جستجوی استاد راهنمای دانشگاهی

Antonio Ferramoscaمشاهده پروفایل

استاد

• Model Predictive Control
• Artificial Pancreas
• Distributed Control
• +۷ مورد دیگر

Antonio Ferramosca is a Full Professor at the University of Bergamo's Department of Management, Information, and Production Engineering. His expertise spans Model Predictive Control (MPC), Artificial Pancreas systems, distributed control, and epidemiological modeling. He holds a Ph.D. from the University of Seville, with postdoctoral research at CONICET (Argentina) and visiting positions at the University of Wisconsin-Madison and the University of Seville. Education: Bachelor's and Master's in Computer Science Engineering (University of Pavia, 2004-2006) Ph.D. in Engineering (University of Seville, 2011) Research focuses on MPC applications in biomedical systems, energy systems, and robotics. He has pioneered techniques for handling circadian variability in insulin sensitivity and optimizing social distancing in SIR epidemiological models. His work integrates AI and machine learning for predictive maintenance and material design. Key awards include the Best Conference Paper Award at the 2022 IFAC CAO conference. Over 90 publications span journals like Automatica , IEEE Transactions , and conferences like ICUAS and LARS. He teaches advanced control courses, including Model Predictive Control fundamentals at the PhD level. His research groups collaborate internationally, addressing challenges in microgrid energy management, obstacle-avoidance robotics, and personalized medical device design.

• Cognitive Science
• Artificial Intelligence in Psychology
• Virtual Reality Applications
• +۳ مورد دیگر

Chiara Lucifora is a Junior Assistant Professor (fixed-term) at the Department of Philosophy, University of Bologna. Her research focuses on Cognitive Sciences, integrating Artificial Intelligence and Virtual Reality (VR) to study human cognitive and behavioral processes in ecological environments. She is an Associate Researcher at the Institute of Cognitive Sciences and Technologies (ISTC-CNR) in Rome and collaborates with national and international teams across computer science, philosophy, and neuroscience. As Lab Manager of the XR-GRACE Laboratory, she explores immersive technologies' impact on empathy, moral reasoning, and social cohesion. Her work bridges neuroscience, ethics, and technology, addressing topics like fear conditioning, vaccine hesitancy, and AI's role in creative processes. Education & Experience: Researcher at University of Bologna since 2023 Associate Researcher at ISTC-CNR, Rome Collaborations: IMT School for Advanced Studies Lucca, University of Messina, Human-Tech Institute, IfADo Lab Research Interests: VR applications in experimental psychology Cognitive processes in ecological environments Moral psychology and decision-making AI integration in psychological practice Neuroethical challenges in autonomous systems Her recent studies investigate fear extinction learning, vaccine hesitancy predictors, and empathy enhancement via VR. Lab & Teams: Leads the XR-GRACE Laboratory, focusing on extended reality (XR) for cognitive and social research. Collaborates across disciplines to develop innovative VR paradigms for therapeutic and educational applications.

• Artificial Intelligence
• Robotics
• Sensitivity Informatics
• +۷ مورد دیگر

Professor Shohei Kato holds a primary appointment at Nagoya Institute of Technology's Graduate School of Engineering (Omohi College) and serves as a Visiting Researcher at the National Center for Geriatrics and Gerontology. His career includes prior roles as Associate Professor at Nagoya Institute of Technology (2002–2015), Lecturer at Toyota National College of Technology (1999–2002), and Research Assistant (1998–1999). He earned his Doctor of Engineering from Nagoya Institute of Technology, where he also completed undergraduate studies in Electrical and Computer Engineering. Kato's research integrates artificial intelligence, robotics, and affective computing, with applications spanning healthcare, optimization, and human-machine interaction. Key themes include kansei (sensitivity) robotics, computational intelligence, dementia screening via speech/fNIRS analysis, and multi-agent systems. His work emphasizes adaptive algorithms, emotion modeling, and real-world AI deployment. Recent publications focus on sim-to-real transfer learning, reinforcement learning for multi-agent cooperation, and non-invasive dementia diagnosis. A consistent trend involves applying particle swarm optimization and deep learning to industrial and biomedical challenges, such as shield tunneling automation and EEG-based emotion recognition. Awards & Honors: Minister of Education Commendation for Science and Technology (2015) IEEE Outstanding Paper Awards (GCCE 2017, 2018) 日本感性工学会論文賞 (2012) Multiple Workshop on Informatics (WiNF) prizes for student collaboration (2011–2020) Kato leads externally funded projects on dementia screening, affective robotics, and optimization algorithms. He directs the Kato Lab (katolab.nitech.ac.jp), collaborating with entities like Shimizu Corporation on AI-driven construction planning. His team explores human-robot symbiosis, BCI interfaces for Duchenne muscular dystrophy patients, and therapy robot design.

• Ecology
• Population Biology
• Conservation Genetics
• +۵ مورد دیگر

Stefano Leonardi is an Associate Professor at the Department of Chemical, Life and Environmental Sustainability Sciences , University of Parma , where he has been working since 1999. He obtained his PhD in Ecology from the University of Parma in 1993 with a thesis on beech population biology. Since 2016, he has served as President of the Master's Degree in Ecology and Nature Conservation . His career spans research collaborations with institutions like Oregon State University and funding from CNR and USDA.

Stefano Martellosمشاهده پروفایل

دانشیار

• Biodiversity Informatics
• Lichenology
• Taxonomy
• +۶ مورد دیگر

Stefano Martellos is Associate Professor of Systematic Botany (SSD BIO/02) at the University of Trieste's Department of Life Sciences since 2019. He holds a PhD in Biomonitoring of Environmental Change from the University of Trieste. His research spans Biodiversity Informatics , Lichenology , and Citizen Science , with particular emphasis on Digital identification systems (FRIDA software, 600+ keys) Species distribution modeling for climate change impact DNA barcoding applications Italian lichen checklists European LIFE+ projects Recent publications focus on Alpine lichen diversity ( MycoKeys 2018), climate change impacts ( Community Ecology 2018), and citizen science validation ( Plant Biosystems 2019). His team has developed ITALIC database for Italian lichens and JellyWeb for marine species. Scientific recognition includes O.P.T.I.M.A. medal (1993) Ferrari-Soave Prize (2009) Acharius medal (2014) Currently coordinates the Biological Sciences and Technologies degree program and participates in European research initiatives like KeyToNature. Collaborates with ISPRA, UNEP/MAP, and National Biodiversity Network.

• Gynaecologic Oncology
• Obstetrics
• Cancer Immunotherapy
• +۳ مورد دیگر

Alice Bergamini is a University Researcher at Vita-Salute San Raffaele University, specializing in Obstetrics and Gynaecology. She actively contributes to gynaecological oncology research and clinical trials. Position: University Researcher (fixed-term) at Vita-Salute San Raffaele University (2022–2025) Research Focus: Ovarian cancer, immunotherapy, molecular tumor boards, and rare gynaecological cancers Her recent work explores CAR T-cell therapy for colorectal cancer metastases, real-world data on PARP inhibitors, and the role of radiotherapy in granulosa cell tumors. She also investigates endometriosis-related ovarian cancers and immune cell interactions in epithelial ovarian tumors. She received the Giorgio Pardi Foundation Award '12x2012' in 2013. Her affiliations include the Multicenter Italian Trials in Ovarian Cancer (Fellow, 2019–present) and the Innovation Committee of the EOTTD (2018–present). 2025: Systemic CAR T-cell targeting of colorectal cancer liver metastases 2024: MITO39 trial on ovarian cancer maintenance therapy 2024: Real-world niraparib use in platinum-sensitive relapsed ovarian cancer 2024: Reproductive outcomes after conservative treatment of MOGCTs 2024: Molecular tumor boards for clinical pathway innovation 2023: CD4 CAR-T cell exacerbation of cytokine release syndrome 2023: Immune cell interactions in epithelial ovarian cancer 2023: Role of radiotherapy in ovarian granulosa cell tumors

Fausto FASANOمشاهده پروفایل

دانشیار

• Mobile Security
• Software Engineering
• Formal Methods
• +۵ مورد دیگر

Prof. Fausto FASANO is an Associate Professor at the University of Molise, affiliated with the Department of Bioscienze e Territorio. He specializes in cybersecurity, software engineering, and formal methods for mobile systems. His research focuses on Android security, privacy indicators, malware detection, and data anomaly analysis. He has published extensively on topics such as permission models, runtime verification, and biomedical informatics applications. His academic work spans over two decades, with contributions in software quality management, code smell analysis, and formal methods for security. Notable areas include dynamic approaches to defusing logic bombs in Android, empirical studies on privacy indicators, and the application of LLMs in medical diagnostics. Prof. FASANO's publications reflect a blend of theoretical and applied research, addressing challenges in both technical (e.g., mobile security, formal verification) and interdisciplinary domains (e.g., biomedical essential oils analysis). His work emphasizes practical tools like RPCDroid for permission context identification and ADAMS for artifact management systems. He has collaborated on projects involving traceability recovery in software systems and the integration of formal methods into security protocols. His research also touches on education, including blended learning strategies and software engineering pedagogy.

PAOLA PIERLEONIمشاهده پروفایل

پژوهشگر

• Telecommunications
• IoT (Internet of Things)
• Wearable Sensors
• +۳ مورد دیگر

Prof. Paola Pierleoni is a Researcher at the Department of Information Engineering of the University of Ancona and Marche Polytechnic (UNIVPM) . Her work focuses on IoT, telecommunications, and wearable sensor systems applied to healthcare, disaster management, and industrial automation. She leads projects in real-time data processing , embedded machine learning , and cross-protocol communication , with notable contributions to earthquake early warning systems and diabetes therapy optimization. Her research combines telecommunications engineering with health monitoring applications , including gait analysis for Parkinson’s disease, stress assessment via wearable sensors, and underwater diver monitoring. She has developed low-cost IoT solutions for infrastructure monitoring (e.g., water leak detection) and AI-driven algorithms for medical diagnostics and industrial predictive maintenance. Key projects include: Earthquake Early Warning systems using MEMS accelerometers AIoT frameworks for ambient assisted living (AAL) Bluetooth mesh network optimization for latency-sensitive applications Machine learning models for diabetes therapy personalization Her work emphasizes interdisciplinary collaboration , integrating engineering, computer science, and medical expertise. Office hours are held on Mondays 9:00–13:00 at the Department of Information Engineering, Via Brecce Bianche.

Andrea MARINمشاهده پروفایل

استاد

• Distributed Systems
• Computer Networks
• Blockchain Technology
• +۴ مورد دیگر

Andrea MARIN is a Full Professor at the Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice. He serves as the Rector's delegate for Orientation and tutoring. His research focuses on Distributed Systems, Cybersecurity, Blockchain Technology, and Performance Evaluation, with emphasis on queuing models, network security, and stochastic analysis. MARIN leads or participates in major projects such as OR²AN²G (Leader), ADAPT (NS), and CINA (PT), addressing topics like quantum key distribution networks, smart city systems, and resilient IoT environments. His work integrates theoretical models with practical applications, such as analyzing blockchain consensus mechanisms (e.g., Verifier’s Dilemma in Proof-of-Work systems) and optimizing data center performance through advanced queuing theory. MARIN’s publications span journals like Electronics and conferences such as International Conference on Artificial Intelligence and Soft Computing, reflecting contributions to both foundational research and applied technologies. Key projects include developing secure IoT systems (SAFE PLACE), enhancing radio access networks (OR²AN²G), and exploring smart-building cybersecurity. MARIN’s research also delves into biomedical case studies, such as dietary impacts on musculoskeletal health, showcasing interdisciplinary reach. Funding sources include EU Horizon 2020 (e.g., MASTER H2020), MIUR (Italian Ministry of Education), and regional initiatives like Veneto POR FESR. MARIN collaborates widely, with colleagues like Peppino Fazio and Sabina Rossi, advancing areas from software aging to distributed resource management.

Isadora ANTONIANO VILLALOBOSمشاهده پروفایل

دانشیار

• Statistics
• Probability Theory
• Bayesian Inference
• +۵ مورد دیگر

Isadora Antoniano Villalobos is an Associate Professor at the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. Her research focuses on statistical models and methods for complex data structures, particularly Bayesian inference for intractable models, extreme value theory, and risk analysis. She leads projects such as RISE (Rethinking and Innovating Statistics for Extremes) and SMARTMUS-E, addressing environmental and energy challenges. Her teaching spans courses like Applied Probability and Statistics for Empirical Research in Management across various degree programs. She collaborates internationally, speaks English, Spanish, and Italian fluently, and contributes to editorial boards in statistical journals. Research Interests : Bayesian methods, extreme value analysis, statistical modeling of spatio-temporal data, risk assessment, and applications in environmental science, hydrology, and social sciences. Her work emphasizes flexible models for complex datasets and interdisciplinary collaborations. Recent Projects : RISE: PRIN-funded project on extreme value analysis (2023-2026) SMARTMUS-E: EU Horizon Europe project on museum energy management (2023-2025) Bayesian Nonparametric Mixture Models for the Dependence of Extremes (BayMEx, 2019-2021) Teaching : Courses include Applied Probability for Computer Science , Statistics for Language Sciences , and Statistics for Empirical Research in Management . She has taught at undergraduate and graduate levels since 2019.

• Quantum Physics
• Condensed Matter Physics
• Nanotechnology
• +۶ مورد دیگر

Marco Affronte is a Full Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Physics, Informatics, and Mathematics. His research focuses on quantum technologies, molecular spin systems, and nanoscale materials. Key areas include spintronics, superconductivity, and hybrid quantum devices. He leads the Low Temperature Lab, exploring coherence in molecular spins and their integration into circuits. Recent work emphasizes coherent coupling of spins with microwave photons, quantum sensing, and nanowire-based detectors. Collaborations span superconducting qubit networks, graphene-based electronics, and molecular magnets. His studies bridge fundamental physics with applications in quantum computing and sensing. Education details are not explicitly provided, but his extensive publication record reflects advanced expertise in condensed matter physics and materials science. Research interests are detailed through projects on magnon-photon systems, spin dynamics, and nanoscale device fabrication. His lab explores quantum criticality, molecular nanomagnets, and graphene-electrode interfaces.

Paola Leccaمشاهده پروفایل

استادیار

• Graph Theory
• Dynamical Networks
• Control Theory
• +۴ مورد دیگر

Paola Lecca is an Assistant Professor at the Free University of Bozen-Bolzano's Faculty of Engineering, specializing in theoretical and applied research on graph theory, dynamical networks, control theory, and causal inference. She is a Senior Professional Member of the Association for Computing Machinery and leads projects at the Smart Data Factory laboratory, focusing on technology transfer to industry. Her research integrates mathematical methods for complex systems in biological contexts, including systems biology, biophysics, and biochemistry. She develops computational approaches for network dynamics, simulation, and analysis, aiming to understand emergent properties in multi-agent systems. Courses taught include Mathematics and Statistics for Data Science and Preparatory Mathematics. She actively contributes to conferences and editorial boards, including Frontiers in Bioinformatics and Big Data Analysis for Medical Sciences.

Matteo Reمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Bioinformatics
• +۴ مورد دیگر

Matteo Re is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on Machine Learning, Computational Biology, and Bioinformatics, with applications in genomics, drug network analysis, and semi-supervised learning. He has contributed to developing tools like RANKS for node label ranking in biological networks and HyperSMURF for predicting genetic variants. His work integrates graph-based methods, ensemble learning, and network modeling to address challenges in disease association, drug repositioning, and biomolecular data analysis. Re’s research interests include semi-supervised node classification, imbalanced data handling, and functional gene prioritization. He has collaborated on projects involving clinical phenotype prediction using patient biomolecular networks and drug therapeutic categorization. His methods emphasize scalable learning, secondary memory usage, and multi-species data fusion. Awards and grants are not explicitly mentioned, but his extensive publication record reflects strong contributions to computational biology and machine learning.

Alessio Micheliمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Alessio Micheli is a Full Professor in the Department of Computer Science at the University of Pisa. His research focuses on Machine Learning, Artificial Intelligence, and Deep Learning, with emphasis on structured data like graphs and sequences. He leads the Computational Intelligence and Machine Learning (CIML) group, which develops innovative models for graph data analysis and reservoir computing. **Research Interests:** Machine Learning, Graph Neural Networks, Deep Learning, Reservoir Computing, Bioinformatics, Cheminformatics, and applications in healthcare and robotics. His work includes pioneering contributions to neural networks for graphs and dynamic graph analysis. **Teaching:** He teaches courses on Machine Learning, Computational Neuroscience, and Artificial Intelligence at both undergraduate and graduate levels. Notable courses include 'Machine Learning Fundamentals' and 'Advanced Neural Networks.' **Key Projects:** Includes EU-funded initiatives like TAILOR (Trustworthy AI) and BrAID (AI for Cardiac Diagnosis). He coordinates national and international research groups, including the MLDM.IT working group and the IEEE Task Force on Reservoir Computing. **Grants and Labs:** Active in projects on AI for healthcare, wastewater quality, and robotics. His team collaborates with industries like FOSBER and MAIOR on applied machine learning solutions.

• Machine Learning
• Artificial Intelligence
• Human-AI Collaboration
• +۳ مورد دیگر

Burcu Sayin Günel serves as a Research Fellow at the Department of Information Engineering and Computer Science, University of Trento, where she conducts cutting-edge research at the intersection of artificial intelligence and real-world critical systems. Her work bridges theoretical machine learning with practical implementations in high-stakes environments requiring human oversight. Her research portfolio centers on: Value-aware and cost-sensitive machine learning frameworks Human-AI collaboration mechanisms for medical diagnostics Safety-critical integration of ML in cyber-physical infrastructure Hybrid intelligence systems combining human cognition with algorithmic processing Active learning optimization for resource-constrained scenarios Economic valuation of machine learning models Analysis of her 2021-2025 publications reveals a consistent trajectory toward making AI systems more reliable, economically viable, and human-compatible. Key contributions include novel methodologies for prediction rejection in critical CPS, collaborative diagnostic tools like MedSyn for physician-AI teams, and value-based active learning frameworks that optimize data acquisition costs. Her work increasingly addresses medical domain challenges while maintaining strong foundations in cyber-physical systems safety. No scientific awards, grant funding details, or student supervision activities were documented in the available materials. Similarly, no specific laboratory affiliations or team leadership roles were mentioned within the provided institutional context.