جست‌وجوی استاد و پژوهشگر

Tooska Dargahiمشاهده پروفایل

پژوهشگر ارشد

• Security and Privacy
• Wireless Sensor Networks
• Ad-Hoc Networks
• +۳ مورد دیگر

Tooska Dargahi serves as a Postdoctoral Researcher at Netgroup Laboratory, University of Rome Tor Vergata, with dual affiliation at CNIT (Consorzio Nazionale Interuniversitario per le Telecomunicazioni). Her work bridges theoretical cryptography with practical implementations for next-generation networked systems, focusing on security architectures for resource-constrained environments including IoT devices and mobile platforms. She maintains active collaborations with international researchers across multiple institutions on cutting-edge security challenges. Her research program centers on Security and Privacy, Wireless Sensor Networks, Ad-Hoc Networks, and Attribute-Based Cryptography. She pioneers lightweight cryptographic solutions for IoT and smartphone ecosystems, develops k-anonymous frameworks for location-based services, and advances forensic methodologies for cloud storage and mobile applications. Key contributions include optimizing attribute-based encryption for constrained devices and designing novel key pre-distribution schemes for wireless sensor networks. Analysis of her publication record reveals consistent focus on applying cryptographic primitives to real-world systems. Her work spans Software-Defined Networking security, IoT device hardening, cloud/mobile forensics, and privacy-preserving location services. A distinctive trend involves translating complex cryptographic concepts into deployable solutions for infrastructure-limited scenarios, evidenced by implementations tested on Android devices and IoT platforms. Scientific recognition includes: Best Paper Award at IEEE WiMob 2017 for WLAN access control research As a core member of Netgroup Lab, Dr. Dargahi contributes to collaborative projects in network security and privacy. While the provided materials indicate extensive co-authorship with senior researchers like Mauro Conti and Giuseppe Bianchi, they contain no explicit references to student advising or principal investigator roles in grant-funded projects.

• Hardware Design for Network Devices
• Fault Tolerance
• Error Correction Codes
• +۳ مورد دیگر

Salvatore Pontarelli is a Researcher at CNIT (Italian National Inter-University Consortium for Telecommunications), affiliated with the University of Rome Tor Vergata. He holds a Master's in Electronic Engineering from the University of Bologna (2000) and a PhD in Microelectronics and Telecommunications from the University of Rome Tor Vergata (2003). His work focuses on hardware design for network devices, fault tolerance, error correction codes, and high-speed packet processing. He contributed to EU-funded projects and received a CISCO Research Award (2011) for work on Bloom filters and Ternary CAM. Research interests include hash-based structures (e.g., Bloom filters, hash tables), stateful programmable data planes, and FPGA-based network intrusion detection systems. He led the Defect and Fault Tolerance Group (DFTGroup) under Prof. Adelio Salsano and has extensive experience in collaborative projects with academic and industry partners. Publications highlight innovations in flow monitoring, data plane optimization, and efficient memory management. His work often bridges theoretical algorithms (e.g., cuckoo hashing) with practical hardware implementations, emphasizing real-world applications in networking and telecommunications.

Alessio Farcomeniمشاهده پروفایل

استاد

• Statistics
• Statistical Methodology
• Data Science
• +۵ مورد دیگر

Alessio Farcomeni is a Professor of Statistics at the University of Rome Tor Vergata, specializing in statistical methodology development. His work bridges academia and applied research across disciplines including economics, medicine, ecology, and engineering. He has authored/co-authored over 250 peer-reviewed papers and two books, with notable contributions in hidden Markov models, Bayesian analysis, and quantitative social science measurement. His research emphasizes interdisciplinary collaboration, reflected in studies on population health, economic policy evaluation, and surgical outcome optimization. Farcomeni’s research interests span statistical theory, with a focus on methodological innovations for complex data structures (e.g., longitudinal, spatial, and high-dimensional datasets). He has pioneered approaches in quantile regression, semi-Markov processes, and latent variable modeling. His applied work addresses real-world challenges such as estimating material deprivation scales, modeling cardiovascular disease risk factors, and analyzing pandemic dynamics (e.g., COVID-19 forecasting in Italy). His recent articles highlight advancements in statistical techniques for healthcare (e.g., AI-driven dermatology diagnostics, atrial fibrillation prediction models) and socio-economic analysis (e.g., macroprudential policy impacts, cross-country material deprivation comparisons). He is recognized for developing open-source statistical software packages, contributing to reproducible research practices. Farcomeni holds the distinction of being ranked among Italy’s top scientists by VIA-Academy. His work frequently integrates Bayesian methods, machine learning, and big data analytics to address pressing questions in public health, environmental science, and economic policy.

• Human Resource Management
• Organizational Behavior
• Digital Transformation in Workplaces
• +۶ مورد دیگر

Tommaso Fabbri is a Full Professor at the Department of Economics Marco Biagi, University of Modena and Reggio Emilia, Italy. He holds the position of Candidate Rettore (Rector candidate) and specializes in Human Resource Management (HRM), organizational behavior, and digital transformation in workplaces. His research focuses on the intersection of technology, work dynamics, and employee well-being, particularly in digitalized environments. He teaches courses like Data-Driven Human Resource Management, emphasizing modern HR practices in analytics-driven contexts. Key research interests include performance management systems, workplace autonomy, work-life balance, and the implications of digitalization on employee attitudes and stress. He collaborates with the Marco Biagi Foundation and has published extensively on topics such as remote work well-being, healthcare workforce resilience, and the role of HR analytics in organizational decision-making. Publications highlight empirical studies on digital workplaces, including analyses of employee attrition, stress factors, and work ability in healthcare settings. He advocates for data-driven HR strategies and explores the challenges and opportunities of hybrid work models. Projects often involve interdisciplinary collaborations, integrating organizational theory with quantitative methods to address contemporary workplace issues. His work has practical implications for organizations seeking to optimize HR practices through digital tools and promote sustainable work environments. Notable contributions include studies on the Green Transition’s impact on labor quality and the design of Total Worker Health® interventions in healthcare institutions.

Filippo Maggioliمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Geometry Processing
• Numerical Simulations
• +۳ مورد دیگر

Filippo Maggioli is a Postdoctoral Researcher at the University of Milano-Bicocca and an Adjunct Professor at Pegaso University. He completed his Ph.D. in Computer Science at Sapienza University of Rome, where he also served as a teaching assistant and postdoctoral researcher. His research focuses on interdisciplinary topics in Computer Graphics, Geometry Processing, and Numerical Simulations, with a strong emphasis on scalable algorithms and real-world applications. He has contributed to projects involving plant wilting simulation, shape correspondence, and procedural texturing. Maggioli advocates for open-source software and has published extensively in top-tier conferences such as ECCV. His teaching roles include courses on Computer Architecture and Networking at Pegaso University. Education: Ph.D. in Computer Science, Sapienza University of Rome (2019–2023) M.Sc. in Computer Science, Sapienza University of Rome (2018–2019) B.Sc. in Computer Science, Sapienza University of Rome (2014–2017) Research Interests: Maggioli’s work bridges computational geometry, numerical methods, and interdisciplinary applications. He explores topics such as functional maps for shape correspondence, physically-based simulation of natural phenomena, and GPU-optimized algorithms. His projects often integrate machine learning and procedural content generation to address challenges in 3D modeling and real-time applications. Awards & Contributions: Maggioli is a proponent of open-source software and has contributed to projects like SBML2Modelica. His research has led to impactful publications in areas like scalable geometry processing and fluid dynamics simulation.

Mir Farooq Aliمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Engineering
• Robotics and Automation
• Materials Science
• +۴ مورد دیگر

Mir Farooq Ali is a Research Fellow at the Department of Information Engineering, Università Politecnica delle Marche, Italy. He is supervised by Professor Andrea Monteriù and is part of the XXXIX cycle of the IIGA (Computer, Management, and Automation Engineering) Ph.D. program. His research focuses on biomedical instrumentation, smart home automation, and ferroelectric materials. He holds a BE in Biomedical Engineering from Hamdard University (Karachi, Pakistan) and an M.Sc. from Università Politecnica delle Marche (2020). Key research projects include developing intelligent robotic systems for home automation platforms aiding frail populations, contactless breathing monitoring systems, and human walking analysis. In 2021, he contributed to a study on ferroelectric nematic liquid crystals, resulting in a PNAS publication on electrostatic instabilities. His work integrates hardware-software design for patient care advancements. Publications span control systems, materials science, and assistive robotics. He is affiliated with the Materials Engineering Laboratory and actively involved in interdisciplinary projects. Contact: m.f.ali@pm.univpm.it .

Lucia PePaمشاهده پروفایل

پژوهشگر

• Telemedicine and Telerehabilitation
• Parkinson's Disease Rehabilitation
• Medical Technology
• +۵ مورد دیگر

Prof. Lucia PePa is a Researcher at the Faculty of Engineering of the Polytechnic University of Marche. Her work focuses on innovative technologies for neurological rehabilitation, particularly Parkinson's disease (PD), leveraging telemedicine, wearable devices, and machine learning. She specializes in developing low-cost tele-rehabilitation systems and AI-driven solutions for symptom monitoring and outcome prediction. Her research integrates interdisciplinary approaches, including cybersecurity for IoT healthcare systems, emotion recognition through physiological signals, and real-time stress detection via computer interaction patterns. She has contributed to projects like RAPIDO and PREPARE, advancing personalized rehabilitation strategies using AI-based patient stratification. Key areas include: telerehabilitation architecture design, wearable sensor data analysis, and clinical trial methodologies for PD interventions. Her publications span from gait analysis using smartphones to ethical considerations in federated learning for healthcare data.

Rocco Pietriniمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Computer Vision
• Retail Technology
• +۳ مورد دیگر

Prof. Rocco Pietrini is a Researcher at the Department of Information Engineering, Università Politecnica delle Marche. His work focuses on AI-driven systems for retail, fashion, agriculture, and digital humanities. He specializes in computer vision, machine learning, and ethical AI applications. Pietrini leads projects like OutfitAI (fashion recommendation systems), Shelf Management (retail automation), and Social4Fashion (social media trend forecasting). His research bridges technical innovation with societal impact, including ethical AI frameworks and legacy code modernization. Roles: Researcher in Information Engineering Affiliations: Dipartimento di Ingegneria dell'Informazione, UNIVPM Key research interests include AI ethics, precision agriculture, and multimodal data integration. His recent studies span smart retail systems, environmental monitoring via YOLO-based buoy detection, and Fortran-to-Python code conversion using LLMs. Pietrini collaborates on EU-funded projects like GREEN PATH (space planning) and Edge-AI for aquaculture. Publications emphasize practical AI applications, from consumer behavior analysis to sustainable water management systems. He maintains an active presence in academic communities through the IRIS repository and teaching at the School of Engineering.

• Breast Imaging
• Tomosynthesis
• Cancer Screening
• +۲ مورد دیگر

Daniela Bernardi is Associate Professor of Radiology at Humanitas University and President of the BSc in Medical Radiology Techniques program. She serves as Chief of the Clinical and Screening Breast Imaging Unit at IRCCS Humanitas Research Hospital, with extensive experience in breast cancer screening programs including leadership of the Trento Provincial Breast Screening Program (2008-2018). Education includes: MD from University of Verona (1995) Specialization in Radiology from University of Verona (1999) Research focuses on advanced breast imaging techniques, particularly digital mammography integration with tomosynthesis for population screening. Her landmark STORM studies demonstrated significant improvements in breast cancer detection rates. Honors include: First prize for best Italian breast imaging paper (2013, 2019) Professional leadership roles: Vice-president of Italian College of Breast Radiologists (2017-2018) Board member of Italian Society of Medical Radiology (2021-present) Teaching includes radiology education programs at multiple academic levels and thesis supervision.

Patrizia Scandurraمشاهده پروفایل

استاد

• Software Architecture
• Formal Methods
• Self-adaptive Systems
• +۳ مورد دیگر

Patrizia Scandurra is a Professor at the University of Bergamo, Italy. She has held significant roles including Co-chair of the Doctoral Symposium-track at ECSA 2020, Program Co-Chair for multiple conferences, and is a member of steering and organizing committees for various international software engineering events. Her research focuses on Software Architecture, Formal Methods, Self-adaptive Systems, and Cyber-Physical Systems. She has contributed to frameworks like ASMETA and RAMSES, emphasizing rigorous system design and safety assurance. Her work spans formal specification, model-based testing under uncertainty, and adaptive systems for diverse applications such as automotive systems, medical devices, and smart cities. She has published extensively on topics like self-adaptation under model uncertainty, microservices resilience, and anomaly detection in urban infrastructure. Patrizia’s contributions include conference organization across ECSA, ASE, ICSE, and SEAMS, reflecting her leadership in advancing software architecture theory and practice. Her research bridges formal methods with real-world applications, addressing challenges in autonomous systems and digital twin integration.

Abelardo Carlos Martínez Lorenzoمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Cross-lingual Semantics
• Information Extraction
• +۳ مورد دیگر

Abelardo Carlos Martínez Lorenzo is a postdoctoral researcher at Sapienza University of Rome working under Professor Roberto Navigli in the Natural Language Processing group. His research focuses on cross-lingual semantics and information extraction, with specialization in semantic parsing and language models. His educational background includes: Software Engineering degree from Universidad de Málaga, Spain Erasmus Mundus Joint Master Degree in Big Data Management and Analytics from Université Libre de Bruxelles (Belgium), Universitat Politècnica de Catalunya (Spain), and Technische Universität Berlin (Germany) Martínez Lorenzo's research centers on overcoming language barriers through advanced semantic representation frameworks. His work develops efficient multilingual parsing systems that address data scarcity in low-resource languages, leveraging transformer architectures and novel linearization techniques. Key contributions include the BabelNet Meaning Representation (BMR) formalism and optimization methods for Abstract Meaning Representation (AMR) parsing, enabling robust cross-lingual semantic analysis without language-specific constraints. His publication record (2022-2024) reveals a consistent trajectory in enhancing AMR parser efficiency and multilingual capabilities. Notable innovations include CLAP's 80% reduction in computational time, cross-lingual alignment through transformer cross-attention, and ensemble methods that maintain structural integrity while improving SMATCH scores. These works collectively advance accessible, high-performance semantic analysis across linguistic boundaries. His scientific recognition includes: Marie Skłodowska-Curie Fellowship as Early Stage Researcher in the Knograph project (Horizon 2020) Funded through the prestigious Marie Curie grant, Martínez Lorenzo conducts research within the Knograph project framework while mentoring junior researchers in the Sapienza NLP group. His work demonstrates significant grant impact through open-source releases (CLAP, LeakDistill) that democratize access to semantic analysis tools. He actively contributes to the Sapienza Natural Language Processing group led by Roberto Navigli, which specializes in knowledge-based multilingual NLP systems. The group maintains strong collaborations through European research initiatives including Horizon 2020 projects, with emphasis on creating interlingual semantic resources and scalable parsing frameworks.

• Statistical Mechanics
• Mathematical Physics
• Probability Theory
• +۴ مورد دیگر

Claudio Giberti is a Full Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Engineering Sciences and Methods. His academic roles include teaching advanced courses in Rational Mechanics, Applied Mathematics, Stochastic Methods for Simulations, and Mathematical Physics. He holds a permanent faculty position with expertise spanning theoretical physics, applied mathematics, and interdisciplinary research in healthcare analytics. Research Interests: His work focuses on statistical mechanics, non-equilibrium thermodynamics, and complex systems. Key areas include anomalous transport in low-dimensional systems, phase transitions in spin models, and applications of stochastic methods to biomedical problems such as electrocardiogram (ECG)-based mortality risk assessment. He also explores interdisciplinary topics like queueing models for logistics optimization. Publications: Over 15 recent articles (2015–2025) address topics ranging from uphill diffusion phenomena in Ising models to machine learning applications in cardiology. Notable contributions include studies on microscopic state equations for oscillator chains and annealed Ising models on random graphs, highlighting his dual expertise in theoretical physics and applied mathematics. Teaching: Courses include Rational Mechanics for Mechatronics Engineering, Stochastic Simulation Methods for Mathematics students, and Applied Mathematics for Management Engineering. He emphasizes hands-on learning with practical exercises and software tools for structural analysis. Future Work: Current projects involve refining ECG-based predictive models for cardiovascular risk and extending studies on non-equilibrium dynamics in multi-component systems. Collaborations with biomedical researchers and engineers underscore his commitment to bridging theoretical and applied research.

• Theoretical Physics
• Materials Science
• Nanotechnology
• +۵ مورد دیگر

Ivan MARRI is an Associate Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Engineering Sciences and Methods. His research focuses on theoretical physics, materials science, and computational chemistry, with a strong emphasis on nanocrystals and low-dimensional systems. He investigates the electronic and optical properties of silicon, germanium, and their alloys, exploring applications in photovoltaics and optoelectronics. His work leverages ab initio calculations and density functional theory to study quantum confinement effects, carrier dynamics, and surface chemistry. Dr. Marri teaches courses in High Performance Computing for Advanced Physical Analysis, Physics II, and Physics of Bodies, emphasizing numerical methods, parallel computing, and fundamental physics principles. His research spans topics such as carrier multiplication in silicon nanocrystals, strain effects in GeSi core-shell systems, and work function modulation in semiconductor surfaces. His publications highlight contributions to understanding nanocrystal interactions, exciton dynamics, and the role of surface functionalization in material properties. While no specific awards are listed, his extensive body of work reflects a deep engagement with theoretical and computational materials science.

• Computer Science
• Artificial Intelligence
• Distributed Systems
• +۳ مورد دیگر

Giacomo CABRI is a Full Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Physical, Computer and Mathematical Sciences. His roles include teaching advanced courses such as Distributed Algorithms, Object-Oriented Programming, and Software Development Methodologies across multiple disciplines including Computer Science, Mathematics, and Medicine. His research focuses on distributed systems, autonomous vehicles, smart cities, and digital twins, with a strong emphasis on applying AI and machine learning to real-world challenges. He actively contributes to academic communities through editorial roles and conference organization. Research interests span distributed algorithms, multi-agent systems, smart infrastructure, and socio-technical systems. His work bridges theoretical foundations with practical applications in transportation, manufacturing, and environmental monitoring. Recent publications highlight innovations in autonomous vehicle coordination, digital twin implementations, and context-aware systems. Teaching responsibilities include courses for Computer Science, Mathematics, and interdisciplinary programs such as Medicine and Dentistry. He integrates hands-on labs and project-based learning, emphasizing practical software development and algorithm design. His courses cover topics like object-oriented programming in Java, distributed systems, and big data architectures. Professional activities include organizing workshops on adaptive computing and collective intelligence, and leading projects like the FIRST initiative for virtual factories. His work addresses challenges in interoperability, scalability, and self-adaptation in complex systems. He maintains a personal website and is active in international collaborations through platforms like ORCID (0000-0002-4942-2453).

• Parallel and Distributed Programming
• Functional Programming
• High-Level Programming Models
• +۳ مورد دیگر

Professor Phil Trinder is a Professor of Computing Science at the University of Glasgow's School of Computing Science. He leads the Glasgow Parallelism Group (GPG) and is a member of the Glasgow Systems Section (GLASS). His research focuses on parallel and distributed programming models, functional programming, and scalable systems. Education: DPhil (Doctor of Philosophy) from the University of Oxford. Research Interests: Design and implementation of high-level distributed/parallel programming models, computational algebra applications, and collaboration with industry (Ericsson, Microsoft, etc.). Publications span over 78 works, emphasizing parallel algorithms, distributed systems, and functional programming frameworks like Erlang. Recent work explores tierless IoT architectures and reliability in distributed platforms. Grants/Projects: Principal Investigator on 12 major projects, including EU funding and EPSRC grants. Notable contributions to SD Erlang and SymGrid-Par frameworks. Labs/Teams: Glasgow Parallelism Group (GPG), GLASS section, and involvement in the Scottish Programming Languages Seminar (SPLS).