Find Academic Supervisors

• Semantic Technologies
• Knowledge Representation
• Natural Language Processing
• +6 more

Aldo Gangemi is a Full Professor in the Department of Philosophy at the University of Bologna, specializing in Informatics (INFO-01/A). His research integrates Semantic Technologies, Natural Language Processing, Data Science, and Cognitive Science to address challenges in knowledge representation, ontology engineering, and cultural heritage informatics. He co-founded the STLab at ISTC-CNR and DHARC at the University of Bologna, and is on leave from Sorbonne Paris Nord University (Computer Science Lab - LIPN). Scientific roles include serving as area chair for Web Semantics , editorial board member for Semantic Web and Applied Ontology , and conference chairs for major events like WWW2015 and ESWC2018. He has led European projects such as GALEN, WonderWeb, NeOn, and MARIO, and developed software tools like FRED, Aemoo, and Framester. Research interests focus on semantic technologies, knowledge patterns, and applications in humanities, medicine, law, and fisheries. Over 250 peer-reviewed publications span these areas, with emphasis on ontology-based knowledge integration, multimodal reasoning, and ethical AI. His work bridges cognitive science and technological innovation, particularly in virtual reality's societal applications and cultural heritage preservation. Notable contributions include the PRIVAFRAME knowledge graph for sensitive data, the ImageSchemaNet ontology for embodied cognition, and the Sandra neuro-symbolic reasoner. His interdisciplinary approach addresses challenges in AI ethics, creative systems, and citizen-driven data curation.

Gabriella CasalinoView profile

Assistant Professor

• Computational Intelligence
• eHealth
• Data Stream Mining
• +5 more

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

Salvatore RuggieriView profile

Professor

• Data Mining
• Knowledge Discovery
• Discrimination Discovery and Prevention
• +9 more

Salvatore Ruggieri is a Full Professor in the Department of Computer Science at the University of Pisa, where he teaches in the Master Programme in Data Science and Business Informatics. He is affiliated with the KDD LAB, a joint research group of ISTI-CNR and the University of Pisa, and actively contributes to national and European AI initiatives such as XAI, NoBIAS, TAILOR, and SoBigData.eu. His research focuses on data mining and knowledge discovery, with a strong emphasis on ethical AI. Key areas include discrimination discovery and prevention, fairness, privacy, explainable AI (XAI), causal inference, and classification algorithms. He has led significant projects such as ENFORCE, a national FIRB project on legal and computational enforcement of non-discrimination and privacy rights in ICT systems (2010–2014), and has served as program chair for the XIII Italian Symposium on Artificial Intelligence (2014). The recent publications (2018–2023) highlight a consistent trend in interpretable and fair machine learning, including selective classification, stability of interpretable models, and causal reasoning for fairness. His work often involves collaboration with leading researchers like Dino Pedreschi and Riccardo Guidotti, and appears in top venues such as AAAI, IEEE TKDE, and WIREs. His scientific honors include the award for the best Ph.D. thesis in Theoretical Computer Science from the Italian Chapter of EATCS. Best Ph.D. Thesis in Theoretical Computer Science, Italian Chapter of EATCS He advises and collaborates with numerous researchers in the KDD LAB and has contributed to major grants and research initiatives in AI and data science. He is involved in educational programs, including the National Ph.D. in Artificial Intelligence - Society, and promotes interdisciplinary research at the intersection of computer science, law, and ethics. Ruggieri is a member of the KDD LAB, where he leads research in ethical and transparent AI. He is also part of large collaborative networks such as SoBigData.eu and HumanE-AI-Net, which aim to build socially responsible and human-centered AI systems.

• Neuroscience
• Cognitive Science
• Neuroimaging
• +7 more

Giacomo Handjaras is an Assistant Professor at the IMT School for Advanced Studies Lucca, affiliated with the MOMILAB research group. His work focuses on neuroimaging, sleep neurophysiology, affective neuroscience, and brain structure-function relationships. He explores topics such as neural correlates of emotions, sleep dynamics, and motor control mechanisms using advanced methodologies like EEG, fMRI, and computational linguistics. Research interests include: (1) Sleep architecture and slow-wave subtypes linked to thalamic activity, (2) Multisensory integration in perception and action, (3) Neuroplasticity in sensory restoration, (4) Computational analysis of dream semantics, and (5) Telemedicine applications in cardiac rehabilitation. His studies often integrate behavioral, neuroimaging, and computational approaches. Recent publications highlight trends in sleep-brain interactions, affective processing across modalities, and neural mechanisms underlying communication deficits. He has contributed to understanding the role of thalamic regulation in sleep stages and the impact of sensory deprivation on brain organization in congenitally blind individuals. His work bridges basic neuroscience with clinical applications in rehabilitation and cardiovascular health.

• Circular Economy
• Sustainable Urban Development
• Climate Resilience
• +7 more

Sara Torabi Moghadam is an Associate Professor in the Interuniversity Department of Regional and Urban Studies and Planning (DIST) at the Polytechnic University of Turin. She serves as a Junior project manager and member of the Interdepartmental Center R3C - Responsible Risk Resilience Centre. Her research focuses on circular economy in built environments , smart resilient urban development , and participatory approaches . She teaches courses in PhD programs on Urban and Regional Development and Master's programs in Urban Planning , emphasizing ESG frameworks and decision-making for sustainable goals. Research Projects: Lead investigator of the EU-funded GREEN-INC project (2024-2026) addressing inclusive climate actions for nature-based solutions. Responsible for a PhD student funding project (2023-2026) in Urban and Regional Development. Teaching and Academic Roles: Member of the College of Architecture and Design and College of Planning and Design at Politecnico di Torino. Core instructor for courses like Decision Making for Sustainable Development Goals and Post-Carbon Sustainable Communities . Research Themes: Her work integrates GIS-based spatial analysis , multi-criteria decision tools , and participatory methodologies to address urban sustainability challenges. Key areas include energy efficiency in cities, nature-based solutions (NBS), and resilience against climate change. She emphasizes ESG frameworks and justice-oriented planning to ensure equitable urban transitions. Supervised Students: Guiding five PhD students in topics like climate resilience, NBS effectiveness, and sustainable urban scenarios. Labs/Teams: Active in the R3C Center, focusing on risk management and resilience strategies for urban systems. Collaborates with interdisciplinary teams on projects like the SCORE H2020 initiative for energy communities.

• Computer Networks
• Edge Computing
• Internet of Things (IoT)
• +5 more

Paolo Bellavista is a Full Professor of Distributed and Mobile Systems at the Department of Computer Science - Science and Engineering, Alma Mater Studiorum – University of Bologna. His research focuses on edge computing, federated learning, IoT, digital twins, and blockchain applications. With over 140 journal articles and 200+ conference papers, his work appears in top venues such as ACM Computing Surveys and IEEE Transactions. He serves as Editor-in-Chief of the MDPI Computers Journal and holds editorial roles in IEEE Communications Surveys&Tutorials and ACM Computing Surveys. His contributions span network optimization, cybersecurity, and smart cities, with notable achievements in citation metrics (h-index 46 on Google Scholar). Research interests include distributed systems, vehicular networks, AI-driven security, and QoS-aware edge infrastructures. He has organized major conferences like IEEE Mobile Cloud and ACM ICDCN. Current projects explore federated learning unlearning, digital twin entanglement, and blockchain-based data spaces. His work bridges theoretical foundations with real-world deployments in smart manufacturing and healthcare. Editorial Roles : MDPI Computers (Editor-in-Chief), IEEE Communications Surveys&Tutorials (Associate Editor), ACM Computing Surveys (Editor) Conference Leadership : Technical Program Chair for IEEE Mobile Cloud 2015, General Co-Chair for SCNS 2018 Key Contributions : Over 140 journal papers, 15 special issues as guest editor, and 200+ conference publications His lab develops middleware for edge-cloud continuum applications and collaborates internationally on projects like InAbled Cities. Office hours are held weekly at the University of Bologna’s DISI department.

• High Performance Computing
• Grid Computing
• Cloud Computing
• +7 more

Giovanni Aloisio is a Full Professor of Information Processing Systems at the Department of Innovation Engineering, University of Salento, Italy. He is a leading figure at the Euro-Mediterranean Center on Climate Change (CMCC), where he serves as Director of the Supercomputing Center and Director of the Scientific Computing and Operations (SCO) Division. He is also a member of CMCC’s Governance bodies, including the Strategic Council and Executive Committee. His research focuses on High Performance Computing (HPC) , grid and cloud computing , and distributed data management . He has played a pivotal role in major European and international initiatives such as the European Grid Forum (Egrid), the EU FP7 IS-ENES projects, the European Exascale Software Initiative (EESI), and the International Exascale Software Project (IESP). His work bridges advanced computing infrastructure with Earth system modeling and climate science. Recent publications highlight a strong trend toward integrating HPC simulations , big data analytics , and machine learning in climate science. His team develops tools like PyOphidia for scalable data analytics and explores AI-driven methods for weather and climate modeling, pandemic forecasting, and environmental data downscaling. The research spans interdisciplinary domains including climate modeling, epidemiology, and environmental health. He has authored over 100 peer-reviewed journal articles. His leadership in EU-funded projects and international working groups, such as the ENES HPC Task Force and the WCES Working Group, reflects his significant contributions to the scientific community. He leads the HPC laboratory at the University of Salento and oversees advanced computing operations at CMCC. His work supports large-scale scientific workflows, climate data spaces within the European Open Science Cloud, and the convergence of HPC, AI, and data analytics.

• Operations Research
• Healthcare Optimization
• Predictive Maintenance
• +4 more

Rosita Guido is an Associate Professor in the Department of Mechanical, Energy and Management Engineering at Università della Calabria, specializing in Operations Research (MATH-06/A). Her academic role focuses on mathematical optimization and artificial intelligence applications across healthcare, manufacturing, and sustainable systems. She teaches graduate courses including Tools and Methods for Innovation in Healthcare and Business Intelligence within the Management Engineering program. Her research interests span healthcare optimization, predictive maintenance, machine learning applications, and sustainable supply chain management. Guido develops advanced mathematical models and algorithms for complex decision problems under uncertainty, with particular emphasis on healthcare systems optimization, tool condition monitoring, and resource allocation. Her methodological expertise includes mathematical programming, combinatorial optimization, stochastic programming, and hybrid AI techniques. Analysis of her recent publications reveals a strong interdisciplinary focus bridging engineering and healthcare. Key trends include the application of machine learning (particularly SVMs and CNNs) to medical diagnostics, optimization of healthcare operations (patient admission scheduling, bed management), and integration of AI with IoT for industrial applications. Her work demonstrates consistent innovation in solving real-world problems through mathematical modeling and algorithm development. As an active member of the Operations Research (Ricerca Operativa) research group at DIMEG, she contributes to projects addressing logistics optimization, green transportation systems, energy infrastructure management, and clinical process optimization. The group maintains international collaborations with prestigious research centers and companies including Amazon. Guido's teaching portfolio includes graduate-level courses in Management Engineering, where she integrates her research expertise into curriculum development. Her academic service includes participation in departmental governance and research initiatives within the Department of Mechanical, Energy and Management Engineering.

• Dependability
• Stochastic Modeling
• Cloud Computing
• +6 more

Paulo Romero Martins Maciel is a full professor at the Center for Informatics, Federal University of Pernambuco (UFPE), Brazil, where he has been a member since 2001. He previously served as a professor in the Department of Electrical Engineering at the University of Pernambuco from 1989 to 2003. He completed his graduate degree in electronic engineering from the University of Pernambuco in 1987, followed by master's and doctorate degrees in electronic engineering and computer science from UFPE. During his doctoral studies, he conducted a "sandwich internship" at Eberhard-Karls-Universität Tübingen, Germany (1996-1997), and later spent a sabbatical year at Duke University's Edmund T. Pratt School of Engineering. Graduate Degree: Electronic Engineering, University of Pernambuco (1987) Master's Degree: Electronic Engineering and Computer Science, Federal University of Pernambuco Doctorate: Electronic Engineering and Computer Science, Federal University of Pernambuco Professor Maciel leads the MoDCS Research Group and has established himself as a leading expert in dependability engineering, stochastic modeling, and performance evaluation of computing systems. His research spans cloud computing, edge computing, software aging and rejuvenation, and reliability analysis of complex systems. His work demonstrates a consistent focus on developing mathematical models (particularly Markov chains and Petri nets) to evaluate system behavior under various conditions. Recent research extends into emerging areas including Metaverse infrastructure, drone surveillance systems, satellite communications, and smart agriculture applications. His publication record is impressive with 191 publications and 1,298 citations, showing remarkable productivity and impact across nearly three decades. His most recent work (2024-2025) continues to push boundaries in aging dependability of cloud-edge systems for digital twins, high availability quantification in Metaverse storage, and stochastic modeling of satellite systems. His research methodology consistently applies hierarchical modeling approaches to address complex system interdependencies. Professor Maciel has mentored numerous researchers who have become active contributors in the field, including doctoral students Paulo Pereira and several co-authors who appear in his recent publications. His collaborative network extends across Brazil and internationally, reflecting the global recognition of his expertise.

• Wireless Systems and Networks
• Internet of Things (IoT)
• Digital Twin
• +9 more

Luciano Bononi is a Full Professor and Deputy Head of the Department of Computer Science and Engineering at the University of Bologna. He holds a PhD in Computer Science (2002) and has been active in academia since 2001, progressing through roles like Researcher, Senior Researcher, Associate Professor (2011), and Full Professor (2020). His research focuses on Wireless Systems, IoT, Digital Twins, Smart Cities, and Mobile Applications , with over 160 peer-reviewed publications and major contributions to EU projects like Arrowhead and IoE. Education: MS (Laurea) in Computer Science, University of Bologna (1997, Summa cum Laude) PhD in Computer Science, University of Bologna (2002) Research Interests: Bononi's work spans Wireless Protocols, IoT Platforms, Digital Twin Integration with AI, Smart Mobility/ Energy Systems, and Edge Computing . He leads the WiLMA Lab and the ALMA-AI CoInnovation Lab , focusing on real-world IoT deployments and smart city solutions. Key Contributions: Coordinated EU projects like Arrowhead (2013-2017) and national initiatives like PNRR-Mobility-Spoke 7 Recipient of 4 Best Paper Awards and top rankings in global scientist databases Editorial roles at 7 journals including Wiley's Wireless Communications and Elsevier's Ad Hoc Networks Organized over 15 international conferences as Chair and participated in 200+ TPC roles Labs & Teams: Directs the WiLMA Lab (Wireless Systems and Mobile Apps) and the ALMA-AI CoInnovation Lab , emphasizing interdisciplinary collaboration between academia and industry.

• Bayesian inference
• Machine learning
• Nonparametric methods
• +5 more

Francesca Panero is an Assistant Professor (RTT) in Statistics at the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome. She also serves as a Visiting Fellow at the Department of Statistics of the London School of Economics and Political Science, where she was previously an Assistant Professor until May 2024. Additionally, she maintains an affiliation with the Grantham Research Institute on Climate Change and the Environment at LSE. Dr. Panero earned her PhD in Statistics from the University of Oxford in 2022, following undergraduate and master's studies at the Department of Mathematics of the University of Turin and Collegio Carlo Alberto. Her academic journey reflects a strong foundation in mathematical and statistical theory applied to complex real-world problems. Her research centers on Bayesian statistical methodologies with applications across multiple domains. She specializes in complex networks analysis using Bayesian nonparametric approaches, disclosure risk assessment for privacy preservation, spatio-temporal Gaussian process modeling for food insecurity prediction, and fair machine learning techniques. Her work demonstrates a consistent thread of developing rigorous statistical methods with practical societal applications, particularly in network science, privacy protection, and algorithmic fairness. Dr. Panero's publications reveal a strong trajectory in high-impact statistical research, with recent work focusing on sparse network modeling, fairness constraints in machine learning, and privacy-preserving statistical techniques. Her methodological contributions bridge theoretical statistics with applications in social sciences, public policy, and computational challenges. 2025 Visiting Lecturer at Faculty of Informatics, University of the Italian Switzerland (USI) Elected as j-ISBA Chair Elect for 2025-2026 Recipient of IMS junior research travel grant for ICSDS 2024 Awarded LSE Research and Impact Support Fund (RISF) grant for food insecurity research As an educator, Dr. Panero teaches Probability and Stochastic Processes at Sapienza University and has previously instructed Deep Learning and Artificial Intelligence courses at LSE. She actively mentors students and encourages PhD candidates interested in her research areas to contact her. She is also a core member of GENIAL, an LSE focus group examining how students utilize generative AI tools for learning. Beyond her technical work, she has been engaged in equality, diversity, and inclusion initiatives since 2020 and maintains an active academic blog discussing data visualization, research projects, and academic life.

Gabriele AccarinoView profile

Research Fellow

• Machine Learning
• Climate Science
• Environmental Data Analysis
• +4 more

Gabriele Accarino is a Postdoctoral Research Scientist at Columbia University's Learning the Earth with Artificial Intelligence and Physics (LEAP) Science and Technology Center, focusing on machine learning applications in climate science. He previously held a research fellowship at the Department of Engineering for Innovation at the University of Salento and served as an adjunct professor there. His career includes a Junior Scientist role at the CMCC Foundation's Advanced Digital Innovation Center (ADIC), where he led the machine learning research unit and contributed to European projects like Silvanus. Education: Master’s in Computer Engineering (with honors), University of Salento PhD in Environmental Sciences, University of Salento His research spans data-driven climate modeling, spatio-temporal analysis, and machine learning for extreme weather events, pandemics, and environmental conflicts. He specializes in LSTM networks, Transformer architectures, and HPC integration with big data analytics. Recent publications highlight his work on AI applications in aquatic sciences, climate change knowledge transfer, ensemble machine learning for cyclones, and pandemic modeling. His projects address interdisciplinary challenges like environmental-COVID-19 links and pollution-conflict dynamics. Scientific Awards: Research Fellowship at University of Salento's Department of Engineering for Innovation Gabriele co-supervises European research initiatives and maintains affiliations with the CMCC Foundation while advancing postdoctoral work at Columbia University. His expertise bridges climate science, computational methods, and public health analytics.

Michael ShekelyanView profile

Lecturer

• Data Management
• Database Systems
• Differential Privacy
• +5 more

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (2023–present), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano (2018) and a Diploma in Media Informatics from Ludwig Maximilian University of Munich (2014). Previously, he worked as a Research Associate at King's College London (2021–2023) and a Research Fellow at the University of Warwick (2018–2021). His academic journey includes roles as a Meta-Reviewer for NeurIPS and ICDT Proceedings Chair (2024), with extensive service as a reviewer for top conferences like ICML, NeurIPS, ICLR, and journals such as TKDE and IEEE T-IFS. Research Interests: Focuses on algorithms and data structures for managing large/sensitive datasets, including differential privacy, random sampling (e.g., Hidden Shuffle Method), and multidimensional data summarization (e.g., DigitHist). His work bridges theoretical guarantees with practical applications in data management and machine learning. Notable contributions include privacy-preserving top-k selection, efficient sampling over joins, and error-bounded data summaries. Publications: Over 15 peer-reviewed articles in top venues including SIGMOD, ICDE, EDBT, AISTATS, and PVLDB. Key works include 'Streaming Weighted Sampling over Join Queries' (EDBT'23), 'Sequential Random Sampling Revisited' (AISTATS'21), and 'Sparse Prefix Sums' (Information Systems'19, which won an ADBIS award). Grants/Awards: Received the ADBIS'17 Award for Sparse Prefix Sums. Active in organizing conferences and mentoring early-career researchers through reviewing roles. Current projects include a PhD studentship on privacy-preserving algorithms for medical data sharing. Labs/Teams: Leads the Privacy-Preserving Algorithms initiative at Queen Mary, collaborating with international research networks in database systems and data privacy.

Gianluca SchiavoView profile

Researcher

• Human-Computer Interaction
• Gamification
• Educational Technology
• +4 more

Gianluca Schiavo is a Researcher at the i3 unit of Fondazione Bruno Kessler in Trento, Italy. His work intersects Human-Computer Interaction (HCI), educational technology, and gamification, with a focus on designing digital interventions for social challenges. He specializes in creating platforms addressing child safety, gender-based violence prevention, and inclusive AI education. Schiavo's research explores: Gamification strategies for sensitive topics like child protection and gender-based violence AI-driven educational tools for K-12 curriculum development Collaborative digital storytelling for conflict resolution and migrant self-expression Playful learning interfaces promoting sustainability and behavioral change His 31 publications (2011-2025) show strong trends in: Applying gamification to social issues (45% of recent work) Developing educational technologies for marginalized groups Designing evaluation frameworks for sensitive HCI applications Schiavo leads projects such as Cesagram (child protection platform) and StandByMe (gender-violence awareness tool), emphasizing co-design with stakeholders. He collaborates extensively with FBK colleagues and University of Trento researchers across 18+ joint publications.

Giovanni SparacinoView profile

Professor

• Biomedical Signal Processing
• Physiological Systems Modeling
• Continuous Glucose Monitoring
• +3 more

Giovanni Sparacino is a Full Professor at the Department of Information Engineering, University of Padova, Italy. He holds teaching responsibilities for courses in Medical Informatics and Biological Data Analysis, while actively contributing to PhD supervision and departmental leadership roles including Vice-Head (2016/17) and Vice-Director of the PhD School in Information Engineering. Education: MSc in Electronics Engineering (summa cum laude), University of Padova (1992) PhD in Bioengineering, Polytechnic of Milan (1996) His research focuses on Biomedical Signal Processing, Computational Neuroscience, and Physiological Systems Modeling. Key contributions include glucose dynamics modeling, nonlinear signal analysis, and advanced algorithms for continuous glucose monitoring sensors. He has extensive experience in multimodal neuroimaging (EEG, fNIRS) and parameter estimation techniques. Notable research trends include: Development of machine learning approaches for diabetes management Integration of EEG and fNIRS for cognitive workload assessment Creation of wearable biomedical devices for foot pressure monitoring Advancements in physiological modeling and deconvolution techniques He has led the Bioengineering track PhD program since 2012 and contributed to establishing a web-based platform for multimodal neuroimaging data analysis.