Marino Miculan is a Professor of Computer Science at the University of Udine, leading the MADS Lab and Cybersecurity Lab. His research focuses on formal methods, cybersecurity, and distributed systems, with an emphasis on verifying security in concurrent systems. He has authored over 100 publications and contributed to 16 research projects. His research interests include formal verification of security protocols, distributed computing architectures, and the application of category theory to system modeling. He has developed frameworks like Netstaldi for network discovery and tools such as DBCChecker for analyzing container compositions. Miculan’s work bridges theoretical foundations (e.g., adhesive categories, behavioral equivalences) with practical applications in IoT security, multi-factor authentication, and cloud containerization. His labs explore cutting-edge topics like continuous learning for anomaly detection and stateful access control mechanisms.
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.
Roberto Montemanni is a Full Professor in the Department of Engineering Sciences and Methods at the University of Modena and Reggio Emilia . He is affiliated with the Operations Research Group and specializes in the MATH-06/A Operational Research disciplinary sector. His role involves teaching and research, with office hours available by appointment via email. He focuses on advancing optimization methodologies for complex logistical, transportation, and healthcare challenges. His research interests include combinatorial optimization , algorithm design , and constraint programming . Key areas of exploration involve conflict-constrained problems (e.g., assignment, knapsack, and spanning tree), probabilistic routing scenarios, and drone-assisted delivery systems. He also investigates real-world applications in home healthcare logistics, warehouse management, and supply chain optimization, often blending heuristic methods with exact algorithms. Recent publications emphasize trends in exact algorithm development for hazardous routing, drone scheduling in heterogeneous vehicle routing, and model-based approaches for time-bomb knapsack and orienteering problems. These works highlight interdisciplinary solutions for industries and healthcare systems. While no scientific awards are listed, his contributions to optimization theory and practice are substantial. His advising and grant activities likely align with his research group’s focus on operational challenges, though specific student names or grant details are not provided. He collaborates actively through the Operations Research Group , addressing issues like network-based data interpretation and intelligent manufacturing systems.
Daniele Pretolani is an Associate Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia. He specializes in Operations Research (MATH-06/A) with a focus on decision support systems, multi-objective optimization, and directed hypergraph algorithms. Teaches Methods and Algorithms for Optimization in Digital and Creative Industry (Management Engineering) Teaches Models and Methods for Decision Support (Management Engineering) His research explores: Stochastic time-dependent network routing Multi-criteria decision analysis for supplier selection Hypergraph reductions for satisfiability problems Visual decision support tools for composite indexes Recent work includes developing a decision support system for global service providers (2024) that improved supplier selection by 25% over company practices, and foundational contributions to the SDEWES sustainability index (2018-2020). No specific awards or student advising details were mentioned in the provided texts.
Rossana Mastrandrea is a Tenure Track Researcher at the Department of Management 'Valter Cantino' of the University of Turin. She specializes in mathematical methods applied to economics, actuarial sciences, and finance. Her research focuses on evolutionary game theory, environmental economics, health economics, and complex systems modeling. She coordinates projects like the PRIN 2022 initiative on pharmaceutical innovation and collaborates with institutions such as IMT School for Advanced Studies (Lucca) and Groningen University. She teaches Mathematics for Finance in the Business Economics program. Affiliations: IMT School for Advanced Studies (Guest Scholar), Laboratory for Analysis of Complex Economic Systems Research Groups: Coordinator of the 'Mathematical Methods for Complex Economic Systems' group with Prof. Franco Ruzzenenti Her work integrates network analysis, agent-based modeling, and statistical physics to study sustainability dilemmas, carbon emissions, and technological innovation. She has published extensively on topics like optimal transport in trade networks and the role of public/private sectors in mRNA vaccine development. Awards: None explicitly listed. Grants & Projects: Scientific coordinator of PRIN 2022 Project '3PBI', funded collaborations with Università Cattolica Sacro Cuore and Scuola Alti Studi IMT. Labs/Teams: Leads research at the Laboratory for Analysis of Complex Economic Systems and collaborates with interdisciplinary teams in health economics and environmental policy.
Tommaso Gili is an Assistant Professor at the IMT School for Advanced Studies Lucca, specializing in interdisciplinary research at the intersection of network science, neuroscience, and biomedical engineering. His work focuses on analyzing complex systems through network theory and advanced imaging techniques, with emphasis on brain connectivity, functional networks, and their disruptions in neurological disorders. Key research areas include: Functional and structural brain network integration Multi-scale Laplacian analysis in heterogeneous systems Applications of network theory to medicine (e.g., schizophrenia, epilepsy, inflammatory bowel disease) Development of novel methods for network coarse-graining and renormalization His research leverages fMRI, MRI, and computational modeling to study: Cerebral blood flow dynamics Neurological connectivity alterations Biomedical signal processing Complex disease biomarker discovery Notable contributions include innovations in: Functional connectivity analysis of C. elegans Topological symmetry-breaking in brain networks Machine learning approaches for brain digital twins
Miguel Ibáñez Berganza is an Assistant Professor at the IMT School for Advanced Studies Lucca. He specializes in network science and complex systems analysis, focusing on interdisciplinary applications of network theory. His research interests center on networks, including structural properties, dynamics, and computational modeling of interconnected systems. He is affiliated with the IMT School's faculty and contributes to the academic community through his work in network science.
Fabrizio Rossi is a Full Professor of Operations Research at the University of L'Aquila, Department of Information Engineering, Computer Science and Mathematics. He has been a member of the Board of Administration at the university since 2019 and previously from 2010 to 2012. His academic career spans over two decades, including roles as Associate Professor (2005–2019) and Assistant Professor (1997–2002). Education: Ph.D. in Operations Research, University of Rome 'La Sapienza' (1996) Laurea Degree in Electrical Engineering, University of Rome 'La Sapienza' (1992) Visiting Student in IEOR at Columbia University, New York (1996) Operations Research School, Scuola di Matematica Interuniversitaria (1996) Research Interests: Fabrizio Rossi specializes in large-scale optimization methodologies, including Integer Programming and Combinatorial Optimization. His work addresses complex applications in telecommunications network design, manufacturing process optimization, logistics systems, and healthcare. Recent innovations include algorithms for DNA sequence optimization and tumor classification using machine learning. He focuses on practical solutions through advanced techniques like lift-and-project operators and robust optimization frameworks, with significant contributions to scheduling and resource allocation in call centers and satellite missions. Scientific Awards: Informs Computing Society Prize (2014) (collaborative with Jim Ostrowski, Jeff Linderoth, Stefano Smriglio) Finalist, Euro Excellence in Practice Award (2006) for research on terrestrial broadcasting migration Advising and Grants: As a project leader, he coordinated major initiatives such as: PRIN 2010–2012 : Integer Programming methods for radio transmission networks 2008–2009 : Analog-to-digital broadcasting migration optimization IST SAILOR Project (2002–2005) : Satellite UMTS emulation systems He also contributed to European Space Agency's MAS Project (2004–2006) and collaborated with Italian regulatory bodies on telecommunication projects. His research portfolio includes over 50 projects since 1993, emphasizing real-world applications in manufacturing, logistics, and healthcare systems. Labs/Teams: His research is conducted within the Department's optimization groups and industry partnerships. Collaborations include ESA satellite scheduling, RAIWay frequency assignment, and healthcare institutions for treatment planning systems.
Rossana Mastrandrea is an Assistant Professor at the IMT School for Advanced Studies Lucca, affiliated with the AXES Research Unit. Her work integrates complex network analysis with interdisciplinary research in Economics, Environmental Science, and Behavioral Studies. She holds a PhD in Economics from the Sant’Anna School of Advanced Studies (Pisa) and has conducted research at the Lorenz Institute of Theoretical Physics (Leiden, Netherlands) and the INET project on Macroeconomics Networks. Her research focuses on theoretical and empirical analysis of complex networks in economic, social, and biological systems. Current projects include studying pharmaceutical innovation through patent citation networks (PRIN project on mRNA vaccines), pro-environmental behavior using evolutionary game theory, and carbon emissions via network approaches. She also explores brain connectivity dynamics and global trade networks using statistical physics and agent-based modeling. Key themes in her work include network reconstruction, motif analysis, and the application of optimal transport methods. Her recent studies address topics like cooperation dynamics in structured populations, coalition formation in litigation, and the role of information load in functional brain connectivity. Rossana coordinates the PRIN project analyzing public and private roles in pharmaceutical innovations and has contributed to high-impact studies on environmental performance metrics, global value chains, and schizophrenia-related brain network alterations.
Marek Elias is an Assistant Professor at Bocconi University in Milan, where he is affiliated with the Theory Group . Prior to joining Bocconi, he held postdoctoral positions at CWI (Amsterdam) and EPFL (Lausanne), following a PhD at TU Eindhoven under the supervision of Nikhil Bansal. Earlier, he studied at Charles University in Prague with advisor Jiří Matoušek. His research focuses on algorithms for optimization under uncertainty , bridging online algorithms , differential privacy , and machine learning-augmented systems . Key areas include Learning-Augmented Algorithms Online Metric Algorithms Combinatorial Optimization Privacy-Preserving Mechanisms The trends in his recent publications highlight advancements in online optimization with applications in machine learning and data structures , particularly in problems like the k-server system , Steiner tree approximation , and differentially private clustering . Papers often explore the interplay between algorithmic robustness and predictive modeling to enhance efficiency in uncertain environments. He has contributed to major conferences including ICML, NeurIPS, SODA, and FOCS, with a consistent emphasis on theoretical guarantees and practical applications . While specific awards or grants are not detailed in the provided texts, his work is supported through institutional affiliations and collaborative research networks. Marek actively engages students in research, particularly in Bocconi's Theory Group , encouraging thesis projects on learning-augmented algorithms and prediction-based optimization . His laboratory environment fosters interdisciplinary collaboration between theoretical computer science and machine learning communities.
Federica Baccini is an Assistant Professor at the Department of Computer, Control, and Management Engineering "Antonio Ruberti", Sapienza University of Rome. She is actively involved in the RSTLess research group, collaborating with Professors Fabrizio Silvestri and Irene Amerini. Her academic journey includes a PhD in Computer Science from the University of Pisa and a Master’s in Applied Mathematics from the University of Siena. Her educational background includes: PhD in Computer Science, University of Pisa, thesis: Analysis of Multiple Relations in Multilayer and Higher-Order Networks , supervised by Prof. Monica Bianchini and Dr. Filippo Geraci. Master’s Degree in Applied Mathematics, University of Siena, summa cum laude , thesis: Network analysis for the Integration of histone modification data to explain haematopoiesis . Visiting PhD student at Queen Mary University of London under Prof. Ginestra Bianconi. Federica's research is centered on the analysis of graph-structured data, particularly focusing on multilayer and higher-order networks. She investigates machine learning models for graphs, with emphasis on similarity network fusion , weighted simplicial complexes , and data integration across disciplines. Her work spans applications in biomedicine, environmental science, scientometrics, and AI security. She has published in high-impact journals such as Physical Review E , Journal of Informetrics , and Mathematics . The recent trend in her publications highlights a strong interdisciplinary approach, combining network science with machine learning to solve real-world problems in healthcare, scholarly communication, and climate systems. Her 2025 work on adversarial data poisoning shows engagement with AI security, while her medical applications (e.g., haemodialysis app, IgA nephropathy) demonstrate translational research impact. She has no listed scientific awards at this time. Federica is actively involved in academic advising and teaching, offering the course Fundamentals of Artificial Intelligence in the Bachelor's program in Mathematical Sciences for Artificial Intelligence. While specific grant details are not mentioned, her ongoing research in AI, network science, and interdisciplinary applications suggests active participation in funded projects. She is a member of the Theory of Deep Learning initiative. She is a key contributor to the RSTLess research group , where she collaborates on cutting-edge topics in deep learning and network analysis.
Cedrix Jurgal DONGMO FOUMTHUIM is a Researcher at the Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice. His work focuses on computational chemistry, biophysics, and molecular dynamics, with particular emphasis on polypeptide behavior, solvent effects, and protein interaction networks. Recent research includes studies on solvent quality in nonbiological oligomer folding, water transport in hydrolases, and computational tools for residue interaction network analysis. His methodological contributions extend to entropy estimation and ligand transport analysis. Key publication trends intersect chemical physics, structural biology, and bioinformatics, with collaborative work spanning protein-ligand interactions, self-assembled monolayer modifications, and hydrophobic surface dynamics.
Emanuela Merelli is a Full Professor of Computer Science at the University of Camerino. She leads the BioShape & Data Science Lab, established during her coordination of the EU-FET Project TOPDRIM, which focuses on topology-driven methods for complex systems. Her research integrates formal methods, algebraic topology, and data science to study biological systems like RNA folding and immune responses. She has held a Fulbright Fellowship at the University of Oregon (2005) and remains active in European academic initiatives. Research Interests: Interactive computation, topological field theory of data (TFTD), foundations of learning processes, cell cycle analysis in somatic/cancer cells, and complex systems modeling. Her work bridges theoretical computer science with systems biology, emphasizing topological approaches to data analysis. Awards: Fulbright Fellow (2005), Fellow Member of COST Action CA19122 EUGAIn (promoting gender balance in informatics). She contributes to the European Association for Theoretical Computer Science (EATCS) through leadership roles and educational initiatives. Professional Activities: Promotes open-access publications, organizes international conferences (e.g., ICALP 2022), and develops educational programs like the EATCS Young Researchers School on Complexity and Concurrency. Her work also extends to smart housing technologies for elderly care (Progetto SIAMADA) and seismic engineering applications. Labs & Projects: Leads BioShape Lab exploring topological methods in biology and data science. Coordinates EU-funded projects on RNA structure analysis and complex systems.
Mayukh Bagchi serves as a Research Fellow at the Department of Information Engineering and Computer Science, University of Trento, where he acts as teaching assistant for Knowledge Graphs courses led by Professor Fausto Giunchiglia across multiple graduate programs including Computer Science, Artificial Intelligence Systems, and Data Science. His research centers on Knowledge Graph engineering methodologies, Semantic Web standards, and AI-driven data structuring techniques. Work focuses on developing efficient frameworks for knowledge representation, covering graph construction, language standards, and toolchain implementation through hands-on project-based learning. Teaching activities include core instruction in Knowledge Graph Engineering and cross-departmental Knowledge Graphs courses, serving students in ICT Innovation, AI and Innovation, and Neurocognitive Architectures streams across Computer Science and Biotechnology programs.
Alfredo De Santis is a Professor and Director of the Dipartimento di Informatica at the Università degli Studi di Salerno. His research focuses on Data Security, Cryptography, Digital Forensics, and Communication Networks. He has authored numerous publications in top-tier journals and conferences, contributing to advancements in secret sharing schemes, cryptographic protocols, and privacy-preserving systems. His work bridges theoretical foundations with practical applications, including secure mobile communications, data compression, and IoT security. Key research interests include secure protocols, algorithm design, and the application of information theory to cybersecurity challenges. He has developed innovative solutions for entity authentication, distributed systems security, and privacy threats in mobile devices. His contributions span academic leadership, curriculum development in Data Security courses, and collaboration on projects like "Blockchain in Healthcare" and "Anti-Forensics Techniques". Publications highlight his expertise in hierarchical key management, secure data streaming, and cryptographic schemes. Recent works address fake news propagation modeling, privacy attacks exploiting smartphone sensors, and secure cloud storage solutions. His research emphasizes both theoretical rigor and real-world applicability across domains like healthcare, IoT, and digital forensics.
