جست‌وجوی استاد و پژوهشگر

• Bioinformatics
• Computational Biology
• Pharmacogenomics
• +۵ مورد دیگر

Giuseppe Agapito is a Professor at the Department of Law, Economics and Sociology (DiGES) at the University of Camerino, where he teaches courses such as Elements of Computer Science and Data Analysis. He specializes in computational biology, bioinformatics, and health informatics, focusing on genomic data analysis, machine learning applications in healthcare, and parallel computing methodologies. His research integrates multi-omics approaches, pathway enrichment analysis, and predictive modeling for drug response and disease mechanisms. Notable contributions include tools like BioPAX-Parser and cPEA, which enhance genomic data interpretation. He actively collaborates in international studies, such as the 4CE consortium analyzing SARS-CoV-2 impacts. His work addresses challenges in privacy-aware bioinformatics, high-performance computing for genomics, and AI-driven medical diagnostics. Education details are not explicitly provided in the texts, but his academic profile reflects extensive expertise in interdisciplinary fields bridging computer science and biomedical research. He maintains an active research agenda with over 50 publications since 2018, emphasizing scalable data analysis, drug biomarker discovery, and computational methods for clinical outcomes prediction. His teaching responsibilities include IT management and data analysis modules within social science curricula, reflecting a commitment to digital literacy across disciplines. Research interests span bioinformatics tool development, genomic data preprocessing, and AI applications in healthcare, with a focus on translational research. Recent articles highlight advancements in fMRI classification using graph neural networks, privacy-preserving genomic pipelines, and edge-based deep learning for medical signal analysis. Awards and grants are not explicitly listed, but his sustained contribution to international research consortia underscores his field influence. He advises students and researchers on computational methodologies and hosts weekly office hours for academic consultations.

Beppe Liottaمشاهده پروفایل

استاد

• Network Analysis
• Graph Drawing
• Data Visualization
• +۵ مورد دیگر

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.

Umberto Villanoمشاهده پروفایل

استاد

• Cybersecurity
• Intrusion Detection Systems
• Machine Learning for Security
• +۳ مورد دیگر

Umberto Villano is a Full Professor at the Department of Engineering of the University of Sannio in Italy. His academic specialization is in the field of Information Processing Systems (ING-INF/05), where he conducts research and teaching activities focused on cybersecurity, machine learning applications for security, and network analysis. Professor Villano's research interests span multiple cutting-edge areas in computer science and security. His primary focus is on cybersecurity , particularly in the domains of intrusion detection systems, IoT security, and cloud security. He has made significant contributions to the application of machine learning techniques for security purposes, especially deep learning approaches using autoencoders for anomaly detection. Another major research stream involves fake news and misinformation analysis , where he applies topic modeling and graph-based approaches to understand information propagation patterns. His work bridges theoretical foundations with practical implementations in real-world security systems. An analysis of Professor Villano's recent publications (2023-2025) reveals a strong focus on advanced security techniques using artificial intelligence. His work demonstrates a consistent pattern of addressing contemporary security challenges through innovative machine learning approaches. The publications show particular emphasis on intrusion detection systems, with numerous papers exploring deep learning methods, especially autoencoders, for identifying network anomalies. There's also a significant thread of research on misinformation analysis, where he applies graph theory and topic modeling to understand fake news propagation. His work often bridges multiple domains, such as combining cybersecurity with IoT systems or applying AI techniques to cloud security challenges. Professor Villano has supervised numerous graduate students through their research in cybersecurity and related fields. His research has been supported by various grants focused on cybersecurity, machine learning applications, and information systems security. His work has contributed to the development of practical security tools and methodologies that address real-world security challenges in networked systems. Professor Villano leads or participates in research groups focused on cybersecurity and machine learning applications. These teams work on developing advanced security solutions, creating benchmark datasets for security research, and investigating novel approaches to information security challenges. His laboratory environment emphasizes both theoretical research and practical implementation, with projects often resulting in open-source tools and publicly available datasets that benefit the broader security research community.

Alessandro Russoمشاهده پروفایل

پژوهشگر

• Semantic Technologies
• Assistive Robotics
• Ontology Engineering
• +۳ مورد دیگر

Alessandro Russo is a researcher at the Institute of Cognitive Science and Technologies (ISTC) of the National Research Council (CNR) in Italy, where he contributes to the Semantic Technology Laboratory (STLab). Since 2015, he has been engaged in EU-funded projects like MARIO (Managing active and healthy aging with caring service robots), SMART VORTEX, and WORKPAD, focusing on knowledge-driven cognitive robot architectures, ontology engineering, and process-aware systems. He earned a PhD in Engineering in Computer Science from Sapienza Università di Roma (2010–2015), where he explored data-centric methodologies for Business Process Management. His research spans semantic technologies, assistive robotics, and healthcare informatics, with a strong emphasis on ontology-based knowledge management for geriatric assessment and reminiscence therapy. Alessandro has authored influential works in these domains, including foundational articles on social robot architectures and ontology design patterns for cognitive agents. His work often intersects with applications in emergency management, clinical workflows, and financial systems. Recent projects highlight his expertise in neurosymbolic AI, multimodal data integration, and software quality modeling. While no formal awards are listed, his involvement in high-impact EU initiatives underscores his role in advancing semantic and robotic technologies for societal benefit. He collaborates with interdisciplinary teams and contributes to tools like the Text2AMR2FRED pipeline for knowledge graph generation.

Marco Mesitiمشاهده پروفایل

دانشیار

• Data Management
• Big Data Visualization
• Data Integration
• +۶ مورد دیگر

Marco Mesiti is an Associate Professor at the Department of Computer Science, University of Milan, Italy. He holds a PhD and Laurea in Computer Science from the University of Genova. His research focuses on data management, big data visualization, and bioinformatics, particularly in biomolecular networks and IoT data. He is a member of AnacletoLAB and has contributed to developing tools like UNIPred-Web for biomolecular network analysis. Education: PhD in Computer Science, University of Genova, 2003 MSc in Computer Science, University of Genova, 1998 Laurea (BSc equivalent) in Computer Science, University of Genova (cum laude) Research Interests: His work spans XML data handling, semistructured data, and applications in bioinformatics, IoT, and credit worthiness. He explores approaches to manage heterogeneous data, including visualization techniques and knowledge graph construction. Recent projects include RNA-centered knowledge graphs and GPU-based algorithms for large networks. Publications: Recent work includes advancements in machine learning for protein design, RNA interaction analysis, and multi-omics data integration. His research emphasizes scalable methods for biomolecular networks and visual analytics tools. Grants & Collaborations: Involved in projects like ELISE and MUSE. Collaborates internationally on bioinformatics and computational biology initiatives. Labs & Teams: Member of AnacletoLAB, focusing on machine learning and data science applications.

• Computer Music
• Optical Networks
• Optimization
• +۹ مورد دیگر

Cristina Emma Margherita Rottondi is an Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino . She is a member of the Photonext Interdepartmental Center and contributes to research in telecommunications, computer music, and network optimization. Her work spans privacy-preserving protocols, smart grid communication, and low-latency audio streaming. Research Interests : Networked Music Performance, Optical Networks, Smart Grid Privacy, Machine Learning. Education : Not explicitly listed. Research Areas include: Smart Grid Privacy : Developing secure protocols for data aggregation and distributed energy optimization. Optical Network Design : Investigating machine learning-driven solutions and spatial division multiplexing. Networked Music Performance : Addressing latency and inclusivity in remote musical collaboration. Publication Trends highlight interdisciplinary work at the intersection of telecommunications , machine learning , and music technology . Recent articles focus on privacy-preserving smart grids , 5G-enabled musical IoT , and UDP packet trace datasets . Scientific Awards : 2020 Charles Kao Award Best Paper Awards at IEEE Online Greencomm (2014), DRCN (2017), and others N2Women Rising Star (2020) Advising includes PhD candidates working on networked music performance , accessible musical education , and medical wearable devices . She has contributed to national patents for inclusive audio hardware.

Giorgia Lodiمشاهده پروفایل

پژوهشگر

• Semantic Web Technologies
• Linked Open Data
• Ontology Design Patterns
• +۴ مورد دیگر

Giorgia Lodi is a Researcher at the Semantic Technology Laboratory (STLab) of the Institute of Cognitive Science and Technologies (ISTC) under the National Research Council (CNR) in Italy. She also serves as a consultant at AgID (Agenzia per l'Italia Digitale). Her work integrates Semantic Web technologies, Linked Open Data, and distributed systems to advance smart governance and community development. Research Interests: Semantic Web Technologies Ontology Design Patterns Smart Cities and Communities Linked Open Data Big Data Applications Key Contributions: Recent publications focus on knowledge graphs for water-health domains (2023-2025), semantic modeling in cultural heritage (2017-2024), and ontology design patterns for IoT and public data (2016-2017). Earlier work includes privacy solutions for cloud computing (2012-2013) and failure prediction in distributed systems (2012). Laboratory: Affiliated with STLab at CNR-ISTC, where she contributes to semantic technology research and development.

Valentina Presuttiمشاهده پروفایل

استادیار

• AI
• Semantic Web
• Linked Data
• +۱۷ مورد دیگر

Valentina Presutti is an Assistant Professor at the University of Bologna and an Associate Researcher at the Institute of Cognitive Science and Technologies (CNR). She coordinates the STLab (Semantic Technology Laboratory) and leads the EU H2020 Polifonia project (2021–2024), following previous roles in projects like MARIO , IKS , and NeOn . She is the co-founder of ontologydesignpatterns.org and the Ontology Patterns (WOP) workshop series. PhD : Computer Science, University of Bologna (2006) Key Collaborations : LIPN (University of Paris 13 and CNRS), CNRS Her research focuses on Semantic Web , Linked Data , Ontology Design , and Social Robotics , with a strong emphasis on Knowledge Engineering , Empirical Semantics , and Collaborative Content Management . Recent publications highlight trends in LOD analysis , Ontology Applications , and Human-Robot Interaction . She serves on the editorial boards of Journal of Web Semantics , Data Intelligence , JASIST , and Intelligenza Artificiale , and co-directs the International Semantic Web Research Summer School . Her work bridges semantic technologies with ICT for eHealth and cognitive computing .

Giulia Michielettoمشاهده پروفایل

استادیار

• Multi-agent Systems
• Aerial Robotics
• Control Theory
• +۴ مورد دیگر

Giulia Michieletto serves as an Assistant Professor (RTD-A) at the Department of Management and Engineering, University of Padova, where she leads research in networked control systems and multi-agent robotics. She is a core member of the SPARCS research team and holds the position of publicity and dissemination lead for the IEEE Robotics and Automation Society's Technical Committee on Multi-Robot Systems. Her academic foundation includes a Laurea Degree in Automation Engineering (2014) and a Ph.D. in Information Engineering (2018), both from the University of Padova, supplemented by a Visiting Researcher appointment at LAAS-CNRS (2016-2017) funded by the Eiffel Scholarship Program. Michieletto's research centers on multi-agent system modeling with specialized focus on aerial vehicle formations and nanosatellite coordination . She pioneers thrust-vectoring aerial platform design for enhanced environmental interaction, while advancing theoretical frameworks in bearing rigidity theory. Her work spans wireless sensor networks for environmental monitoring, multi-camera systems for cooperative tracking, and ground robotics for autonomous manipulation, addressing critical challenges in robustness, security, and distributed control. Analysis of her publication trajectory reveals deep integration of bearing rigidity theory with practical aerial robotics applications, evolving toward cybersecurity-enhanced navigation and distributed estimation for sensor networks. Recent work demonstrates increasing sophistication in handling GNSS spoofing attacks, multi-robot formation control under disturbances, and real-time surveillance systems. Her scientific recognition includes: Eiffel Scholarship Program of Excellence (2016) Finalist for IEEE RAS Italian Chapter Fabrizio Flacco Young Author Best Paper Award (2019) As an active educator, Michieletto supervises master's theses on contact-aware aerial robotics and mobile manipulation while securing research leadership as Principal Investigator for the Contact-Aware Robotics (CAR) project and co-PI for formation control research. Her grant portfolio spans national initiatives including TSTARK and INTERACT, plus EU H2020's AEROARMS, frequently as fellowship recipient. She drives innovation within the SPARCS research ecosystem, focusing on networked control solutions for complex robotic systems operating in constrained environments.

Emanuele Principiمشاهده پروفایل

دانشیار

• Electrical Engineering
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

Emanuele Principi serves as an Associate Professor in the Department of Information Engineering at the College of Engineering, Università Politecnica delle Marche (UNIVPM). His academic appointment falls under the scientific sector IIET-01/A - Elettrotecnica (Electrical Engineering). Based at Via Brecce Bianche 12 in Ancona, Italy, Professor Principi maintains regular office hours on Fridays from 15:00-18:00 via Microsoft Teams, and can be contacted at e.principi@univpm.it or by phone at 0712204626. Professor Principi's research spans electrical engineering with a strong focus on signal processing, machine learning applications, and energy systems. His work particularly emphasizes non-intrusive load monitoring (NILM), acoustic signal analysis, and renewable energy integration. He has developed innovative approaches combining deep learning with electrical engineering principles to address challenges in energy disaggregation, sound event classification, and smart grid technologies. His research bridges theoretical advancements with practical applications in sustainable energy systems and intelligent monitoring solutions. Analysis of Professor Principi's publication trends reveals a consistent research trajectory with increasing focus on machine learning applications for energy systems. His recent work (2023-2025) demonstrates growing sophistication in knowledge distillation techniques for non-intrusive load monitoring, with increasing attention to interpretability and edge computing implementations. He has also expanded his research into photovoltaic-thermal systems with thermoelectric generators, showing diversification into renewable energy conversion while maintaining his core expertise in electrical signal processing. Professor Principi has made significant contributions to the fields of non-intrusive load monitoring and acoustic signal processing through his extensive publication record. His work on knowledge distillation frameworks, weakly supervised learning approaches, and real-time signal processing systems has advanced methodologies in energy monitoring and sound event classification. Professor Principi actively advises students and researchers in electrical engineering, signal processing, and machine learning applications. His research projects likely involve collaborations with industry partners in energy technology and smart grid development, though specific grant details are not provided in the available information. He appears to lead or participate in research teams focused on developing practical implementations of his theoretical work, particularly in edge computing applications for energy monitoring. Professor Principi's laboratory or research group likely focuses on signal processing for energy systems and acoustic analysis, with equipment supporting electrical measurements, audio signal acquisition, and machine learning model development. His work suggests integration of hardware and software systems for real-world implementation of monitoring technologies.

• Fluid Dynamics
• Turbulence
• Wind Energy
• +۵ مورد دیگر

Stefania Cherubini is a Full Professor in the Department of Mechanics, Mathematics & Management at Politecnico di Bari, Italy. Her academic work focuses on fluid dynamics, turbulence, and wind energy, with a particular emphasis on stability analysis, computational fluid dynamics (CFD), and data assimilation techniques. Research areas: Fluid Dynamics, Turbulence, Wind Energy, Stability Analysis, Computational Fluid Dynamics, Data Assimilation, Aeroacoustics, Nonlinear Optimization Recent publications highlight her work on wind turbine simulations using Large Eddy Simulation (LES) and Fluid-Structure Interaction (FSI) models, dynamic mode decomposition (DMD) for wake analysis, and graph neural networks (GNNs) for CFD closure modeling. She investigates superhydrophobic surfaces, boundary-layer instabilities, and nonlinear optimization in transitional and turbulent flows. Key article trends include: (1) Wind turbine aeroacoustics and wake dynamics; (2) Machine learning integration in fluid flow simulations; (3) Nonlinear stability analysis of complex flows; (4) Superhydrophobic drag reduction mechanisms; (5) Turbulent transition modeling in channel and boundary-layer flows.

Stefano Querمشاهده پروفایل

دانشیار

• Computer Science
• Electrical Engineering
• Formal Methods
• +۷ مورد دیگر

Stefano Quer is an Associate Professor at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He holds a PhD in Electronic Engineering from the same institution and has been affiliated with DAUIN since 1996. Researcher (1996-2000) Associate Professor (2000-present) Visiting Faculty at UC Berkeley (1994-1995) Research Interests His work spans Formal Verification BDD/SAT Techniques Embedded Systems Hardware/Software Co-Verification Parallel Computing with applications in VLSI CAD, industrial IoT, and energy-efficient systems. Recent articles focus on GPU-accelerated graph algorithms, wireless sensor calibration, and AI-driven test optimization. Scientific Awards Best Paper Award, IEEE EURO-DAC'94 Academic Contributions Supervised PhD students Lorenzo Cardone and Andrea Calabrese Member of DATE, ICSOFT Technical Program Committees Topical Advisor for Sensors MDPI 60+ publications in IEEE/ACM venues

• Complex networks
• Multi-agent systems
• Nonlinear control
• +۵ مورد دیگر

Anton Proskurnikov is an Associate Professor in the Department of Electronics and Telecommunications at Politecnico di Torino, Italy. His academic appointment spans the College of Computer, Film and Mechatronics Engineering and the College of Mechanical, Aerospace and Automotive Engineering. Based in office L.240, he maintains an active research program focused on control theory with applications to complex networked systems. Dr. Proskurnikov's research centers on complex networks, multi-agent systems, nonlinear control, and social network analysis, with significant contributions to understanding opinion dynamics and consensus mechanisms. His work bridges theoretical control engineering with practical applications in social dynamics, earning recognition through prestigious awards including the O. Hugo Schuck Best Paper Award (2023). His expertise spans control theory, optimization, and network analysis with implications for sustainable infrastructure and smart cities. His publication trajectory reveals a sophisticated evolution from foundational control theory toward increasingly complex applications in social dynamics and networked systems, with recent emphasis on bounded confidence models, higher-order interactions in social dynamics, and quantum optimization for complex system design. This interdisciplinary approach connects traditional control engineering with emerging challenges in social network analysis. Dr. Proskurnikov has received multiple significant awards: O. Hugo Schuck Best Paper Award (2023) from the American Automatic Control Council EPEPS Best Paper Award (2020) from EPEPS Conference Annual Reviews in Control Paper Prize (2020) from IFAC As an academic advisor, Dr. Proskurnikov currently supervises PhD student Jinze Wu in Electrical, Electronics, and Communications Engineering. His research is supported by the National Research PRIN project "Higher-order interactions in social dynamics with application to monetary networks" (2023-2025), for which he serves as Scientific Manager. He contributes to editorial boards of leading journals including IEEE TRANSACTIONS ON AUTOMATIC CONTROL and INTERNATIONAL JOURNAL OF CONTROL, reflecting his standing in the control theory community. Dr. Proskurnikov is an active member of the Automatica research group within the Department of Electronics and Telecommunications, focusing on systems, automation, and control. His collaborative work extends internationally to institutions including TU Delft, St. Petersburg National Research University, and the University of Groningen, demonstrating his strong international research presence and interdisciplinary approach to complex networked systems.

Alfonso Capozzoliمشاهده پروفایل

استاد

• Building Automation
• Energy Management Systems
• Data Analytics
• +۴ مورد دیگر

Alfonso Capozzoli is a Full Professor at the Department of Energy (DENERG) of Politecnico di Torino, coordinating the College of Electrical and Energy Engineering. He specializes in building energy systems, data-driven energy management, and smart building technologies. His roles include membership in the SmartData@PoliTO Lab and editorial boards of journals like ENERGY AND AI and SUSTAINABLE ENERGY, GRIDS AND NETWORKS . Education: Graduated in Mechanical Engineering from the University of Naples Federico II (Italy) and earned a PhD in Engineering of Mechanical Systems. He has conducted visiting research at the University of Wollongong, Australia. Research focuses on adaptive control strategies, fault detection in HVAC systems, and energy data analytics. He leads projects on predictive energy management, smart building envelopes, and coordinated energy management in building clusters. His work bridges building physics and data science, emphasizing AI-driven solutions for energy efficiency. Teaching includes courses on building physics, energy management systems, and architectural sustainability at Politecnico di Torino. He supervises PhD students exploring topics like AI-based control systems, energy flexibility in communities, and semantic integration in building management. Key contributions include developing the CityLearn platform for grid-interactive communities and co-founding the BAEDA Lab. He has secured funding from EU projects (e.g., EU-DREAM) and national grants, focusing on data-driven solutions for energy systems. Awards include recognition for his students (e.g., Sabrina Savino’s AIDDA 2024 award) and PoliTo Quality Awards for doctoral research. His work addresses sustainable development goals related to energy (SDG 7), cities (SDG 11), and responsible consumption (SDG 12).

Ognjen Savkovicمشاهده پروفایل

استادیار

• Knowledge Graphs
• Semantic Web
• Database Management
• +۷ مورد دیگر

Ognjen Savkovic is an Assistant Professor (RTD-a) at the KRDB Research Center for Knowledge and Data, Faculty of Engineering, Free University of Bozen-Bolzano, Italy. He is based at the NOI Techpark in Bolzano and is actively involved in research on Knowledge Graphs, Semantic Web, and data quality. His work bridges formal logic and machine learning to enhance data management systems. His research interests include Knowledge Graphs, Semantic Web, Database Management, Data Quality, Artificial Intelligence, and Logic Reasoning. He focuses on schema validation (e.g., SHACL), property graph schemas (PG-Schema), and integrating machine learning with declarative languages like Datalog for industrial applications such as welding quality monitoring and cloud resource configuration. His recent publications show a strong trend in semantic technologies for industrial applications, particularly in collaboration with Bosch and Siemens. His work spans theoretical foundations of schema languages and practical implementations in smart manufacturing and cloud systems. He has published in top venues including WWW, ISWC, CIKM, and SIGMOD. Scientific Awards: No specific awards mentioned. Ognjen Savkovic advises students and collaborates on research projects, though specific students are not listed. His research involves significant grant-funded collaborations, particularly in EU-level industrial AI and semantic technology initiatives. He has contributed to projects involving Siemens and Bosch, focusing on semantic diagnostics and scalable data science solutions. He is a key member of the KRDB Research Center, contributing to a vibrant research team working on knowledge representation, reasoning, and industrial applications of semantic technologies.