aims to study and advance the state-of-the-art solutions in the area of Video RAGs and their applications to real-world multimedia learning scenarios. |
| Research objectives and methods | Objectives Tentative work plan List of possible publication venues |
| Required skills | The PhD candidate is expected to - Have the ability to critically analyze complex systems, model them and identify weaknesses; - be proficient in Python programming; - know data science fundamentals; - have a solid background on machine learning and deep learning; - have natural inclination for teamwork; - be proficient in English speaking, reading, and writing; |
Human-Centered AI within Internet-of-Things Ecosystems | |
| Proposer | Luigi De Russis, Alberto Monge Roffarello |
| Topics | Computer graphics and Multimedia, Data science, Computer vision and AI, Software engineering and Mobile computing |
| Group website | https://elite.polito.it |
| Summary of the proposal | Human-Centered AI (HCAI) is an emerging discipline intent on creating AI systems that amplify and augment rather than displace human abilities. This Ph.D. proposal aims at designing, developing, and evaluating concrete HCAI systems to support inhabitants of IoT-enabled environments in various tasks related to their daily life. |
| Research objectives and methods | Artificial Intelligence (AI) systems are widespread in many aspects of the society, and Generative AI lowered some barriers to access information. While this leads to many advantages in decision processes and productivity, it also presents drawbacks such as disregarding end-user perspectives and safeness. The Ph.D. proposal aims at designing, developing, and evaluating concrete HCAI systems to support users of IoT-enabled environments in various tasks related to their settings. The main research objective is to investigate solutions for designing and developing HCAI systems in IoT-enabled environments. A particular focus will be on how the adoption of the HCAI framework can bring tangible benefits to users and to the IoT research field. The research activities will mainly build on the following characteristics of the HCAI framework: |
| Required skills | The ideal candidate should have a solid background in Computer Engineering or Data Science, with prior experience with AI, especially around machine learning and/or deep learning. The candidate should also have a knowledge of Human-Computer Interaction methods and techniques. |
Preference models for multimodal annotations | |
| Proposer | Luca Cagliero, Elena Baralis |
| Topics | Data science, Computer vision and AI |
| Group website | https://dbdmg.polito.it/ https://smartdata.polito.it |
| Summary of the proposal | Data sources are commonly enriched with multimodal annotations, e.g., a video can be annotated with visual tags, textual summaries, audio excerpts, and OCR text. The choice of the modality and style of the data annotations is often arbitrary and independent of the downstream models and tasks. The research aims to define automatic preference models for Multimodal LLMs for annotations that automatically recommend the right modality, format, and type according to the task, context, and model. |
| Research objectives and methods | Objectives Tentative work plan List of possible publication venues |
| Required skills | The PhD candidate is expected to - Have the ability to critically analyze complex systems, model them and identify weaknesses; - be proficient in Python programming; - know data science fundamentals; - have a solid background on machine learning and deep learning; - have natural inclination for teamwork; - be proficient in English speaking, reading, and writing; - proficiency with Docker and Kubernetes software is a plus. |
Spatio-Temporal Data Science | |
| Proposer | Paolo Garza, Daniele Apiletti |
| Topics | Data science, Computer vision and AI |
| Group website | https://dbdmg.polito.it/ |
| Summary of the proposal | Spatio-Temporal (ST) data continuously increase (time series collected from IoT sensors, satellite images, and textual geo-referenced documents). Although ST data have been extensively studied, the current data science pipelines do not manage heterogeneous sources effectively. Most of them focus on one source at a time. Innovative deep learning approaches based on latent spaces, designed to integrate information conveyed by heterogeneous sources, are the primary goal of this proposal. |
| Research objectives and methods | The main objective of this research proposal is to study and design data-driven pipelines and deep learning models to analyze heterogeneous spatio-temporal data (e.g., time series, satellite images, and geo-referenced documents). Both descriptive and predictive problems will be considered. The main issues that will be addressed are as follows. Heterogeneity. Several sources, characterized by different data types or formats, are available. Each data source represents the phenomena under analysis from different retrospectives and provides helpful insights only if adequately integrated with the other sources. Innovative data integration techniques based, for instance, on latent spaces will be studied to address this issue. Properly integrating heterogeneous data sources permits analyzing all facets of the phenomena of interest without losing information. Scalability. Spatio-Temporal data are frequently big (e.g., vast collections of remote sensing data, extensive collections of social network messages). Hence, big data pipelines are commonly used to process and analyze them, mainly when historical data are analyzed. Timeliness. Timeliness is crucial in several domains (e.g., emergency management, fraud detection, online news). Real-time and incremental machine learning algorithms must be designed and implemented.
1st year. Analysis of the state-of-the-art algorithms and data science pipelines for Spatio-Temporal data. Based on the pros and cons of the current solutions, a preliminary common data representation based on latent spaces will be studied and designed to integrate heterogeneous data effectively. Based on the proposed data representation, novel algorithms will be designed, developed, and validated on historical data related to specific domains (e.g., emergency management news summarization). 2nd year. Common representations of heterogeneous Spatio-Temporal data will be further analyzed and proposed, focusing on scalable and resource-awareness algorithms. Specifically, solutions based on big data frameworks will be considered. 3rd year. The timeliness facet will be considered during the last year. Specifically, the focus will be on real-time Spatio-Temporal data analysis based on near real-time ML-based algorithms. The outcomes of the research activity are expected to be published at IEEE/ACM International Conferences and in any of the following journals: - ACM Transactions on Knowledge Discovery in Data |
| Required skills | Strong background in data science fundamentals and machine learning algorithms, including embeddings-based data models and LLMs. Strong programming skills. Knowledge of big data frameworks such as Spark is advisable but not required. |
Advanced data modeling and innovative data analytics solutions for complex application domains | |
| Proposer | Silvia Anna Chiusano, Tania Cerquitelli |
| Topics | Data science, Computer vision and AI |
| Group website | |
| Summary of the proposal | Data science projects entail the acquisition, modelling, integration, and analysis of big and heterogeneous data collections generated by a diversity of sources, to profile the different facets and issues of the considered application context. However, data analytics in many application domains is still a daunting task, because data collections are generally too big and heterogeneous to be processed through machine learning techniques currently available. |
| Research objectives and methods | The PhD student will work on the study, design and development of proper data models and novel solutions for the integration, storage, management and analysis of big volumes of heterogeneous data collections in complex application domains. The research activity involves multidisciplinary knowledge and skills including database, machine learning and artificial intelligence algorithms, and advanced programming. Different application contexts will be considered to highlight a wide range of data modeling and analysis problems, and thus lead to the study of innovative solutions. The objectives of the research activity consist in identifying the peculiar characteristics and challenges of each considered application domain and devise novel solutions for the modelling, management and analysis of data for each domain. Example scenarios are urban context and in particular urban mobility, and the medical domain. More in detail, the following challenges will be addressed during the PhD: 1. Modeling Heterogeneous Data: Design innovative approaches for modeling heterogeneous data, including structured and unstructured data from different sources, integrating them into a single coherent framework. The experience gained on data modeling in different application contexts can lead to the realization of a Computer-Aided Software Engineering (CASE) tool that guides the user through the design process, reducing design time and improving the quality of the modeling result. 2. Innovative algorithms for data analytics. Study, design, and implementation of innovative machine learning algorithms, with a primary emphasis on clustering and classification tasks. The objective is to overcome limitations of current approaches, enhancing their accuracy, scalability, and ability to deal with heterogeneous data collections. 3. Scalable Learning: Investigate scalable learning techniques to address the increasing complexity and volume of data for achieving optimal performance in big data environments. This research is indeed driven by the growing demand to develop machine learning systems capable of dynamically adapting to the increasing complexity of data and models. For recent machine learning/AI applications, it is crucial to propose innovative models capable of handling large volumes of data with parallel and scalable solutions. The research activity will be organized as follows. 1st Year. The PhD student will start considering a first reference application domain (for example the urban scenario) and a first reference use case in this scenario (for example urban mobility). The PhD student will review the recent literature in the selected use case to (i) identify the most relevant open research issues, (ii) identify the most relevant data analysis perspectives for gaining useful insights, and (iii) assess of main data analysis issues. The PhD student will perform an exploratory evaluation of state-of-the-art technologies and methods on the considered domain, and she/he will present a preliminary proposal for the optimization techniques of these approaches. 2nd and 3rd Year. Based on the results of the 1st year activity, the PhD student will design and develop a suitable framework including innovative data analytics solutions to efficiently model data in the considered use case and extract useful knowledge, aimed at overcoming weaknesses of state-of-the-art methods. Moreover, during the 2nd and 3rd year, the student will progressively consider a larger spectrum of application domains. The student will evaluate if and how his/her proposed solutions can be applied to the new considered domains as well as he/she will propose novel analytics solutions. During the PhD, the student will have the opportunity to cooperate in the development of solutions applied to the research project on smart cities (e.g., PRIN project on the development of an atlas for historic buildings in an urban context). The student will also complete his/her background by attending relevant courses. The student will participate to conferences presenting the results of his/her research activity. Possible pubblication venues includes international journals such as IEEE Transactions on Intelligent Transportation Systems, Information Systems Frontiers (Springer), Information sciences (Elsevier), and international conferences such as IEEE Big data, ACM Inter. Conf. on Information & Knowledge Management (CIKM), IEEE International Conference on Data Mining (ICDM) |
| Required skills | The candidate should have good programming skills, and competencies in data modelling and techniques for data analysis. |
AI4CTI - ARTIFICIAL INTELLIGENCE FOR CYBER THREAT INTELLIGENCE | |
| Proposer | Marco Mellia, Paolo Garza |
| Topics | Cybersecurity, Data science, Computer vision and AI |
| Group website | |
| Summary of the proposal | As digital reliance grows, cyber fraud is surging, with costs projected to hit $13.8T by 2028. Social engineering attacks exploit multimedia and fake news, bypassing outdated security tools. AI is key to countering these threats, using advanced algorithms for scalable, adaptive threat detection. The candidate will develop AI-driven cybersecurity solutions, leveraging multimodal analysis to detect malicious content, despite limited ground truth data, enabling on-device protection and integration. |
| Research objectives and methods | Scenario and motivations: Nowadays we rely on digital services to stay informed, organize our work, manage our savings, etc. Numbers in hand, 63,1% of the global population accesses the web daily for work, social media, and any service. With this, cyber fraud and attacks are proliferating. With the explosion of social networks and instant messaging, attack vectors multiply, making social engineering attacks based on counterfeit multimedia and fake news an everyday threat. Research objectives: The candidate will develop AI-based solutions to counterfight fight cyberthreats, focusing on the automatic detection of phishing attacks on multiple vectors, including email, websites, and messaging applications. The project will be based on three key pillars:- data collection and aggregation: Crawl the web and the dark web, in a scalable and cost-effective way, and discover and explore online groups in messaging applications such as Telegram or WhatsApp and Online Social Media Networks like Instagram or TikTok.- data storage and indexing: develop an innovative graph-based data structure that allows to simplify the query process to support the integration with AI-based algorithms that typically need to process data during training. Given state-of-the-art graph-based platforms are still in their infancy, the candidate will contribute to new solutions specifically tailored to the web security scenario.- AI algorithms: The candidate will focus on the development of a foundation model specifically engineered for cybersecurity. This will be a cornerstone that will streamline and open applications to several use cases. Differently from Large Language Models or Computer Vision models that address a single specific domain, the model will be multimodal in nature given the mix of text, images, videos, languages, etc. that are found on the web. Research work plan: We foresee three phases:- During the first year, the candidate will review the state of the art, and focus on the data collection, storage and indexing platforms- During the second year, the candidate will focus on the development of AI solutions, leveraging the data collected and aggregating CTI outlets to obtain labelled data to train algorithms. These algorithms will work initially on separate domains, like text and images separately.- During the third year, the candidate will deep dive into AI approaches, fine-tuning the models to vertical applications like phishing detection and malicious profiles found on social media networks. Here the models will be multimodal in nature, able to analyse images and text at the same time, References:- Boffa, M., Valentim, R. V., Vassio, L., Giordano, D., Drago, I., Mellia, M., & Houidi, Z. B. (2023). LogPr\'ecis: Unleashing Language Models for Automated Shell Log Analysis,Computers & Security, Volume 141,2024,- Boffa, M., Milan, G., Vassio, L., Drago, I., Mellia, M., & Houidi, Z. B. (2022, June). Towards nlp-based processing of honeypot logs. EuroS&PW- Valentim, R., Drago, I.,Mellia, M., Cerutti, F.. 2024. X-squatter: AI Multilingual Generation of Cross-Language Sound-squatting. ACM Trans. Priv. Secur.- Valentim, R., Drago, I., Mellia, M. F. Cerutti, Lost in Translation: AI-based Generator of Cross-Language Sound-squatting, EuroS&PW, 2023 List of possible venues for publications: Collaborations and projects: This scholarship is in collaboration with the Ernes Cybsersecurity company, in the context of the FISA-2023 AI4CTI project funded by the Ministry of University and Research with a 6.1 Million Euro grant. |
| Required skills | - Good programming skills (such as Python, Torch, Spark) - Solid Machine Learning knowledge - Knowledge NLP and LLM - Fundamentals of Networking and computer security |