جست‌وجوی استاد و پژوهشگر

• Control Systems
• Robotics
• Structural Health Monitoring
• +۶ مورد دیگر

Carcaterra Antonio is a Full Professor at Sapienza University of Rome, specializing in control systems, robotics, and structural engineering. His research focuses on advanced control methodologies, nonlinear dynamics, and innovative applications in mechanical engineering. Key research areas include: vibration control, autonomous robotics, metamaterials, structural health monitoring, and energy harvesting systems. Recent projects involve developing novel algorithms for damage detection in infrastructure and optimizing gait patterns for quadruped robots. His work integrates machine learning techniques with traditional engineering principles, exemplified by projects like the SUNMARE marine vehicle and the Auto-sapiens autonomous driving system. He has pioneered methods for wave propagation analysis in nonlocal materials and developed adaptive control strategies for automotive suspensions. Notable contributions include the ADACTA damage detection technique and the VFC variational feedback controller. His research bridges theoretical developments with practical applications in transportation systems, environmental monitoring, and disaster response technologies.

• Structural Health Monitoring
• Vibration Control
• Statistical Energy Analysis
• +۳ مورد دیگر

Antonio Culla is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Sapienza University of Rome. His research focuses on structural health monitoring, vibration control, and nonlinear dynamics. He specializes in applying advanced analytical techniques to mechanical systems, robotics, and biomedical engineering. His work includes developing algorithms for damage detection in infrastructure and studying collective behavior in biological systems like bird flocks. Culla also explores energy harvesting from vibrations and acoustic optimization in aerospace structures. Key projects include the SUNMARE marine vehicle and OPTYRE tire performance analysis. His methodologies combine statistical energy analysis (SEA), machine learning, and experimental validation. He has contributed to interdisciplinary fields such as biomechanics, robotics, and materials science, with a strong emphasis on real-world applications in automotive, aerospace, and civil engineering sectors.

• Computational Biology
• Protein Folding
• Genome Annotation
• +۱ مورد دیگر

Rita Casadio is a Full Professor of Biochemistry/Bioinformatics/Biophysics at the University of Bologna's Department of Pharmacy and Biotechnology. Her research focuses on computational approaches to biological problems including protein folding, genome annotation, protein interaction networks, and variant effect prediction. Dr. Casadio develops machine learning methods for predicting protein structural features, stability changes upon mutation, and functional impacts of genetic variations. Her group maintains active involvement in Critical Assessment of Genome Interpretation (CAGI) challenges, evaluating computational methods for predicting variant effects in disease contexts. Recent work includes developing frameworks for antigen discovery, comparing protein structure prediction algorithms across the human proteome, and integrating multi-omics data in agricultural genomics. Her laboratory creates bioinformatics resources including web servers for protein feature prediction and functional annotation. Dr. Casadio founded the Master's Degree in Bioinformatics at the University of Bologna and serves on editorial boards of several bioinformatics journals.

• Algorithmic Bioinformatics
• String Algorithms
• Theoretical Computer Science
• +۳ مورد دیگر

Zsuzsanna Liptak is an Associate Professor in the Department of Computer Science at the University of Verona, Italy, specializing in algorithmic bioinformatics and string algorithms within the INFO-01/A Informatics academic sector. Her research focuses on: Algorithmic solutions for biological data String and sequence algorithms Mass spectrometry data interpretation Non-alignment based sequence comparison Applications to EST genomic sequences and nanopore technologies Dr. Liptak actively contributes to research projects including 'Securing Decentralized Finance and Remote Healthcare Systems - SHIELD' (starting October 2024) and 'Novel Methodologies and Tools for Next Generation Cyber Ranges - NOMEN' (starting May 2024), working at the intersection of theoretical computer science and practical bioinformatics applications. She serves on scientific committees for major international conferences: WABI (Workshop on Algorithms in Bioinformatics) CPM (Combinatorial Pattern Matching) SPIRE (String Processing and Information Retrieval) IWOCA (International Workshop on Combinatorial Algorithms) Her teaching portfolio spans multiple programs: Computational Analysis of Genome-Scale Sequences (Medical Bioinformatics Master's) Discrete Biological Models (Bioinformatics Bachelor's) Fundamental algorithms for Bioinformatics Advanced Data Structures for Textual Data (PhD program) She conducts research within the Algorithms and Algorithmic Bioinformatics research groups at the University of Verona, with connections to the INdAM Research Unit.

• Comparative Literature
• Digital Humanities
• Literary Criticism
• +۵ مورد دیگر

Simone Rebora is an Associate Professor in the Department of Foreign Languages and Literatures at the University of Verona, specializing in Comparative Literature with a strong focus on Digital Humanities. His academic work bridges traditional literary scholarship with computational approaches, making significant contributions to how we understand literature in the digital age. Professor Rebora's research interests span Comparative Literature , Digital Humanities , Empirical Study of Literature , and Computational Methods for Literary Analysis . He investigates how digital technologies transform reading practices, literary analysis, and the preservation of textual heritage, with particular attention to the relationship between literature and prosocial behavior. His recent scholarly output demonstrates a consistent focus on digital approaches to literary studies, with publications covering computational methods, digital editions of literary works (particularly Robert Musil's writings), OCR technology for historical documents, and the impact of digital media on reading comprehension. These works reflect his interdisciplinary approach that combines literary theory with empirical research methods. Member of EREAD research group studying differences between reading on paper and on screen Participant in ELIT (Empirical Study of Literature Innovative Training Network), a Horizon 2020 Project Member of Internationale Robert Musil Gesellschaft (IRMG) Involved with DAIH (Digital Arena for Inclusive Humanities), CRIER, and SKENÈ research centers Professor Rebora actively supervises students across multiple degree programs and serves in significant institutional roles including Department Council membership, Teaching Committee participation, and as Departmental Teaching Representative. His commitment to both research excellence and educational innovation makes him a central figure in advancing Digital Humanities at the University of Verona.

• Temporal Reasoning
• Artificial Intelligence
• Temporal Constraint Networks
• +۴ مورد دیگر

Roberto Posenato is a current Associate Professor in the Department of Computer Science at the University of Verona. His academic sector is INFO-01/A - Informatics, with research classified under ERC PE6_6 (Algorithms, Distributed/Parallel Algorithms) and PE6_7 (Artificial Intelligence). Office: Ca' Vignal 2, Floor 1, Room 1.84 Research Interests Temporal Reasoning with constraint networks BPMN Systems in Medicine Temporal Constraint Modeling for Healthcare Processes Algorithm Design & Computational Complexity Data Management Systems Teaching Activities Algorithms (12 credits, 2025/2026) Temporal Reasoning (6 credits, 2025/2026) Web Applications (6 credits, multiple academic years) Database & WEB courses (6-12 credits) Software Engineering (1-6 credits) Research Groups Algorithms group (focus on algorithm design, data structures, computational complexity, machine learning) Databases and Information Systems INdAM Research Unit at University of Verona

• Bioinformatics
• Computational Biology
• Graph Algorithms for Biological Networks
• +۷ مورد دیگر

Rosalba Giugno is a Full Professor of Informatics at the University of Verona's Department of Computer Science. She serves as Principal Investigator of the InfOmics Laboratory and is the reference person for the Master in Medical Bioinformatics program. Previously, from 2017 to 2022, she directed the Infolife laboratory, which comprised 35 research groups from various Italian universities. Her research focuses on algorithmic bioinformatics and computational biology, specifically developing graph algorithms for biological networks, integrating and analyzing biomolecular data, and modeling biological systems for personalized medicine. She leads a research group consisting of 5 PhD students and multiple master's thesis students. Her work bridges theoretical computer science with practical biomedical applications, utilizing approaches from machine learning, data science, mathematics, and graph theory. Professor Giugno has authored 130 scientific publications, with 70 appearing in international journals. Her recent work shows a strong trend toward applying advanced computational methods to solve complex problems in personalized medicine, particularly in patient stratification using multi-omics data, drug repurposing, and combination therapy prediction. She has secured funding for numerous national and European research projects, with recent initiatives focusing on multi-drug resistance in rheumatoid arthritis and personalized prostate cancer evaluation. She serves as an editor for the journal Information Systems and participates in scientific committees for international conferences and schools. Her leadership extends to university governance through roles on the Computer Science Teaching Committee and Department Council. Professor Giugno teaches courses in the Master's program in Medical Bioinformatics and the PhD program in Computer Science, including 'Analisi di dati Multi-omics da single-cell' and 'Programming for bioinformatics.' She has consistently taught these courses from 2016 through 2025, demonstrating commitment to training the next generation of bioinformatics specialists. The InfOmics Laboratory under her direction develops computational methods for biomedical data analysis, with applications spanning genomics, patient classification, and therapeutic optimization. The lab maintains strong connections with both academic and industrial partners through collaborative research projects.

• Temporal Data Mining
• Biomedical Decision Support Systems
• Temporal Logics
• +۵ مورد دیگر

Pietro Sala is an Associate Professor at the University of Verona , Department of Computer Science. His research intersects Temporal Data Mining , Biomedical Decision Support Systems , and Formal Verification of temporal logic specifications. Key Research Areas: Interval Temporal Logic and Model Checking Temporal BPMN for healthcare processes Data fusion in clinical environments Algorithm design for repetitive task automation Notable Projects: Dioxin impact analysis on cardiovascular development Open-source clinical data warehousing (DW-SAN) Real-time system verification frameworks Teaching: Lecturer for Biomedical Decision Support Systems (Master's) Instructor for Data Mining and Software Engineering courses The articles highlight his work in temporal logic verification (2025), clinical process modeling (2024-2025), and data integration for NLP pipelines (2023). His publications span theoretical computer science conferences (LICS, ICALP) and healthcare informatics journals (ICHI, CBM).

• Cardiac Electrophysiology
• Inverse Problems
• Mathematical Biology
• +۳ مورد دیگر

Simone Pezzuto is an Assistant Professor in the Department of Mathematics at the University of Trento, specializing in computational cardiac electrophysiology and mathematical biology. His research integrates mathematical modeling, numerical analysis, and biomedical applications. Research focuses on inverse problems in electrocardiography, arrhythmia mechanisms, and cardiac digital twins. Recent work (2024-2025) develops novel methods for Purkinje network reconstruction, atrial fibrillation source localization, and fibrosis-based inducibility prediction. Computational approaches include physics-informed neural networks, multirate schemes, and eikonal modeling for efficient simulations. Key innovations address cardiac conduction system identification from surface ECGs, ablation strategy optimization, and anatomically-accurate atrial modeling. Methodological contributions span regularization techniques for ill-posed problems and parallel-in-time algorithms for large-scale electrophysiology simulations.

• Mathematical Physics
• Network Theory
• Kinetic Theory
• +۴ مورد دیگر

Jonathan Franceschi serves as a Visiting Professor in the Department of Mathematics at the University of Pavia, maintaining an office in room C11. His academic profile is anchored in the Mathematical Physics research group, with active contributions to interdisciplinary mathematical modeling. His research spans Mathematical Physics, Network Theory, Kinetic Theory, and Fractional Calculus, with pronounced applications in social dynamics and public health. Key focus areas include epidemic modeling, opinion polarization mechanisms, and fake news diffusion—particularly examining Italian vaccination hesitancy during the Covid-19 pandemic. His methodological toolkit integrates kinetic equations, graph theory, and optimal control frameworks for complex networked systems. Analysis of his 15 publications (2019–2025) reveals a cohesive trajectory: early work established foundations in fractional calculus and numerical methods, while recent research pivots toward data-driven social modeling. Dominant themes include consensus optimization with condensation phenomena, graphon-based opinion dynamics, and uncertainty quantification in epidemic control—all demonstrating rigorous mathematical formalism applied to real-world sociotechnical challenges. Franceschi actively supports academic community initiatives including Five o'clock TeX (offering LaTeX courses via Zoom), The break bridge (curating educational handouts), and the CompMat poster template project. His digital scholarship footprint is visible through arXiv, ResearchGate, ORCID, and Google Scholar profiles.

• Artificial Intelligence
• Biomedical Imaging
• Machine Learning
• +۴ مورد دیگر

Andrea Bizzego is an Assistant Professor in the Department of Psychology and Cognitive Science at the University of Trento. His research focuses on artificial intelligence applications in neuroscience, particularly using machine learning and signal processing techniques to analyze social interactions and neural data. His expertise spans neuroimaging methods like fNIRS, computer vision applications, and computational modeling of psychological phenomena. His research interests center on: Interpersonal neural synchrony during social interactions Motion artifact correction in fNIRS data Machine learning approaches to personality neuroscience Neurocomputational models of decision-making Cross-cultural differences in brain-to-brain coupling Bizzego's recent publications demonstrate a strong focus on developing novel neuroimaging methodologies and applying computational approaches to understand social cognition. His work frequently employs hyperscanning techniques to study dyadic interactions and develops algorithms to improve signal processing in neuroscience research.

Claudio LUPIمشاهده پروفایل

استاد

• Econometrics
• Financial Accounting
• Time Series Analysis
• +۳ مورد دیگر

Prof. Claudio LUPi is a Full Professor at the Department of Economics, University of Molise. His expertise spans econometrics, financial accounting, and time series analysis with a focus on Benford's Law applications, regional economics, and consumer behavior. He has contributed significantly to understanding financial statement quality in public entities, business cycle synchronization, and the impact of elections on governance. His research also explores innovative areas like medical data simulation for atrial fibrillation detection. Key research areas include statistical methodology (e.g., unit root testing, copula models), public sector economics (municipal governance, fiscal policies), and consumer studies (cultured meat acceptance, dietary trends). His work bridges theoretical econometrics with practical applications in policy and healthcare. Prof. LUPi has authored over 50 publications since 1992, with recent articles emphasizing Benford's Law validation, cross-country banking analysis, and spatial economic dynamics. He actively contributes to R package development for econometric testing (e.g., CADFtest) and has collaborated on national projects like Italy's GDP forecasting.

• 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.

Rosario Baroneمشاهده پروفایل

استادیار

• Statistics
• Economics
• Finance
• +۲ مورد دیگر

Rosario Barone is an Assistant Professor of Statistics at the University of Rome Tor Vergata. His research focuses on methodological development of statistical tools with applications in economics, finance, social sciences, and medicine. He specializes in Bayesian inference, Markov processes, and copula models, with recent work addressing complex dependencies and stochastic systems. His research interests include Bayesian nonparametrics, time-interaction processes, and semi-Markov models. Applications span healthcare (e.g., preterm birth analysis) and econometrics. Barone's publications highlight advancements in MCMC methods for continuous-time models and high-dimensional data analysis. He has contributed to interdisciplinary fields, merging statistical theory with practical applications in diverse domains.

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.