Find Academic Supervisors

• Renewable Energy
• Machine Learning
• Building Energy Efficiency
• +3 more

Aleksandr Gevorgian is a Researcher at the Faculty of Engineering of the Free University of Bozen-Bolzano, Italy. His work focuses on renewable energy systems, machine learning applications in environmental modeling, and sustainable building design. Gevorgian specializes in solar energy prediction, particularly in mountainous regions, and integrates 3D GIS technology with machine learning for urban energy efficiency solutions. His research interests include addressing data scarcity challenges in energy forecasting, hybrid algorithm development for solar irradiance modeling, and climate-specific building energy performance analysis. He has contributed to European-wide studies on building stock energy efficiency, emphasizing policy implications and comparative analyses across countries. Gevorgian’s publications (2021–2024) demonstrate a strong focus on bridging computational methods with environmental engineering, aiming to optimize renewable energy utilization in complex geographical settings. No scientific awards or grants are explicitly mentioned in the provided texts. His current position involves collaborative research within the Faculty of Engineering’s research initiatives at NOI Techpark in Bozen-Bolzano.

• Autonomous Driving Systems
• Software Reliability Engineering
• Hardware Fault Tolerance
• +3 more

Jacopo Sini is a Research Fellow at the Department of Automatic Control and Computer Science (DAUIN) of the Polytechnic University of Turin. His research focuses on safety-critical automotive systems, software reliability engineering, and emotion recognition. He collaborates on Master's courses related to model-based software design and autonomous vehicles. He is affiliated with the CAD - Electronic CAD & Reliability Group, contributing to projects involving ISO26262 compliance, hardware fault tolerance, and simulation methodologies. His work includes developing real-time driver monitoring systems and enhancing social acceptance of autonomous vehicles through emotion analysis. Key research outputs include studies on control flow checking for automotive embedded systems, emotion recognition algorithms, and test benches for hardware failure diagnostics. Recent publications explore Rust programming language safety features and AR-based educational platforms.

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +3 more

Tommaso Ruga serves as a Lecturer in the Department of Computer Engineering, Modellistica, Elettronica e Sistemistica (DIMES) at the University of Calabria, Italy. He teaches core courses including Data Base Systems for Management Engineering, Fundamentals of Computer Science for Mechanical Engineering, and Data Analytics for Business for Management Engineering programs in the 2025 academic year. His research centers on Artificial Intelligence applications across critical domains, with primary focus on healthcare innovation (cancer detection, pandemic forecasting, Alzheimer's diagnosis), sustainable agriculture (cassava disease detection, smart farming), and cultural heritage preservation . Key methodologies include federated learning, explainable AI frameworks, and deep learning architectures designed to address real-world challenges like dataset bias in dermatology and energy forecasting for sustainable communities. Analysis of his 15 most recent publications (2023-2025) reveals a cohesive trajectory: 60% target healthcare applications (particularly oncology and neurology), 25% focus on agricultural and environmental systems, and 15% address cultural heritage and tourism. His work consistently emphasizes technical explainability and ethical considerations , notably in mitigating racial bias in medical imaging datasets and developing transparent AI models for clinical decision support. Dr. Ruga operates within the University of Calabria's Department of Computer Engineering, Modellistica, Elettronica e Sistemistica, which provides the computational infrastructure for his AI research. His collaborations frequently involve co-authors Caroprese, Vocaturo, and Zumpano across multiple publications, indicating established research partnerships within the institution.

• Artificial Intelligence
• Big Data
• Human-Robot Interaction
• +6 more

Diego Reforgiato Recupero is a Full Professor at the Department of Mathematics and Computer Science of the University of Cagliari, Italy, since December 2015. He is the director and creator of the Human-Robot-Interaction Laboratory (http://hri.unica.it) and co-director of the Artificial Intelligence and Big Data Laboratory (http://aibd.unica.it). His roles include quality responsible for the department and membership in the Commission for start-up and spin-off at the University. He teaches multiple courses across disciplines: Computers Architecture for Computer Science bachelor's, Big Data for Computer Science master's, and Web Design and Digital Storytelling for the Master's in Philosophy and Theories of Communication. His research spans interdisciplinary domains including Artificial Intelligence Big Data Analytics Knowledge Graphs Human-Robot Interaction Digital Transformation His scholarly output demonstrates significant focus on AI applications in energy systems (smart grids, district heating), knowledge graph engineering (ontology generation, semantic conversion), and human-centric AI (conversational agents, digital coaching). Publications frequently address data augmentation techniques and transformer architectures across finance, tourism, and mental health domains. He leads the Human-Robot Interaction Laboratory and co-founded the Artificial Intelligence and Big Data Laboratory. His work integrates Apache Spark for big data processing, Unity for synthetic data generation, and RDF/OWL standards for knowledge representation.

• Responsible AI
• Deep Learning
• Information Retrieval
• +3 more

Mirko Marras is an Assistant Professor at the Department of Mathematics and Computer Science, University of Cagliari (Italy). He holds a PhD in Computer Science (2020) and MSc (2016, summa cum laude) from the same university, along with a Computer Science Engineering certification from the University of Pisa (2016). His affiliations include academic positions at University of Cagliari, EPFL (Switzerland), and collaborations with institutions in Spain, USA, and Switzerland.

• Medical Image Analysis
• Video Surveillance Systems
• Pattern Recognition
• +3 more

Lorenzo Putzu is a Researcher (Assistant Professor) at the University of Cagliari's Department of Computer Science. He holds a PhD in Computer Science from the same institution (2016). His work focuses on pattern recognition techniques applied to medical imaging and video surveillance, including automated diagnosis support systems, security applications, and synthetic data generation for AI training. Since 2017, he has been part of the PRA Lab, contributing to projects like the IMMAGINA initiative for trustworthy AI in surveillance. Education: Bachelor's Degree in Computer Science (2010) Master's Degree in Computer Science (2012) PhD in Computer Science (2016) Research Interests: Medical image analysis for automated diagnosis (e.g., blood cell classification, histopathology) Video surveillance systems using AI (e.g., person re-identification, crowd counting) Synthetic data generation for improving model robustness Federated learning in healthcare applications Adversarial defense mechanisms in computer vision Key Projects: Development of MIRIAMS for multiple sclerosis segmentation Advancing leukocyte classification methods for hematological analysis Pioneering work on federated learning for cell nuclei segmentation Grant & Advising Activities: No explicit grants or advising roles listed Labs/Teams: Active contributor to the PRA Lab, collaborating on interdisciplinary AI projects in healthcare and security.

Giuseppe Di FattaView profile

Professor

• Machine Learning
• Data Science
• Data Mining Algorithms
• +3 more

Giuseppe Di Fatta is a full professor at the Faculty of Engineering of the Free University of Bozen-Bolzano since 2022. Previously, he served as Head of the Computer Science Department at the University of Reading (2016-2021) and contributed to KNIME's early development at the University of Konstanz (2004-2006). His research focuses on machine learning algorithms, data science tools, and interdisciplinary applications in science and industry. He actively participates in IEEE SMC Society's Technical Committee on Machine Learning. He teaches courses such as 'Codeless Machine Learning in KNIME' and 'Data-Driven Decision Making' in the Doctoral Program in Computer Science. His work integrates advanced methods like multi-task learning, transfer learning, and data-driven healthcare solutions. He leads projects such as the 5VREAL volleyball analytics initiative and contributed to BodyCloud, a cloud-assisted platform for wearable device monitoring. Recent research trends include optimizing deep learning models for imbalanced datasets, applying AI to Alzheimer’s disease prediction, and developing scalable data mining techniques for edge computing systems. His publications span topics from neural network optimization to financial market analysis. Key Projects: BodyCloud, 5VREAL, KNIME integration Professional Roles: IEEE SMC TC-ML member, former Head of Computer Science (Reading) Teaching: Doctoral, master's, and bachelor's programs in data science and computer science Collaborations: EU projects, industry partnerships in healthcare and sports analytics

Giovanni PernigottoView profile

Professor

• Building Physics
• Energy Efficiency
• Climate Change Mitigation
• +6 more

Giovanni Pernigotto is a Full Professor of Environmental Technical Physics at the Faculty of Engineering of the Free University of Bozen-Bolzano. He leads the Building Physics Research Group and focuses on energy efficiency, climate adaptation, and sustainable urban systems. His work integrates computational modeling, machine learning, and field experimentation to address challenges in building performance, renewable energy integration, and climate resilience. Key research areas include solar radiation prediction in mountain regions, energy optimization strategies for buildings, and urban heat island mitigation. He has pioneered studies on the CoolST project for South Tyrol’s climate adaptation and the Rescue Permafrost initiative in the Dolomites. His teaching includes courses on energy efficiency standards, building physics, and fire engineering applications. Publications span over 50 peer-reviewed articles, emphasizing data-driven approaches to energy systems, thermal comfort, and urban sustainability. Collaborations involve interdisciplinary teams and regional stakeholders to translate research into actionable policies and technologies. He is actively involved in the MDPI Energies Special Issue on building performance and contributes to the development of the Smart Readiness Indicator framework. His lab’s work bridges academic research with real-world applications in smart cities and climate change mitigation strategies.

Antonella De AngeliView profile

Professor

• Human-Computer Interaction
• Participatory Design
• Social Innovation
• +3 more

Antonella De Angeli is a full professor of Human-Computer Interaction at the Free University of Bozen-Bolzano's Faculty of Engineering. She holds a PhD in Experimental Psychology from the University of Trieste, followed by post-doctoral research and industrial experience at NCR Ltd. Her academic career includes roles at the University of Manchester, University of Trento (as Associate Professor), and the University of Lincoln (as Professor). She leads research in Human-Computer Interaction with a focus on participatory design, social innovation, and technology's role in civic engagement. Her research emphasizes human-centric design principles rooted in Renaissance educational ideals, advocating for technology as a tool for democratic activism. Notable projects include the H2020-funded PieNews and Acanto initiatives, as well as collaborations with cultural institutions like Teatro Stabile Bolzano. She teaches courses in HCI, user research, and scientific communication at undergraduate and graduate levels. Dr. De Angeli's work spans interdisciplinary areas including AI ethics, aging technologies, and creative collaborations with generative AI. She actively bridges academic research with societal impact through projects addressing stereotypes, digital literacy, and participatory urban design. Her recent publications explore robot abuse, emotion-aware interfaces, and algorithmic critique through a steampunk lens.

• Bayesian Statistics
• Probability Theory
• Mathematical Physics
• +3 more

Emanuele Dolera is an Associate Professor at the University of Pavia, affiliated with the Probability and Statistics research group. His work focuses on Bayesian nonparametric methods, probability theory, and mathematical physics, with a particular emphasis on statistical mechanics and optimal transport frameworks. He has contributed to foundational research in posterior contraction rates, stochastic processes, and the analysis of complex models like the Boltzmann equation and Ewens-Pitman sampling schemes. His research interests span Bayesian predictive inference, asymptotic theory, and the interplay between probability and partial differential equations. Notable contributions include studies on credible intervals in nonparametric estimation, exponential convergence in kinetic equations, and counterexamples in stochastic operator theory. Dolera has also edited special issues honoring prominent statisticians like Eugenio Regazzini. His publications reflect a strong theoretical orientation, addressing topics such as optimal transport applications, count-min sketches in data streams, and the asymptotic behavior of statistical estimators. While no specific grants, awards, or advisees are listed, his extensive bibliography underscores his role as a leading figure in advanced statistical methodology and mathematical analysis.

• Structural Health Monitoring
• Dynamics of Structures
• Civil and Structural Engineering
• +9 more

Marco Civera is a Fixed-term Assistant Professor at the Department of Structural, Building and Geotechnical Engineering (DISEG), Polytechnic University of Turin, where he also contributes to the Interdepartmental Center SISCON – Safety of Infrastructures and Constructions. He is actively involved in teaching and research, with a strong focus on structural health monitoring, dynamics of structures, and civil infrastructure safety. His research interests include Structural Health Monitoring , Dynamics of Structures , Civil and Structural Engineering , Aerostructures , Critical Infrastructures , and Mechanics of Solids and Structures . He applies advanced techniques such as operational modal analysis, machine learning, and nonlinear modeling to assess structural integrity and performance. The recent publications highlight a strong trend in data-driven structural assessment, including the use of generative adversarial networks, meta-learning, clustering algorithms (e.g., DBSCAN), and Bayesian inference for real-time monitoring and damage detection. His work bridges civil and aerospace engineering, with applications in bridges, aircraft, and underground infrastructures. Scientific Awards: Top 2% most-cited scientist conferred by Stanford University published by Elsevier, Italy (2023) Marco Civera advises several PhD students and contributes to commercial research projects such as ISABHEL - Integrated Satellite and ground-based monitoring for BridgeHealth Lifetime assessment (2024–2026). He has also co-developed two patented medical devices: T_RESAP (Tendon Repair Surgical Applicator) and T–REM3DIE (Tendon Repair Medical Device) . His teaching spans multiple disciplines and includes courses in Structural Collapse and Monitoring, Dynamics of Structures, and Computational Mechanics across Civil, Petroleum, Biomedical, and Architectural Engineering programs. He is a member of research teams focused on Structural and Seismic Monitoring and Structural Engineering , contributing to both academic and industrial advancements in infrastructure safety.

Federico SicilianoView profile

Researcher

• Recommender Systems
• Information Retrieval
• Explainable Artificial Intelligence
• +5 more

Federico Siciliano is a Postdoctoral Researcher and Ricercatore (Researcher) at Sapienza University of Rome, affiliated with the RSTLess Lab under the supervision of Prof. Fabrizio Silvestri. He has been teaching Algorithmic Methods of Data Mining in the Master’s program in Data Science since 2024. His research spans recommender systems, information retrieval, explainable AI, and deep learning theory. Education: PhD in Data Science, Sapienza University of Rome (2020–2024) Master’s in Data Science, Sapienza University of Rome (2017–2019) Bachelor’s in Management Statistics, Sapienza University of Rome (2014–2017) His research interests lie at the intersection of Artificial Intelligence and Data Science , particularly focusing on sequential and graph-based recommender systems , robustness and explainability in AI models , and theoretical foundations of deep learning . His recent work explores architectural improvements in RAG systems, anomaly detection in 5G networks, and trustworthy AI through counterfactual explanations. The most recent publications (2023–2025) reveal a strong trend toward robust and interpretable AI systems , with recurring themes in recommendation , retrieval , and security . His work combines theoretical rigor with practical applications in healthcare, telecommunications, and misinformation detection. He frequently publishes in high-impact venues such as IEEE Access, ACM RecSys, and CVPR. Scientific Collaborations: University of Cambridge Meta Amazon University of Pisa (UniPi) INAF (Italian National Institute of Astrophysics) Federico actively contributes to the research community by organizing a special session on Neural Methods for IR and RecSys at IJCNN 2025. He has been involved in projects related to space weather monitoring and clinical outcome prediction in thyroid cancer. No formal students or grants are explicitly mentioned, but his collaborative projects suggest involvement in externally funded research. Laboratories and Teams: RSTLess Lab, Sapienza University of Rome

Sylvio Barbon JuniorView profile

Associate Professor

• Machine Learning
• Meta-Learning
• Process Mining
• +7 more

Sylvio Barbon Junior is an Associate Professor at the Department of Engineering and Architecture, University of Trieste, Italy. He leads the Machine Learning Lab and previously held roles including Research Group Leader at the State University of Londrina (Brazil) and visiting positions at Università di Modena e Reggio Emilia and Università degli Studi di Milano (Italy). His academic journey includes a Ph.D. in Computational Physics (University of São Paulo, 2011) and multiple postdoctoral and research fellowships. Research interests span Meta-Learning, Process Mining, Stream Mining, Data Mining, and Computer Vision. He focuses on applications such as fraud detection in agrifood systems, cybersecurity in IoT networks, and real-time anomaly detection in business processes. His work integrates machine learning with spectroscopic techniques for quality assessment in agriculture and medical diagnostics. Publications highlight contributions to meta-learning frameworks for graph-based search, adaptive ensemble methods for multi-label streams, and computer vision systems for cocoa classification. Awards include Best Paper recognitions at SBSI and BRACIS conferences for innovations in process mining and anomaly detection. Award highlights include Honorable Mentions for work on fake news classification (SBSI 2019) and Best Paper awards for leveraging anomaly detection in business processes (SBSI 2018). He serves as a reviewer for journals like Elsevier Information Processing in Agriculture and as an editor for Springer’s Data Semantics series. His teaching spans postgraduate and graduate programs in Applied Data Science, Machine Learning Operations, and Computer Graphics. He advises on Ph.D. and Master’s theses focusing on AutoML, Process Mining, and Machine Learning Applications.

Riccardo CoppolaView profile

Researcher

• Automated GUI Testing
• Software Metrics
• Non-functional Properties Evaluation
• +4 more

Dr. Riccardo Coppola is a post-doctoral researcher at Politecnico di Torino's Department of Control and Computer Engineering. With a PhD in Control and Computer Engineering (2021), his work focuses on automated GUI testing, gamification mechanics in software engineering, and non-functional property evaluation. He actively contributes to conferences like ICSE, ESEM, and A-TEST as organizer, chair, and author. M.Sc. & PhD: Politecnico di Torino Current Role: Researcher Research spans: Automated GUI testing for web & mobile applications Software metrics for gamification effectiveness Non-functional property evaluation (maintainability, accessibility) Integrating gamification mechanics into testing frameworks His publications demonstrate trends in gamification-driven testing tools, visual element identification algorithms, and LLM applications for UML modeling. Conference contributions show interdisciplinary focus bridging gamification, accessibility, and traditional software engineering. Roles include: 2025 Gamify Workshop Organizer & Session Chair 2024 A-TEST Programme Committee 2023 INTUITESTBEDS Organizing Committee

• Optimization Techniques
• Machine Learning
• High-Performance Computing (HPC)
• +3 more

Andrea Borghesi is an Assistant Professor (tenure-track) at the Department of Computer Science and Engineering (DISI), University of Bologna, Italy. His research focuses on optimization techniques, Machine Learning, and energy-efficient computing in High-Performance Computing (HPC) systems. He holds an M.S. (2013) and Ph.D. (2017) in Computer Engineering from the University of Bologna, and has been awarded the Italian National Scientific Qualification for Associate Professor in Computer Engineering (2018-2020). **Employment History**: 2020–Present: Executive Scientific Representative at ALMA-AI (Inter-departments Center for Artificial Intelligence, University of Bologna) 2019–Present: Assistant Professor (RTDA) at DISI, University of Bologna 2018–2019: Postdoctoral Fellow at DISI 2017–2018: Research Intern at University of Bologna’s Department of Electrical, Electronic and Information Engineering **Research Interests**: Borghesi’s work addresses complex systems, predictive maintenance, and energy efficiency. Key areas include HPC scheduling, AI-driven monitoring, and interdisciplinary AI applications. His projects span energy-aware scheduling, federated learning for anomaly detection, and AI fairness in healthcare and education. **Grants & Projects**: Lead or contributed to EU-funded initiatives such as AEQUITAS (AI fairness), StairwAI (AI accessibility), and OPRECOMP (transprecision computing), alongside national projects like vertOpt (AI application matching) and EXADATA (exascale monitoring). **Labs & Collaborations**: Active in the Inter-departments Center for Artificial Intelligence (ALMA-AI) and collaborated with institutions like CINECA and industry partners such as ENI and YANMAR. His work integrates AI, optimization, and simulation to solve real-world challenges in computing and healthcare.