Find Academic Supervisors

• Engineering Drawing
• Building Information Modeling (BIM)
• Geographic Information Systems (GIS)
• +12 more

Maurizio Marco Bocconcino is an Associate Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at Politecnico di Torino. He is a member of the Interdepartmental Center R3C (Responsible Risk Resilience Center) and serves as Coordinator of basic subjects for the 2nd year of Engineering. His academic work bridges civil engineering and architectural design, with a strong focus on representation and digital modeling. His research interests include engineering drawing, territorial and urban surveying, geographic information systems (GIS), Building Information Modeling (BIM), data representation, and tools for urban and social regeneration. He investigates methods for surveying historic buildings, urban resilience, and the integration of digital technologies in heritage and urban planning. His work is aligned with UN Sustainable Development Goals 11 and 17, emphasizing sustainable cities and collaborative research. Bocconcino's recent publications explore digital archives for academic heritage, analog artifacts in engineering education, urban form, LEAN-BIM integration, and the visualization of social impact. His research outputs span journals such as DISEGNO , International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , and AGATHÓN , as well as conference proceedings in representation and urban planning disciplines. He is actively involved in research projects, including the EU-funded MAINCODE project on urban climate shelters and multiple commercial consulting contracts as Scientific Manager, focusing on urban and social regeneration. His teaching responsibilities include courses in engineering drawing, digital modeling, and graphic language across Civil, Environmental, and Building Engineering programs. Bocconcino collaborates with researchers such as Mariapaola Vozzola, Giorgio Garzino, Martino Pavignano, and Fabio Manzone. His affiliations with ERC sectors highlight expertise in civil engineering, computational modeling in culture, computer graphics, design, and information systems.

Silvia BOZZAView profile

Associate Professor

• Forensic Statistics
• Bayesian Statistics
• Evidence Evaluation
• +4 more

Silvia BOZZA is an Associate Professor in the Department of Economics at Ca' Foscari University of Venice, where she also serves as Department Delegate for Teacher Training for High Schools. Her academic profile is centered around Statistics (SSD STAT-01/A), with a strong focus on forensic applications of statistical methods. She maintains dual institutional affiliations, working within both the Department of Economics (based in San Giobbe) and the Interdepartmental School of Economics, Languages and Entrepreneurship for International Exchanges (located in Treviso - Palazzo San Paolo). Professor BOZZA's research primarily explores the application of Bayesian statistics to forensic science and legal evidence evaluation. Her work demonstrates a consistent trajectory from early publications on spatio-temporal models to her current specialized focus on forensic statistics. She has developed expertise in probabilistic graphical models, Bayes factors, and statistical decision theory as applied to evidence interpretation. Her research addresses critical questions in forensic science regarding how to properly evaluate and communicate statistical evidence in legal contexts, with particular attention to handwriting analysis, DNA evidence, and authorship attribution. Her publication record shows remarkable consistency and productivity, with over 90 publications spanning from 2001 to 2025. Recent work (2022-2025) demonstrates her ability to address contemporary challenges, including AI authorship discrimination using Bayesian methods for distinguishing human from ChatGPT-generated text. Her articles frequently appear in high-impact forensic and statistical journals including Law, Probability & Risk, Forensic Science International, and Annual Review of Statistics and its Application. Professor BOZZA actively supervises undergraduate and graduate theses involving data analysis, requiring knowledge of statistical software including the R programming language used in the Statistics for Economics Laboratory course. She encourages students to propose topics while offering guidance to refine research questions. Her office hours show she maintains regular availability for student consultations, indicating active engagement in academic mentoring.

Marianna Nicolosi AsmundoView profile

Associate Professor

• Mathematical Logic
• Automatic Deduction
• Description Logic
• +7 more

Marianna Nicolosi Asmundo is an Associate Professor of Mathematical Logic (MAT/01) at the Department of Mathematics and Computer Science (DMI) of the University of Catania. She earned her PhD from the same university in 2003 and serves as a proposing member of CINUM - Interdepartmental Center for Humanistic Computing. Teaches undergraduate and graduate programs in Mathematics Master's Degree Programs in Computer Science Textual Sciences for Digital Professions (Department of Humanities) Her research spans automatic deduction, tableaux-based deductive systems, decision procedures in elementary set theory and non-classical logic, interactive theorem proving, knowledge bases, ontologies, and reasoning services for the semantic web. Recent publications emphasize ontology interoperability, blockchain-based knowledge representation, and set-theoretic reasoning frameworks. She contributes to ontologies for cultural heritage (Saint Gall monastery modeling, archaeological Sicilian landscapes) and blockchain applications. Collaborates extensively with researchers like D. Cantone and D.F. Santamaria on description logic systems. Active member of CINUM - Interdepartmental Center for Humanistic Computing Supervises thesis projects on semantic web and blockchain ontologies

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +6 more

Prof. Fabio Galasso is a Full Professor in the Department of Computer Science at Sapienza University of Rome, where he heads the Perception and Intelligence Lab (PINLab). His research focuses on fundamental innovation in computer vision and machine learning, with particular emphasis on distributed intelligent systems, perception frameworks, and general intelligence within sustainable and interpretable AI contexts. His research interests span multiple domains of computer vision including video segmentation , motion forecasting , distributed intelligent systems , and shape reconstruction . Galasso's work emphasizes sustainable AI approaches that operate within constrained computational resources while maintaining interpretability and verifiability. His research bridges theoretical foundations with practical applications across retail, smart cities, and industrial settings. His recent publications demonstrate a clear progression toward increasingly complex human motion understanding and forecasting, with a strong emphasis on real-world applications. The research trajectory shows movement from foundational video segmentation techniques toward sophisticated motion prediction systems and anomaly detection frameworks that integrate multiple modalities. Key themes include temporal consistency, computational efficiency, and practical deployability in resource-constrained environments. His scientific achievements have been recognized with prestigious awards: 2019 IoT/WT Innovation World Cup 2019 Digital Champions Award 2018 Deutscher Digital Award Galasso has coordinated significant research initiatives including a Marie Sklodowska-Curie Actions project (Horizon 2020) and served as Principal Co-Investigator in multiple German-funded projects from the Ministry of Education and Ministry of Economics. His industry experience includes founding and directing OSRAM's Computer Vision Department in Munich, where he led R&D efforts connecting AI research with smart lighting applications, resulting in successful innovation transfers like the award-winning VISN product. He leads the Perception and Intelligence Lab (PINLab) at Sapienza University of Rome, fostering research that spans fundamental computer vision techniques to practical implementations in retail, smart cities, and industrial applications. The lab maintains strong connections with both academic institutions (including previous collaborations with University of Cambridge and Max Planck Institute) and industry partners.

• Computer Vision
• Natural Language Processing
• Artificial Intelligence
• +7 more

Federico D'Asaro is a researcher and PhD student at Politecnico di Torino, affiliated with the Department of Control and Computer Science (DAUIN) and the Computer Graphics & Vision Group (CGVG). He works as an external lecturer and teaching assistant for the Applied Data Science Project course in the Data Science and Engineering program. His research focuses on Vision-Language Models (VLMs), particularly addressing the Modality Gap in multimodal feature spaces and their applications in downstream tasks like semantic segmentation and speech emotion recognition. Education: Master's degree in Data Science and Engineering (2021), currently pursuing PhD in Computer and Systems Engineering (39th cycle, 2023-2026). His research at the intersection of Natural Language Processing and Computer Vision investigates how reducing the Modality Gap improves crossmodal performance. Recent work applies Large Speech Models (LSMs) to cross-lingual emotion recognition and non-verbal vocalization tasks. He collaborates with researchers like Andrea Bottino, Giuseppe Rizzo, and Juan José Márquez Villacis on projects involving multimodal deep learning and feature extraction. The trends in his publications highlight expertise in multimodal learning (Vision-Language Models, speech-text alignment), deep learning for segmentation and emotion recognition, and crossmodal adaptation in speech processing. His 2025 work focuses on contrastive alignment and non-verbal vocalization, while 2024 studies explore transfer learning of speech models across languages. Teaching Contributions External lecturer for Applied Data Science Project (2025/26) Course collaborator for Applied Data Science Project (2024/25) Federico is part of the Computer Graphics & Vision Group (CGVG) , contributing to interdisciplinary projects that bridge Computer Vision , Natural Language Processing , and Speech Emotion Recognition . His work emphasizes practical applications of multimodal models in real-world scenarios.

Marco GrangettoView profile

Professor

• Computer Vision
• Image Processing
• Artificial Intelligence
• +5 more

Marco Grangetto serves as Full Professor in the Department of Computer Science at the University of Turin, coordinating research in image processing and computer vision. His expertise spans wavelets, image/video coding, data compression, error resilient video coding, and biomedical image processing, with significant contributions to ISO JPEG2000 standardization and editorial roles in IEEE Transactions on Multimedia. His educational background includes a PhD in Electrical and Communications Engineering (2003) and MSc in Telecommunications Engineering (1999), both from Politecnico di Torino. His research integrates Artificial Intelligence and Deep Learning with medical imaging and fundamental compression theory , producing innovations in neural network pruning, capsule networks, and entropy-based models. Recent work focuses on Covid-19 diagnosis from chest X-rays and efficient 3D scene modeling. His publication trends reveal dual trajectories: applied medical AI (Covid-19 diagnostics, lung cancer segmentation) and theoretical advances (learned compression, contrastive learning, bias mitigation). This bridges clinical validation with information-theoretic foundations, particularly in resource-constrained environments. Scientific recognition includes: Premio Optime by Unione Industriale di Torino (2000) Fulbright Grant for research at UC San Diego (2001) He maintains leadership through IEEE editorial positions, ISO standardization participation, and MPAI membership. His research group develops medical datasets (UniToChest, UniToPatho) while advancing neural network efficiency for clinical deployment. Current projects focus on entropy minimization techniques and unbiased representation learning for healthcare applications.

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +6 more

Francesco Strada is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. He serves as a Course Lecturer for Virtual Reality and Technical Art for Cinema and Video Games, and as a Course Collaborator for multiple courses across Computer Engineering, Film and Media Engineering, and Architecture programs. He is an active member of the College of Computer, Film and Mechatronics Engineering and the College of Architecture and Design teaching committees. Dr. Strada's research focuses on Computer Graphics, Virtual Reality, Augmented Reality, and Human-Computer Interaction with particular emphasis on Embodied Conversational Agents, emotion recognition, and serious games applications. His work bridges technical computer science with psychological and human factors considerations to create more believable and effective virtual experiences. His research spans applications in education, healthcare, automotive interfaces, cultural heritage, and emergency response training. His recent publications demonstrate a strong trend toward emotionally intelligent virtual agents, VR/AR applications in specialized domains, and technical innovations in latency management and digital human representation. His work often combines psychological principles with technical implementations to enhance user experience and system effectiveness. Dr. Strada actively supervises PhD students including Alessandro Emmanuel Pecora, Stefano Calzolari, and Leonardo Vezzani, whose research focuses on Emotionally Aware Embodied Conversational Agents (E2CA) and Car AR-HUD design. He leads significant research projects including Holo-BLSD (2024-2025), a Mixed Reality tool for first aid emergency response training, and '50 shadows of AI' (2025), focusing on personalized education in corporate settings. He is a member of the CGVG - Computer Graphics and Vision Group at DAUIN, where his team develops cutting-edge applications in AR/VR, Human-Computer Interaction, User Experience, Computer Vision, Machine Learning, and Artificial Intelligence. His research has practical applications in education, training, healthcare, automotive interfaces, and cultural heritage preservation.

• Machine Learning
• Human-Computer Interaction
• 3D Graphics
• +8 more

Chiara Innocente is a PhD student in Management, Production, and Design at the Department of Management and Production Engineering (DIGEP) of Politecnico di Torino. She holds the academic rank of Consultant Lecturer and contributes to teaching in courses related to 3D graphics in biomedical engineering applications. Her research spans computational modeling, human-computer interaction, and machine learning, with a focus on rehabilitation engineering, cultural heritage preservation, and medical imaging. Education: PhD in Management, Production, and Design (2022-2025) Research interests include: Machine learning applications in healthcare and rehabilitation engineering Human-computer interaction for augmented/virtual reality 3D graphics and computational modeling for biometric and cultural heritage Convolutional neural networks in medical image analysis Her recent publications highlight interdisciplinary work bridging computer science with clinical applications and cultural preservation. She collaborates on national patents for athlete performance evaluation systems and participates in the Augmented Humans research framework.

• Industrial Engineering
• Sustainable Manufacturing
• Circular Economy
• +3 more

Luigi Panza is a Fixed-term researcher at the Department of Management and Production Engineering (DIGEP) within the College of Management and Production Engineering at the Polytechnic University of Turin. He also serves as an invited member of the College of Mechanical, Aerospace, and Automotive Engineering. His scientific disciplinary sector is Manufacturing Technologies and Systems (IIND-04/A) under Area 0009 - Industrial and Information Engineering. Dr. Panza's research centers on sustainable production systems with emphasis on circular economy integration, Industry 4.0 applications, and advanced manufacturing technologies. His work bridges environmental policy, digital transformation, and industrial processes, particularly examining carbon taxation impacts, additive manufacturing sustainability, and laser-based polymer processing. He actively explores cross-sector applications including agricultural technology modernization through digital tools. His 2024-2025 publications reveal a strong focus on systemic industrial transitions, combining bibliometric analysis with empirical environmental assessment. Key trends include circular economy frameworks using 10R principles, life-cycle evaluation of decentralized manufacturing, policy-driven business model shifts, and material-specific advanced production techniques. No scientific awards were documented in available sources. Panza serves as course collaborator across five engineering disciplines at Polytechnic University of Turin, teaching Manufacturing Processes, Production Systems, and Graphic Communication since 2020/21 with assignments extending through 2025/26. His instructional scope spans Mechanical, Automotive, Industrial Production, and Management Engineering programs, including the Challenge@PoliTo NewLAW project on alumina waste valorization. No student advising or grant details were specified in current profiles.

• Computer Vision
• Computer Graphics
• Machine Learning
• +5 more

Umberto Castellani is a Full Professor at the Department of Computer Science, University of Verona. His research spans computer vision, computer graphics, and machine learning, with applications in biomedical analysis and cultural industries. Research Focus 3D acquisition and registration Geometric deep learning and spectral methods Neuroimaging (diffusion MRI, fNIRS, EEG) Augmented/virtual reality and human-machine interaction Key Projects INDICE - Digital Innovations for Cultural Industries (2025) VVV - Virtual simulation of natural ecosystems (2023) SARAS - Robotic surgical assistant (2018) Collaborations with game industry and biomedical companies Laboratory : Member of Vision, Image Processing & Sound (VIPS) Lab.

Beppe LiottaView profile

Professor

• Network Analysis
• Graph Drawing
• Data Visualization
• +5 more

Beppe Liotta is a Full Professor at the Department of Engineering, University of Perugia. He serves as Rector Delegate for ICT and Digital Agenda. His research spans network discovery, graph drawing, algorithm engineering, and computational geometry . Laurea in Electrical Engineering (1990), Ph.D. in Computer Engineering (1995), both from University of Rome 'La Sapienza' Post-doc at Brown University (1995-1996) Current teaching: Information Visualization and Database Management Systems Liotta has authored over 170 papers and led projects like VisFAN (financial crime detection), VHyXY (large graph visualization), COWA (web traffic analysis), and WhatsOnWeb (web clustering). His work focuses on hybrid visualizations and network robustness . Recent articles highlight his expertise in biological networks , financial activity networks , and one-to-many matched graph visualizations . He has contributed to journals like IEEE Transactions on Visualization and Computer Graphics and conferences including PacificVis and Graph Drawing . Liotta actively participates in scientific service, including editorial roles for the Journal of Graph Algorithms and Applications and program committees for IEEE PVIS 2019.

Giancarlo ManziView profile

Associate Professor

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +5 more

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

Francesco RagusaView profile

Research Fellow

• Computer Vision
• Pattern Recognition
• Machine Learning
• +5 more

Francesco Ragusa is a Research Fellow at the University of Catania, Italy, holding an Industrial Doctorate in Computer Science (2021). He spent part of his PhD at the University of Hertfordshire, UK. His work focuses on First Person (Egocentric) Vision, including Human-Object Interaction, Industrial Applications, and Augmented Reality. He co-founded NEXT VISION s.r.l., an academic spin-off from the University of Catania since 2021. Key projects include the MECCANO dataset for industrial human-object interactions and the EGO-EXO4D initiative analyzing skilled human activity from multi-perspective views. Research interests span Computer Vision, Pattern Recognition, and Machine Learning. He contributed to seminal datasets like MECCANO and ENIGMA-51, advancing understanding of human behavior in industrial settings. His work emphasizes practical applications, such as wearable assistive systems and visual navigation solutions. Ragusa has delivered tutorials at major conferences (e.g., ICIAP, VISIGRAPP) on First Person Vision’s history, challenges, and trends. He actively promotes industrial collaborations and has secured grants including PNRR MUR (Code E63C22001940006) and EU funding under Next Generation EU. His teaching and professional activities include organizing workshops on egocentric vision for AI-driven assistants and industrial safety systems. Current research directions involve multimodal synthetic data utilization, gaze-based interaction analysis, and cross-modal fusion for human-robot collaboration.

• Artificial Intelligence
• Neuromorphic Computing
• Brain-inspired Computing
• +13 more

Vittorio Fra is a Fixed-term Assistant Professor at the Interuniversity Department of Regional and Urban Studies and Planning (DIST) at Politecnico di Torino, where he conducts research in artificial intelligence, neuromorphic computing, edge computing, and robotics. He is a member of the PIC4SeR Interdepartmental Center for Service Robotics and contributes to interdisciplinary research bridging engineering, nanotechnology, and smart urban systems. His research interests center on AI and neuromorphic computing for industrial and IoT applications , with strong emphasis on brain-inspired computing, memristive devices, and nanoscale technologies. His work spans from low-level hardware characterization to high-level algorithm design, integrating machine learning, bio-inspired computing, and scientific simulation. He actively explores neuromorphic solutions for real-world edge applications such as human activity recognition, smart traffic control, and assistive technologies like Braille readers. The trend across his recent publications (2022–2025) reveals a consistent focus on deploying spiking neural networks and neuromorphic architectures on commercial edge devices, optimizing neural execution, developing benchmarking tools (e.g., NeuroBench, WiN-GUI), and validating neuromorphic solutions on practical problems like Sudoku and the knapsack problem. His work bridges theoretical AI with applied engineering, targeting sustainability and innovation in infrastructure and urban communities. Scientific Awards: No scientific awards explicitly mentioned in the text. Advising and Grants: Vittorio Fra supervises Filippo Aisa , a PhD candidate in Electrical, Electronic, and Communications Engineering. He leads a commercially funded research project titled Supporto allo sviluppo di un smart digital water distributor monitoring system (2025–2026) , serving as the Scientific Responsible. His teaching roles include PhD instruction, course collaboration, and invited membership in academic councils across engineering and planning programs. Labs and Research Teams: He is a member of the PoliTO Interdepartmental Centre for Service Robotics (PIC4SeR) , a multidisciplinary research center focused on robotics for societal applications. His collaborations span multiple institutions and projects, involving teams working on neuromorphic ecosystems (e.g., Lava-Loihi), wireless sensor networks, and brain-inspired computing frameworks.

Michael NeriView profile

Researcher

• Computer Vision
• Deep Learning
• Audio Processing
• +3 more

Michael Neri is a Researcher in Audio Signal Processing at Tampere University and a Ph.D. student in Applied Electronics at Roma Tre University. His primary affiliation is with the Department of Signal Processing at Tampere University. He holds a B.Sc. in Information Engineering from the University of Padua (2019) and an M.Sc. in ICT for Internet & Multimedia from the University of Padova (2021). He was a visiting PhD student at Tampere University of Technology in 2023. His research focuses on Audio Processing, Computer Vision, and Deep Learning, with applications in safety and security. Key projects include RAILGAP (EU Horizon 2020), which uses GNSS for rail positioning, INSECTT (ECSEL EU Project) for secure IoT-AI systems, and ISEEYOO (University of Padova Grant) for anomaly detection in Cyber-Physical Systems. He has also contributed to the ESA-funded VOLIERA project on multi-sensor railway positioning. Neri's work spans environmental sound classification, speaker localization, and AI-driven multimedia quality assessments. His research is supported by grants from the European Union and ESA programs. He actively collaborates on projects addressing occupational safety through VR integration and anomaly detection in audio signals. Contact him via michael.neri@tuni.fi or his professional profiles on ResearchGate and GitLab .