Filip Agneessens serves as a Teaching Fellow in the Department of Sociology and Social Research at the University of Trento, Italy, with contact details including office location at Via Verdi, 26 - 38122 Trento and email filip.agneessens@unitn.it. His academic role focuses on advancing social network methodologies within sociological and organizational contexts. His research expertise encompasses Group dynamics, Social network analysis, Organizational behavior, and Diversity management, with particular emphasis on negative ties in networks, ethnic diversity effects, and methodological innovations for workplace network studies. This interdisciplinary focus bridges sociology, psychology, and organizational science to examine how network structures shape group processes and individual outcomes. Agneessens' recent publications reveal consistent scholarly contributions in social network analysis across domains including education (e.g., bullying dynamics in schools), public administration (governance networks), and organizational behavior (workplace conflict). His work demonstrates methodological leadership through the 2022 book Analyzing Social Networks Using R and empirical studies applying longitudinal and multilevel network approaches to real-world group phenomena.
Luis Alberto Barron Cedeno is an Associate Professor at the Department of Interpretation and Translation, University of Bologna since 2022, where he previously served as Senior Assistant Professor from 2019-2022. He holds a PhD in Computer Science from Universitat Politècnica de València (2012) and has worked at Qatar Computing Research Institute (2014-2019) and TALP Research Center (ERCIM fellowship, 2012-2014). PhD: Universitat Politècnica de València (2012) MSc: AI (Universitat Politècnica de València, 2009) MSc: Computing Science (UNAM, 2007) BEng: Computing (UNAM, 2004) His research focuses on NLP applications for analyzing text qualities like originality (plagiarism detection) and intent (hate speech, propaganda). Recent work includes persuasion techniques in spam emails , misogyny detection using argumentation theory, and Spanish language varieties analysis. He has published over 100 papers with 20+ in top-tier conferences. Key awards include SemEval 2020 Best Task for propaganda detection and ACL 2020 Best Demo . He serves as Specialty Chief Editor for Frontiers in Artificial Intelligence (NLP) since 2023. His students include PhD candidates Arianna Muti (misogyny detection), Katerina Korre (hate speech), and Paolo Gajo (inceldom analysis). 2020: SemEval Best Task 2020: ACL Best Demo 2012: PhD International Mention 2009: Best MSc Thesis 2007: MSc Cum Laude 2004: BEng Cum Laude He organizes the annual CheckThat! lab at CLEF since 2018 and chaired CLEF 2022. His teaching includes NLP courses, computational linguistics, and game localization for translation students.
Paolo Ciancarini is a Professor of Computer Science at the University of Bologna since 1992. He serves as a member of the Faculty of the PhD School in Computer Science and holds leadership roles as President of the Italian Association of University Professors in Computer Science and vice-Director of CINI (National Inter-University Consortium for Informatics). Education: PhD in Informatics from the University of Pisa (1988) His research focuses on complex adaptive software architectures , semantic web technologies , and software engineering for entertainment computing . He has led projects funded by the European Commission and Italian Government, contributing to domains like social robotics and data interoperability. Recent publications analyze trends in linked open data , self-citation practices , and altmetrics for research quality assessment. His work spans both theoretical and applied aspects of computer science, including collaborations on European research initiatives. Contact: paolo.ciancarini@unibo.it
Mattia Zorzi is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. He has held academic positions since 2014, transitioning from Assistant Professor to Associate Professor in 2020. His research focuses on system identification, machine learning, and robust control, with applications in dynamic brain networks, robotic systems, and quantum information processing. Education: Ph.D. and M.S. in Information Engineering from University of Padova International Experience: Visiting Scientist at University of Cambridge (2013-2014), Research Associate at University of Liege (2013-2014) Zorzi's research integrates robust and distributed filtering, inverse dynamics learning, and nonparametric identification of Kronecker networks. His work bridges theoretical advancements in spectral estimation with practical applications in neuroscience (e.g., effective connectivity analysis) and control systems (e.g., robust Kalman filtering under uncertainty). Recent publications (2024-2023) demonstrate expertise in ARMA graphical models, kernel-based estimation, and optimal transport for Gaussian processes. He has contributed to the IEEE and IFAC communities as Associate Editor and actively participates in editorial roles for leading journals. Scientific Awards: IEEE Senior Member (2021) Member of IFAC Technical Committee TC 1.1 (2018) Associate Editor roles in Automatica, IEEE Control Systems Letters, and major conferences Grants & Collaborations: Collaborated on projects involving quantum channel estimation, free-space quantum communication, and biomedical signal processing.
Corsini Alessandro is a Full Professor at the Department of Engineering, Sapienza University of Rome, specializing in renewable energy systems, computational fluid dynamics, and turbomachinery. His research focuses on optimizing offshore wind energy systems, hydrogen storage, and sustainable energy communities. He leads projects on wind turbine aerodynamics, fluid-structure interaction, and machine learning applications in engineering. Key research areas include wake dynamics in offshore wind farms, adaptive turbine blade design, and the integration of renewable energy technologies into urban and island systems. His work addresses challenges in energy efficiency, environmental impact mitigation, and innovative solutions for sustainable power generation. Recent studies explore technology roadmaps for energy sectors, desalination in renewable energy communities, and predictive modeling of material erosion in turbines. He collaborates on experimental testing of wave energy converters and machine learning-driven analysis of energy systems. Corsini has contributed to advancements in computational fluid dynamics, including variational multiscale methods and surrogate modeling for noise prediction. His interdisciplinary approach bridges engineering, environmental science, and data-driven innovation.
Fabrizio Montecchiani is an Associate Professor at the University of Perugia's Department of Engineering. He holds a Ph.D. in Computer Engineering from the same university (2014). His research focuses on graph drawing, algorithms, computational geometry, and information visualization with applications to Big Data. He coordinates the Large-scale Data Analysis & Visualization Lab (LDAV LAB), part of the national CINI Big Data Laboratory since 2019. Education: Ph.D. in Computer Engineering, University of Perugia, 2014 Research Interests: Graph Drawing and Algorithms Computational Geometry Visual Analytics and Information Visualization Algorithm Engineering for Big Data His work bridges theoretical foundations with practical applications in network visualization, data science, and algorithm design. Grants & Awards: Principal Investigator for MIUR, PRIN 2022 grant (NextGRAAL) Recipient of the 2022 National Scientific Habilitation for full professorship 2021 Best Young Italian Researcher Award in Theoretical Computer Science 2021 Best Paper Award at GD Teaching & Leadership: Lectures on Computational Models, Software Engineering, and Big Data Co-founder of CONTATTI Srl (2017), a spin-off for tourism-focused ICT solutions Editorial roles at Journal of Graph Algorithms and Applications and Computational Geometry His contributions extend to academic leadership, including organizing major conferences like GD and EuroCG. Technology Transfer: Co-founder of Vis4 Srl (2009), working on visualization solutions for financial and marketing domains Collaborations with institutions like the Financial Intelligence Agency (San Marino) and Fabrica (Benetton Group)
Paolo Coletti is a researcher and faculty member at the Free University of Bozen-Bolzano, specifically within the Faculty of Economics and Business Administration . His academic journey spans computational fluid dynamics, computational linguistics, and computational finance. He currently teaches courses such as Big Data and Blockchain and Financial Trading and Algorithms . His research focuses on financial network analysis, data organization of Italian public companies, mass customization, and cryptocurrency technology. Coletti has contributed to the construction of a historical financial database of the Italian stock market (1973–2011), ensuring data accuracy by addressing corporate actions like dividends and mergers. His work on minimum spanning trees and correlation networks of the Italian stock market has provided insights into sectoral clustering and crisis impacts. He has also explored cross-cultural consumer behavior, including the effects of product-harm crises on brand perception. Key projects include analyzing stock market dynamics during the 2008–2011 financial crises, where petroleum and utilities sectors formed distinct clusters. His research bridges computational methods with financial and economic systems, emphasizing practical applications in portfolio diversification and risk management.
Andrea Torsello is a Full Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. His research focuses on Computer Vision, Machine Learning, and Artificial Intelligence with applications in quantum computation, graph theory, and 3D modeling. He is actively involved in teaching courses on quantum computation, artificial intelligence foundations, and computer science fundamentals at both undergraduate and graduate levels. Prof. Torsello's research interests include graph neural networks, quantum machine learning, and structural pattern recognition. His work often bridges theoretical computer science with practical applications in cultural heritage digitization and environmental monitoring. His recent articles highlight advancements in graph generation via spectral diffusion, quantum lens-based 3D shape analysis, and interpretable graph neural networks. He serves on the editorial boards of journals like Pattern Recognition and has contributed to projects funded by the EU’s H2020 program, including the VEiL initiative for visualizing engineered landscapes. His teaching spans topics such as quantum computation, advanced algorithms, and machine learning across multiple engineering programs.
Marijn Keijzer is a Research Fellow at the Institute for Advanced Study in Toulouse (IAST) and the Toulouse School of Economics (TSE) in France. His work bridges computational social science, network science, mathematical sociology, and political science, with a focus on understanding social dynamics in digital environments. Keijzer earned his PhD in Sociology (cum laude) from the University of Groningen, where he was affiliated with the Norms and Networks Cluster and the ICS. Prior to his current position, he served as a Postdoctoral researcher at the Karlsruhe Institute of Technology's Chair of Sociology and Computational Social Science. His research centers on the emergence of polarization in online social media, micro-macro transitions in opinion dynamics, and social trust. Specifically, he investigates how pro-environmental attitudes form and how to overcome barriers like false polarization, identity-based disagreement, and belief consolidation that hinder collective climate action. Keijzer employs agent-based modeling, survey methods, and experiments to study these phenomena, combining computational approaches with traditional sociological methods. Keijzer's publication record reveals a sustained focus on polarization mechanisms, with recent work examining YouTube's recommendation algorithms, noise in opinion dynamics, and the relationship between filter bubbles and polarization. His research consistently bridges theoretical modeling with empirical validation, contributing significantly to our understanding of how digital environments shape social dynamics and collective decision-making. At the Toulouse School of Economics, Keijzer teaches tutorials on Graph Analysis in the masters Econometrics and Statistics program and Data Science for Social Sciences. He previously taught at the BIGSSS Summer School in Computational Social Science in 2022 and 2023, demonstrating his commitment to training the next generation of computational social scientists.
Andrea Galassi is a Junior assistant professor (RTD-A) at the University of Bologna's Department of Computer Science and Engineering (DISI), part of the Language Technologies Lab led by Paolo Torroni. He holds a PhD in Computer Science and Engineering from the University of Bologna (2021), with a dissertation on integrating deep neural networks and symbolic knowledge. His postdoctoral research included roles at Stanford, Imperial College London, and the Humane-AI-Net European project on ethical AI. He also served as an Adjunct Professor at the University of Bologna. His research focuses on Machine Learning, Natural Language Processing (NLP), and neuro-symbolic techniques applied to argument mining, legal text analysis, and ethical AI. Notable projects include the FAIR initiative (developing scalable AI techniques), CLAUDETTE, PRIMA, and ADELE legal analytics projects, and StairwAI's horizontal matchmaking services. He is an expert in argument mining for judicial decisions and automated analysis of privacy policies. Galassi has taught over 300 hours of courses, ranging from foundational computer science to advanced NLP topics. He holds National Scientific Qualification for Associate Professor in Computer Engineering (ASN 2023-2025). His recent work emphasizes ethical AI applications, misinformation detection, and AI-driven legal systems, with publications in areas like cross-lingual legal benchmarking (LEXTREME) and subjectivity detection in news media. He leads projects on AI for social impact, including chatbots for asylum seekers and privacy-preserving dialogue systems. His lab participates in CLEF challenges on news credibility and legal argumentation analysis, demonstrating expertise in collaborative AI frameworks and real-world societal applications.
Nicoletta Noceti is an Associate Professor at the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) of the University of Genoa. Her academic roles include teaching courses such as Computer Vision, Deep Learning, and Machine Learning across undergraduate and master's programs in Robotics Engineering and Computer Science. She also contributes to innovative education initiatives like the 'Smart rogaining' model for computer science orientation. Her research focuses on interdisciplinary areas spanning computer vision, human-centered artificial intelligence, and robotics. Key themes include human motion analysis, causal discovery, and the application of AI in healthcare and social interaction. Recent work emphasizes disentangled representations for microscopy images, scene-unbiased action recognition, and understanding engagement dynamics in virtual teams of older adults. She also explores uncertainty-aware systems for head pose estimation and kinematic primitives in action similarity judgments. Her research frequently intersects with robotics, cognitive science, and biomedical engineering, addressing challenges such as causal inference in nonlinear models and embodied interaction design for robots. These efforts aim to bridge computational methods with human perceptual and motor systems, with applications in assistive technologies and social robotics.
Sandra Pieraccini is a Full Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Politecnico di Torino, where she also serves as Deputy Director of the department, Contact Person for student orientation, and Coordinator of the basic subjects for first-year engineering programs. She is a member of the University Open Access Commission and actively contributes to academic governance. Her research interests include machine learning, numerical analysis, scientific computing, uncertainty quantification, and numerical optimization . She is a key member of the research group Numerical Analysis and Scientific Computing and leads the national research project FaReX (2023–2025) on reduced-order modeling and automatic learning. Her work integrates advanced numerical methods with AI techniques, particularly in modeling discrete fracture networks and fluid dynamics. The most recent publications reflect a strong trend in combining graph-informed neural networks , explainable AI , and meshless computational methods to solve complex problems in geophysics, fluid mechanics, and data science. Her research bridges applied mathematics with real-world engineering and environmental challenges. She is an active member of the scientific community, serving on the editorial boards of Journal of Machine Learning for Modeling and Computing and GEM , and participating in steering committees of UMI groups on AI and machine learning. She has also contributed to organizing major workshops and conferences. Sandra Pieraccini teaches across multiple programs, including doctoral courses in Mathematical Sciences and Aerospace Engineering, master’s courses in Mathematical Engineering and Data Science, and bachelor’s courses such as Linear Algebra and Problem Solving Lab. She is deeply involved in curriculum development and academic leadership.
Milli Letizia is an Assistant Professor in the Department of Computer Science at the University of Pisa, Italy. She is a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group connecting the University of Pisa, CNR-ISTI, and Scuola Normale Superiore. Her work bridges theoretical and applied aspects of network science, data mining, and computational social science. Education: PhD in Computer Science, University of Pisa (2018) Master Degree in Computer Science, University of Pisa, magna cum laude (110/110 cum laude, 2013) Bachelor Degree in Mathematics, University of Pisa (2010) Her research focuses on data mining , complex networks , diffusion of innovation , quantification , and the science of success . She investigates how information, behaviors, and diseases spread across networks, using both data-driven and simulation-based approaches. Her work integrates machine learning, network modeling, and social theory to understand spreading phenomena in real-world systems. The trend in her recent publications reveals a strong emphasis on modeling diffusion processes in complex networks, with a focus on algorithmic bias, community-aware diffusion, and opinion dynamics. She has developed influential open-source tools such as NDlib and CDLIB , which are widely used in network science for simulating diffusion and detecting communities. Her work spans disciplines including computer science, public health, and social science, demonstrating interdisciplinary impact. Scientific Service and Recognition: Program Committee Chair, 3rd and 4th International Workshop on Dynamics in Networks (DyNo) at PKDD 2017 and ASONAM 2018 Program Committee Member, NetSciX 2019, DATA ANALYTICS 2017–2019, GOODTECHS 2017, DataMod 2018 Member of Local Organizing Committee, XIII AI*IA Symposium on Artificial Intelligence (2014) She has contributed to major EU projects including SoBigData++ , HumanE-AI-Net , SBD@RT , and CIMPLEX . Although no formal advising or grant leadership is explicitly mentioned, her active research output and tool development suggest significant involvement in funded research and potential mentorship. She has taught courses on data mining, big data analytics, and databases at both undergraduate and master’s levels. Milli Letizia is affiliated with the Knowledge Discovery and Data Mining Laboratory (KDDLab) , a leading interdisciplinary research group focused on big data analytics, social mining, and network science. The lab fosters collaboration between academia and research institutions, promoting innovation in data-driven societal applications.
Stefano Coniglio is an Associate Professor of Computer Science and Artificial Intelligence at the University of Bergamo (UniBg), where he joined in 2022 after earning his PhD in Information Technology from the Polytechnic University of Milan in 2011 and conducting research abroad in Germany and the United Kingdom for ten years. His research focuses on deep learning models, machine learning using neural networks, algorithm design and analysis, and mathematical optimization. With over 50 scientific publications in international journals and conference proceedings, his work spans multiple domains including energy systems, medical applications, and theoretical computer science. Coniglio has made significant contributions to graph neural networks, as evidenced by his highly cited papers on quaternion-valued Laplacians and directional hypergraph neural networks. His research trends show a strong interdisciplinary approach, with publications bridging computer science with energy systems, cardiology, and mathematical optimization. His work on S-ICD screening demonstrates practical medical applications of deep learning, while his theoretical contributions to graph neural networks have advanced the field of geometric deep learning. Since November 2023, Coniglio has been a member of the UniBg Interdepartmental Working Group on Artificial Intelligence, collaborating on the design of an interdepartmental doctoral program in AI. He serves as the unit leader for the PRIN-PNRR project HEXAGON: Highly specialized EXact Algorithms for Grid Operations at the National level since 2023. Coniglio teaches Computer Science, Artificial Intelligence, and Deep Learning courses across multiple departments at UniBg, including the Department of Economics and the Department of Letters, Philosophy, and Communication. He has collaborated with major organizations including National Grid Plc, UK MetOffice, Airbus, Babcock International, British Telecom, CESI, and CISL, demonstrating strong industry connections and practical application of his research.
Johann Gamper is a Professor and Head of the Database Systems Group at the Free University of Bozen-Bolzano. He previously served as Vice-Rector for Research (2018-2024) and Vice-Dean for Studies (2014-2018). Education: Information not provided in text. Research Focus: His core research investigates data-intensive systems with emphasis on temporal data management, time series analysis, and efficient query processing. His team combines fundamental research with system development for applications in governance, tourism, healthcare, and agriculture. Current projects include temporal RDBMS extensions, time series motif mining, and multidimensional range count approximation. Publication Trends: Recent work addresses temporal query optimization, time series classification, and generative approaches for e-commerce data. His research demonstrates consistent innovation in temporal data handling and database performance optimization. Teaching: Currently teaches Database Management Systems and advises PhD students in temporal database research.
