جست‌وجوی استاد و پژوهشگر

• Work Psychology
• Wearable Technology
• Sleep Health
• +۴ مورد دیگر

Luca Menghini is an Assistant Professor at the University of Padova, specializing in occupational health psychology, wearable technology applications, and work-related stress. His research focuses on leveraging digital tools such as wearable devices and mobile sensors to study sleep patterns, workplace behavior, and psychosocial risk factors. Key areas include workaholism impacts, healthcare worker well-being, and the validation of new technologies for health monitoring. His research interests span the intersection of technology and psychology, particularly in: Wearable sensor validation for sleep and physiological monitoring Workplace stress and its physiological correlates (e.g., blood pressure, emotional exhaustion) Multilevel analyses of healthcare environments Experience sampling methodologies for real-time psychological assessment Recent work emphasizes standardized evaluation frameworks for sleep-tracking devices and explores how emerging technologies like VR controllers can integrate physiological sensing. His studies often address contemporary challenges such as digital transformation's mental health implications and menopause management in workplaces. Publications consistently highlight innovative applications of data science in occupational health, with a focus on actionable insights for improving worker well-being through validated technological solutions.

VERONICA MUFFATOمشاهده پروفایل

استادیار

• Spatial Cognition
• Environmental Psychology
• Cognitive Aging
• +۴ مورد دیگر

Veronica Muffato is an Assistant Professor at the University of Padova, conducting research in spatial cognition, navigation, and individual differences. Her work explores how environmental interaction, technology use (e.g., GPS), and psychological factors influence spatial abilities across genders and age groups. She investigates topics such as pro-environmental behaviors linked to exploration tendencies, the impact of aging on cognitive navigation skills, and the emotional dimensions of spatial learning. Her research integrates environmental psychology, cognitive science, and gender studies to understand spatial decision-making and environmental perception. Key themes include: the role of visuospatial working memory in environmental knowledge acquisition, gender differences in wayfinding strategies, and the relationship between technology dependency (GPS) and spatial self-efficacy. She also examines how personality traits and emotional states correlate with injury-related behaviors and active aging outcomes. Her work spans both theoretical contributions (e.g., developing self-report measures for GPS use) and applied insights (e.g., implications for urban design and aging populations). Despite prolific publication output, no specific awards or grants are explicitly mentioned in available texts.

Vito Walter Anelliمشاهده پروفایل

استادیار

• Recommender Systems
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Vito Walter Anelli is an Assistant Professor specializing in the technical and ethical aspects of Recommender Systems. His work focuses on fairness, privacy, adversarial robustness, and regulatory compliance in AI-driven recommendation frameworks. He actively contributes to the development of evaluation methodologies, frameworks for reproducible research, and counterfactual reasoning techniques to assess model fairness. His research spans multiple domains including federated learning for privacy preservation, adversarial attacks on visual and graph-based recommenders, and applications in healthcare such as predicting kidney disease progression. He has organized workshops on Knowledge-Aware Recommender Systems (KaRS) and contributed to conferences like RecSys and Dagstuhl Seminars. Anelli's publications emphasize interdisciplinary approaches, blending computer science with legal and ethical considerations. He has developed frameworks like ELLIOT for rigorous recommender system evaluation and explored the integration of knowledge graphs to enhance recommendation interpretability and user control over data. Key themes in his work include addressing bias in graph collaborative filtering, auditing algorithmic fairness in compliance with regulations, and advancing techniques to protect against adversarial manipulation. His contributions bridge technical innovation with societal impact, particularly in ensuring transparency and accountability in AI systems.

Yashar Deldjooمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

Yashar Deldjoo is an Assistant Professor specializing in artificial intelligence, machine learning, and their applications in healthcare, user modeling, and recommender systems. His research focuses on explainable AI, ethical AI practices, multimodal learning, and brain-computer interfaces. He has contributed to projects like WeBIUM (Wearable Devices and Brain-Computer Interfaces for User Modelling) and ARIEL (emotional support via BCI and LLMs). His work bridges technical advancements with societal impacts, addressing challenges in healthcare diagnostics (e.g., Alzheimer’s disease prediction), environmental sensing (real-time pollution-aware recommendations), and cybersecurity (data poisoning attacks on recommendation systems). He also explores fairness in large language models and the integration of EEG-based emotion recognition systems. Notable contributions include frameworks like BRAINEX (CNN model evaluation for brain age prediction), Ducho (multimodal feature extraction for recommendation systems), and KGUF (knowledge-aware graph-based recommenders). He chairs conferences like RecSys and contributes to industry-relevant standards for trustworthy AI systems. His research emphasizes interdisciplinary collaboration, combining neural networks, ethical considerations, and human-computer interaction. Current projects involve neurorehabilitation tools, quantum security for smart cities, and AI-driven usability testing.

• Wearable Technologies
• IoT in Healthcare
• Biomedical Signal Processing
• +۵ مورد دیگر

Veronica Mattioli is a Researcher in the Department of Engineering and Architecture at the University of Parma, actively contributing to interdisciplinary research at the intersection of engineering and human health. She teaches Multimedia Technologies in the Computer, Electronic and Communications Engineering undergraduate program, demonstrating strong engagement in both education and applied research. Her research focuses on developing and evaluating technological systems for health monitoring, particularly using wearable devices and IoT platforms. Key areas include driver stress assessment , cardiac autonomic response analysis , and physical activity tracking via smartwatches . Her work leverages real-world data collection and signal processing techniques to understand human physiological responses in natural environments. The recent publications (2023–2025) reflect a consistent trend in applying engineering solutions to healthcare and transportation safety challenges. Her studies often involve longitudinal experiments and multi-sensor integration, emphasizing practical deployment and validation of monitoring systems in real-life scenarios such as car driving and sports performance. While no formal scientific awards are listed in the provided text, her active publication record indicates strong scholarly contributions. She collaborates extensively with researchers including Luca Davoli, Laura Belli, Riccardo Raheli, and Gianluigi Ferrari, suggesting participation in a well-established research team focused on biomedical and IoT applications. She advises no students listed in the text, and no information about grants or specific laboratories is provided. However, her involvement in multi-partner projects like DistriMuse implies participation in collaborative, possibly EU-funded or industry-linked initiatives. Her email for professional contact is veronica.mattioli@unipr.it .

Angelika Peerمشاهده پروفایل

استاد

• Robotics
• Control Systems
• Human-Robot Interaction
• +۱۵ مورد دیگر

Angelika Peer is a Full Professor in the Faculty of Engineering at the Free University of Bozen-Bolzano, Italy, a position she has held since November 2017. Previously, she served as a Full Professor at the Bristol Robotics Laboratory, University of the West of England, UK (2014–2017), and as a senior researcher and lecturer at the Technical University of Munich, Germany, where she was also a TUM-IAS Junior Fellow. Her academic base is firmly within engineering, particularly in intelligent and human-centered systems. Diploma in Electrical Engineering and Information Technology, Technical University of Munich, 2004 Doctor of Engineering, Technical University of Munich, 2008 Her research interests are centered on intelligent systems that bridge humans and machines. Key areas include robotics, control systems, human-robot interaction (HRI), emotion recognition through physiological signals, shared control, and intelligent manufacturing. She investigates how dynamic models and machine learning can improve safety, adaptability, and efficiency in human-machine collaboration. Her work spans both theoretical control design and practical applications in Industry 5.0, sustainable production, and assistive technologies. Her recent publications (2023–2025) reflect a strong trend toward data-driven and model-based intelligent systems. Topics include energy-based safety control in HRI, neural-network-based trajectory adaptation in powder compaction, emotion intensity estimation using NARX models and appraisal theory, and task prediction in digitalized shop floors using support vector regression. These works demonstrate expertise in integrating dynamic modeling, machine learning, and real-time control with applications in manufacturing and human-centered computing. Angelika Peer has advised PhD students including Ehtisham Ul Hasan and Isabel Francisca Sota Machado Barradas. She is involved in research initiatives such as the Human-centered Intelligent Systems (HCIS) macro area at her university, promoting interdisciplinary work at the intersection of computer science, engineering, and human factors. Her research is supported by collaborations and publications in high-impact journals and conferences, with a strong emphasis on open access and peer-reviewed dissemination.

• Information Retrieval
• Data Science
• Quantum-inspired Information Retrieval
• +۲ مورد دیگر

Massimo Melucci serves as Associate Professor of Computer Science at the University of Padua's Department of Information Engineering, maintaining an active research and teaching profile with office location in Building DEI/G, Room 312, Padova, Italy. His instructional responsibilities include undergraduate Information Retrieval and Databases courses, alongside graduate-level Computing Methods for Data Science instruction, complemented by freely available lecture notes on information retrieval fundamentals. His research program centers on two pioneering domains: Quantum-inspired Information Retrieval , where he develops mathematical frameworks applying quantum probability to model contextual relevance and interference effects in search systems, and Algorithmic Fairness , investigating trade-offs between ranking effectiveness and equitable outcomes. These threads converge in his exploration of fairness-effectiveness optimization through eigensystems and variation modeling, while his interdisciplinary work extends into diving medicine neurophysiology , analyzing EEG-based brain connectivity disruptions in professional divers under hyperbaric conditions. Recent publication analysis (2020-2025) reveals three dominant trajectories: quantum-inspired neural architectures for multimodal sentiment analysis, rigorous fairness-effectiveness quantification in ranking algorithms, and biomedical applications of signal processing in extreme environments. He actively fosters academic collaboration through initiatives like the BIRDS workshop series and CIRCLE conference, which bridge information science, retrieval, and data science communities. His work demonstrates consistent innovation in applying non-classical probability models to information access challenges while maintaining strong connections to real-world applications in both search technology and human safety.

Daniela Iacovielloمشاهده پروفایل

دانشیار

• Optimal Control
• Biomedical Systems
• Epidemiological Modeling
• +۳ مورد دیگر

Daniela Iacoviello is an Associate Professor at Sapienza University of Rome, working in the Department of Computer, Control and Management Engineering (DIS). She has held teaching positions since 2002, including courses in Automatic Control for Clinical Engineering. Her career spans research roles at Georgia Tech's Broadband & Wireless Networking Laboratory and leadership in interdisciplinary research centers like CISB and CRAS. B.Sc. in Mathematics (1992), Sapienza University of Rome Postdoctoral studies in Identification and Optimal Control (2000-2001), DIS PhD in System Engineering (1998), DIS Her research focuses on optimal control applications in biomedical systems and epidemic modeling, with expertise in image analysis for materials science and signal processing for biological data. Her work combines theoretical control systems with real-world implementations in public health and materials diagnostics. Recent publications demonstrate trends in epidemic containment (HIV, arboviruses, SARS-CoV-2), nonlinear control systems for chemotherapy optimization, and computational imaging for material characterization. She explores cross-disciplinary applications from aerospace research to neurological disorders. She serves on editorial boards of journals like International Journal of Imaging and scientific committees of image analysis conferences. Her funded projects include ASI's SARFIRE and PRIN grants for neural network algorithms in biomedical imaging. Key laboratory affiliations: Center for Biomedical Models & Information Analysis (CISB) Sapienza Aerospace Research Center (CRAS) DIS research group in control theory

• Deep Learning
• Computer Vision
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Letizia Bergamasco is a Ph.D. candidate in Computer and Control Engineering at Politecnico di Torino, currently in her 38th cycle (2022-2025). She is affiliated with the SMILIES research group (reSilient coMputer archItectures and LIfE Sciences) within the Department of Control and Computer Engineering (DAUIN), and collaborates with the LINKS Foundation. Bergamasco received her B.Sc. in Electronics Engineering (2018) and M.Sc. in ICT for Smart Societies (2020) with a Double Degree from Politecnico di Torino and Politecnico di Milano through the Alta Scuola Politecnica program. LINKS Foundation researcher since 2020 Focus on medical/industrial AI solutions Her research combines computer vision and AI for clinical applications, particularly in early dementia detection through facial expression analysis and pediatric pain assessment using camera-based vital parameter evaluation. Recent publications demonstrate her work in: Deep learning for cognitive impairment detection Digital twin architectures for energy optimization Neonatal pain assessment systems LLM applications in pediatric emergency diagnostics Current projects involve developing non-invasive biomarkers for dementia diagnosis and AI algorithms for infant monitoring systems. Bergamasco's work bridges computer engineering with healthcare applications, integrating multimodal data analysis and real-time processing systems.

• Neuropsychology
• Cognitive Neuroscience
• Neurorehabilitation
• +۵ مورد دیگر

Costanza Papagno is Full Professor at the University of Trento's Department of Psychology and Cognitive Science, with additional affiliations at the Center for Mind/Brain Sciences (CIMeC) and Centre for Medical Sciences (CISMED). Her clinical leadership includes serving as Head of the Center for Neurocognitive Rehabilitation (CeRiN) since 2017. With dual degrees in Medicine and Neurology from Milano University and a PhD in Psychobiology, she has held research positions at Cambridge's MRC Applied Psychology Unit and visiting professorships internationally. Her research explores neuropsychological rehabilitation, subcortical recovery mechanisms, Parkinson's cognitive deficits, and neuroplasticity in brain tumors. Key investigations include auditory-verbal memory in sentence comprehension, social cognition in neurodegeneration, and neural correlates of abstract language. She maintains active collaborations in awake surgery protocols and develops clinical assessment tools. Publication analysis reveals concentrated expertise in Parkinson's neurocognition, aphasia rehabilitation, brain asymmetries, and teleneuropsychology platforms. Recent works emphasize digital health interventions, network neuroscience approaches to neurodegeneration, and refinement of intraoperative cognitive mapping techniques. Honors include multiple international grants (ESF, NATO, Erasmus), editorship of the Journal of Neuropsychology, and presidency of the Italian Neuropsychology Society. She coordinates multinational initiatives like the Signature project for social cognition assessment standards in neurocognitive disorders. Clinical innovations include pioneering awake surgery protocols at Milan's Policlinico Hospital and establishing the CeRiN neurorehabilitation center. She regularly contributes to national treatment guidelines and serves on ethics committees for neurosurgical research.

• Human-Centered AI
• Virtual and Augmented Reality
• Social Robotics
• +۶ مورد دیگر

Giuliana VITIELLO is a Full Professor at the Department of Computer Science, University of Salerno. Her research focuses on Human-Centered AI, Social Robotics, Virtual/Augmented Reality, and Gender-Inclusive Language Technology. She has contributed to the design of AI-based tools for institutional communication, therapeutic robotics, and immersive educational environments. Current affiliation: University of Salerno Email: gvitiello@unisa.it Office: Fisciano Campus, Building F2, Room 083 Her recent publications highlight trends in: AI ethics and inclusivity Robot-assisted cognitive therapy Metaverse collaboration tools IoT-driven healthcare systems Immersive educational applications

• Nautical Design
• Industrial Design
• Yacht Design
• +۴ مورد دیگر

Mario Zignego is Full Professor at the University of Genoa , Department of Architecture and Design (DAD), where he coordinates the Nautical Product Design degree course, serves on the Board of Directors for the same programme, and is a member of the Technology Transfer Commission. Research Interests: His scholarly work lies at the intersection of nautical architecture and artificial intelligence . He investigates how multi-agent systems can navigate and manage scientific knowledge, explores human-centred AI for emotion recognition in confined marine environments, and advances industrial and yacht design methodologies augmented by intelligent systems. Teaching & Service: He teaches several core courses across bachelor and master levels: Nautical Design Theory 3 Industrial Design Laboratory 3 – Nautical Product Design Applied Industrial Design B – Nautical Engineering Industrial Design 3-2 Yacht Design Studio Workshop B – Yacht Design Master Publications & Trends: Recent publications (2024–2025) demonstrate a clear trajectory toward embedding AI and affective computing into yacht design workflows, focusing on modular emotion-sensing frameworks, knowledge-navigation agents, and future-oriented concepts of superhuman harmony in marine craft. Offices & Contact: He holds weekly office hours: Wednesday mornings in Genoa (by appointment) and Thursdays in La Spezia (by appointment), reachable at mario.ivan.zignego@unige.it .

• Neuropsychology
• Cognitive Neuroscience
• Placebo Effects
• +۳ مورد دیگر

Mirta Fiorio is a Full Professor at the University of Verona specializing in neuropsychology and cognitive neuroscience within the Movement Sciences Section. Her research explores placebo effects in motor systems, Parkinson's disease rehabilitation, and functional movement disorders using techniques like TMS and VR. Recent work examines how expectation modulates motor learning and how virtual reality affects postural control in neurological patients. Her neuroscience research bridges fundamental mechanisms of motor cognition with clinical applications in neurorehabilitation. Current investigations focus on implicit learning specificity in Parkinson's disease, velocity-based training benefits, and non-deceptive placebo paradigms for therapeutic applications.

Filippo Lanubileمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence
• Human-Computer Interaction
• +۲ مورد دیگر

Filippo Lanubile is a Full Professor and Head of the Department of Computer Science at the University of Bari, where he leads the Collaborative Development Research Group (Collab). He co-founded the academic spin-off PeoplewareAI and previously served as a research associate at the University of Maryland. His research spans human factors in software engineering, AI-augmented development, engineering of AI-enabled systems, and health informatics. Major publications focus on MLOps, emotion recognition in development, and LLM applications in software engineering. Lanubile received the NASA Group Achievement Award (1996), IBM Eclipse Innovation Award (2006), IBM Faculty Award (2008), and Microsoft Research Software Engineering Innovation Foundation Award (2011). He chaired the ACM/IEEE ESEM symposium (2020-2021) and IEEE Software Advisory Board (2020-2023).

• Database Systems
• Assistive Technology
• Smart Home Systems
• +۵ مورد دیگر

Sara Comai is an Associate Professor at the Politecnico di Milano , affiliated with the Department of Electronics, Information and Bioengineering . She coordinates the Assistive Technology Group (ATG) and leads the MEP (Maps for Easy Paths) project. Research Focus : Database systems, smart home technologies, indoor localization, and assistive solutions for elderly/dementia patients Recent Trends : 2025-2023 publications highlight Occupancy detection via Wi-Fi/infrared sensors Behavior classification for Alzheimer's patients Wearable/RFID-based health monitoring Human-robot collaboration in agriculture Adaptive machine learning for land use segmentation