Werner Nutt is a Professor at the Free University of Bozen-Bolzano , Italy, affiliated with the Faculty of Computer Science since 2005. He previously held academic positions as a Reader at Heriot-Watt University (2000-2005), Visiting Professor at Hebrew University of Jerusalem, and Research Scientist at DFKI (1992-2000). Current Role: Professor, Free University of Bozen-Bolzano Past Roles: Reader (Heriot-Watt), Visiting Professor (Hebrew University), Research Scientist (DFKI) Research Interests focus on Data Management , Knowledge Representation , and Intelligent Information Extraction , with emphasis on modeling Construction Processes and ensuring Data Quality . His work bridges Semantic Web technologies with Business Process Management , notably through the COCkPiT project (2017-present) for construction process optimization. Key Contributions include foundational work on Query Completeness in databases, SPARQL Reasoning , and Semantic Diagnostics . He has published extensively in venues like ISWC , BPM , and CIKM , with an h-index of 35 on Google Scholar. His 154+ publications span topics from Probabilistic XML to Construction Process Modeling .
Alfredo Pulvirenti is a Full Professor of Computer Science at the University of Catania, Italy. He holds a joint appointment with the Department of Clinical and Experimental Medicine and the Department of Mathematics and Computer Science. Born in 1974, he earned his Laurea (1999, summa cum laude), PhD (2003), and post-doc (2004) in Computer Science from the University of Catania. His academic career includes roles as Assistant Professor (2005-2014), Associate Professor (2014-2023), and Full Professor since 2023. His research bridges Bioinformatics and Biomedicine , focusing on RNA interference (microRNAs, long non-coding RNAs) via stochastic and network-based inference methods. Key contributions include subgraph matching , motif finding , and multiple network alignment using Monte Carlo techniques for analyzing biological networks . He also applies time series analysis to seismic and infrasonic signals through collaborations with Italy's National Institute of Geophysics and Volcanology (INGV). Recent publications highlight advancements in graph algorithms (e.g., MultiGraphMatch, ArcMatch) and systems biology tools like MITHrIL and SPECifIC. He co-directs the Jacob T. Schawartz International School for Scientific Research and leads national/regional projects on Bioinformatics and Big Data in Cancer . His group includes 3 researchers, 1 post-doc, and 3 PhD students in Complex Systems. Awards and Leadership : Best Paper Award at CBS 2009 Guest Editor for BMC Bioinformatics, Briefings in Bioinformatics, Elsevier Program Committee Member for major international conferences International Collaborations span institutions like NYU, Ohio State University Wexner Medical Center, Brown University, Tel Aviv University, Toronto University, and Università di Ancona.
Stefano Scialò is an Associate Professor at the Department of Mathematical Sciences (DISMA) at the Polytechnic University of Turin. He is also a member of the Interdepartmental Center Ec-L - Energy Center Lab and serves as Academic Advisor for Mathematics for Engineering. His academic journey at Politecnico di Torino began with a Master's degree in Aerospace Engineering (2007), followed by a PhD in Mathematics for Engineering (2014), after which he progressed from postdoctoral fellow to Assistant Professor and ultimately to his current position as Associate Professor. Scialò's research focuses on advanced numerical methods with particular emphasis on Virtual Element Methods (VEM), development of discretization strategies for non-conforming meshes, and numerical approaches for coupled problems with high dimensionality gaps (3D-1D). His work addresses flow simulation in complex geometries, PDE-constrained optimization, and uncertainty quantification techniques. He has made significant contributions to the development of domain decomposition strategies based on optimization approaches, which have applications in porous media flow, fracture modeling, and biomedical simulations. Analysis of his recent publications reveals a consistent focus on extending and refining the Virtual Element Method framework, particularly for challenging applications involving complex geometries, fractures, and multi-dimensional coupling. His work demonstrates a strong integration of theoretical development with practical implementation, often targeting high-performance computing environments. The research spans multiple application domains including geoscience, biomedical engineering, and computational fluid dynamics, showcasing the versatility of his numerical approaches. Scialò actively supervises graduate students, with Matteo Trombini currently pursuing a PhD under his guidance in the Mathematical Sciences program. He contributes to multiple research projects, most notably as Scientific Director of the FREYA project (2023-2026) focused on fault reactivation modeling. His teaching portfolio is extensive, covering advanced numerical methods, scientific computing, and mathematical foundations across various engineering disciplines at both undergraduate and graduate levels. He participates in multiple research networks including the INdAM-GNCS Project (2018-2019) and aligns his work with Sustainable Development Goals related to quality education, industry innovation, and sustainable cities. His research group within DISMA focuses on Numerical Analysis and Scientific Computing, with particular expertise in 3D-1D coupled problems.
Daniele Padula serves as an Associate Professor in the Department of Biotechnology, Chemistry and Pharmacy at the University of Siena, where he teaches Applied Computational Chemistry, Organic Chemistry, and Heterocyclic Organic Chemistry for Chemistry, Biotechnology, and Pharmacy degree programs. His institutional contact details include office location (II lotto, III floor, room B_03_86) and direct communication channels through university email and phone systems. Padula's research integrates computational and organic chemistry methodologies to investigate fundamental properties of organic electronic materials. His primary focus areas include quantum-mechanical modeling of charge transport mechanisms, design of chiral photoluminescent systems with inverted singlet-triplet gaps, development of accurate force fields for molecular simulations, and computational exploration of photoswitches and organic semiconductors. This interdisciplinary work bridges theoretical chemistry with practical applications in optoelectronics and materials science. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) Advanced computational protocols for quantum-mechanically derived force fields (evident in JOYCE3.0 development), (2) Fundamental studies of chiral phenomena in organic materials for circularly polarized luminescence applications, and (3) Multiscale modeling of charge and exciton transport mechanisms in organic semiconductors. His work increasingly incorporates machine learning techniques to accelerate materials discovery while maintaining quantum chemical accuracy. Scientific awards: No awards or fellowships were documented in the provided materials. Advising and grant activities: The available documentation does not specify doctoral students, postdoctoral researchers, or externally funded research projects. His teaching portfolio indicates supervision of graduate and undergraduate theses within his computational chemistry courses. Research infrastructure: While specific laboratory facilities aren't detailed, his publication record suggests active participation in computational research groups with access to high-performance computing resources, likely through the University of Siena's computational chemistry infrastructure and potential collaborations with experimental groups for validation studies.
Dr. Mihai Teodor Lazarescu is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino , where he contributes to research and teaching activities. He is also a member of the PolitoBIOMed Lab (Biomedical Engineering Lab) and the Ambient Sensing and Processing research group. Scientific Affiliation: IEEE Member (2019-present) Editorial Roles: Guest Editor for SENSORS, ELECTRONICS, and ACM Transactions on Embedded Computing Systems His research interests focus on hardware acceleration for machine learning algorithms, particularly using FPGAs for data center and embedded applications. He works on high-level synthesis optimization flows, capacitive sensor design for indoor monitoring, and low-power embedded systems . His work intersects Internet of Things , Wireless Sensor Networks , and Machine Learning with applications in human localization, environmental monitoring, and industrial automation. Recent publications demonstrate expertise in neural network optimization , DSP resource sharing , and multi-FPGA allocation . His teaching spans Applied Electronics , Digital Electronic Design , and Embedded Systems Optimization across bachelor's and master's programs in Electronic Engineering and Computer Engineering . Patents: Noise cancellation for single-plate capacitive sensors Capacitive sensor for space change detection Projects: Scientific Manager for Horizon 2020-S2RJU project (2018)
Roberto Zunino is an Associate Professor in the Department of Mathematics at the University of Trento, specializing in blockchain technologies, formal methods, and distributed systems. His research bridges theoretical computer science with practical cryptographic applications, particularly in Bitcoin and smart contract ecosystems. His research interests focus on blockchain security , smart contract formalization , and probabilistic verification . Key areas include MEV (Maximal Extractable Value) theory, UTXO-based smart contracts, and computationally sound tokenization. His work combines rigorous mathematical modeling with real-world protocol analysis, emphasizing security guarantees through formal methods. Recent publications demonstrate a strong trend toward theoretical foundations of blockchain economics and security. His 15 most recent papers (2020-2025) analyze MEV formalization, Bitcoin contract liquidity, UTXO scalability, and smart contract language design, revealing deep integration of type theory, game theory, and cryptographic primitives. Zunino actively teaches courses including Informatics , Interactive Theorem Proving (using Lean 4), and Computer Tools for Mathematics . His educational focus emphasizes formal verification, imperative programming foundations, and mathematical logic applications in computer science.
Sonia Mazzucchi is a Full Professor in the Department of Mathematics at the University of Trento, specializing in probability theory, stochastic processes, and mathematical physics. Her research focuses on Feynman path integrals, quantum mechanics, and operator theory, with recent extensions into quantum information and photonics applications. She teaches core courses including Probability Calculus II, Mathematics and Statistics II, Quantum Information, and Stochastic Processes. Her research interests bridge abstract mathematical theory with practical quantum technologies. She investigates stochastic processes for modeling quantum systems, develops rigorous mathematical frameworks for path integrals on Lie groups, and applies probability theory to quantum random number generation and LiDAR systems. Her work combines functional analysis, measure theory, and differential equations to solve problems in quantum mechanics and information science. Recent publications (2022-2025) reveal a strong interdisciplinary trajectory merging mathematical physics with quantum engineering. Key trends include quantum random number generators using single-photon entanglement, SPAD-based LiDAR innovations for photon flux measurement, and advanced treatments of path integrals on Riemannian manifolds. Her work demonstrates consistent progression from foundational mathematical theory to quantum technology applications. No scientific awards are documented in the available information. Details regarding graduate student supervision and research grants are not specified in the source materials. Her academic activities center on teaching core mathematics courses and publishing in high-impact journals spanning mathematical physics and quantum information science.
Simone Di Marino is an Associate Professor in the Department of Mathematics at the University of Genoa. His research focuses on Optimal Transport, Mathematical Analysis, and their applications to Partial Differential Equations and Functional Analysis. He is a member of the research commission and teaches courses such as Mathematical Analysis, Calculus, and specialized topics in Mathematics and Engineering programs. His research interests include Optimal Transport Theory, Nonlinear Analysis, and the development of variational methods for problems in mathematical physics and differential geometry. He explores topics such as gradient flows, entropy regularization, and geometric inequalities on manifolds. Recent work highlights contributions to the theory of Grand-Canonical Optimal Transport, nonlinear mobilities in transport models, and the convexity properties of ground state energies in quantum systems. His articles often bridge pure analysis with applications in physics and numerical methods. Di Marino is available for student consultations on Wednesdays from 2pm to 4pm. Though no specific grants or awards are listed, his extensive publication record reflects active engagement in collaborative research.
FRANCO SCARSELLI is a Full Professor at the University of Siena, affiliated with the Department of Information Engineering and Mathematical Sciences. His primary research focuses on Graph Neural Networks (GNNs), machine learning applications in IoT, blockchain, bioinformatics, and medical imaging. He has contributed extensively to the theoretical foundations of GNNs, including their expressive power and VC dimensions. His teaching roles include courses on Advanced Machine Learning for the Master's program in Artificial Intelligence and Automation Engineering, as well as Information Systems for undergraduate Management Engineering students. He has been actively involved in curriculum development since at least 2021/2022. Research highlights include interdisciplinary work on agrifood supply chain traceability using IoT and blockchain, molecular property prediction with GNNs, and semantic analysis of diffusion models. Key collaborations involve institutions like the University of Siena and industry partners in smart logistics and healthcare. His publications span over two decades, with recent contributions emphasizing the theoretical underpinnings of GNN architectures and their applications in dynamic graphs, medical diagnostics, and industrial fault detection. He has also pioneered open-source tools like GNNKeras for graph neural network implementations.
Gianluigi Pillonetto is an Assistant Professor at the Department of Information Engineering , University of Padova , where he has been employed since 2005. His academic career focuses on system identification, stochastic systems, and nonparametric regularization techniques. Born: January 21, 1975 in Montebelluna, Italy Education: Doctoral degree (1998) and PhD (2002) in Computer Science/Engineering Research roles: Visiting scholar (2000), Visiting scientist (2002), Research Associate (2002-2005) His research spans system identification, stochastic processes, and deconvolution problems, with a particular emphasis on Bayesian methods and kernel-based regularization. He has contributed to areas like distributed Gaussian regression, sparse system identification, and nonlinear stochastic modeling in physiological systems. Recent publications (2016-2021) examine Gaussian regression techniques for distributed systems, entropy-based kernel design, and nonlinear stochastic deconvolution. These works incorporate machine learning principles into control theory, focusing on applications in wireless communications, robotics, and biomedical engineering.
Simone Di Marino is an Associate Professor at the University of Genova, specializing in mathematical analysis and optimal transport. His research focuses on optimal transport theory, partial differential equations, and functional analysis, with contributions to calculus of variations and mathematical physics. Education: PhD in Mathematics (2014) Master's/Undergraduate Degree in Mathematics (2012) Research Interests: Optimal Transport with applications to physics and biology Analysis of PDEs (e.g., Fokker-Planck, Hele-Shaw models) Functional Analysis (Sobolev/BV spaces on metric measure spaces) Geometric Analysis (manifolds, metric geometry) Publications: Recent work includes advances in Wasserstein space algorithms, multi-marginal transport, and semiclassical limits. Key contributions address particle approximation, gradient flows, and convexity properties in quantum systems. Grants/Awards: Supported by the Simons Foundation. No explicit awards listed. Professional Activities: Organized conferences like 'Calculus of Variations and Free Boundary Problems' Co-organizer of workshops on optimal transport and machine learning
Maria Domenica Di Benedetto is a full Professor of Control Theory at the University of L'Aquila, Italy, leading the Department of Electrical and Information Engineering. She holds a Dr. Ing. (summa cum laude) from Sapienza University of Rome (1976), a Docteur-Ingenieur (1981), and a Doctorat d'Etat Sciences (1987) from Université de Paris-Sud. Her academic journey includes roles as Assistant Professor at Sapienza (1983–1987), Associate Professor at Istituto Universitario Navale (1987–1990) and Sapienza (1990–1993), and Adjunct Professor at UC Berkeley (1995–2002). She has held visiting roles at MIT, University of Michigan, and others. Her research focuses on nonlinear control, hybrid systems, and networked embedded systems, with applications in automotive and air traffic control. She pioneered the DEWS Center of Excellence, addressing embedded controllers and wireless systems, and co-founded WEST Aquila S.r.L. for industrial collaboration. Key roles include IEEE Fellow (2002), editorships for major journals (e.g., IEEE Transactions on Automatic Control, International Journal of Robust and Nonlinear Control), and leadership in global research initiatives like HYCON and iFly projects. Notable contributions include fault-tolerant control of wireless networks, symbolic control for nonlinear systems, and safety-critical analysis of air traffic systems. Her work bridges theory and practice, addressing real-world challenges in control engineering and cybersecurity. Over 150 peer-reviewed publications and 10+ patents reflect her prolific output. Awards: IEEE Fellow (2002), multiple editorial roles, and leadership in international research consortia. Labs/Teams: Director of DEWS Center, Scientific Committee member of CETEMPS, and collaborator with industry partners like Volvo Technology and BlueCrest Capital.
Francesco V. Pepe is an Associate Professor in the Department of Physics at the University of Bari Aldo Moro, Italy, where he is affiliated with the Dipartimento Interateneo di Fisica. He leads the Quantum Optical Technologies Laboratory (QuOT Lab) and is the Principal Investigator of the INFN project PICS (Plenoptic Imaging with Correlations), focused on advancing correlation plenoptic microscopy. His research spans quantum imaging, quantum information, and quantum optics, with a strong emphasis on correlation-based imaging techniques and their applications in high-resolution 3D microscopy. PhD in Physics (specific institution and year not specified in text) His research interests lie at the intersection of quantum optics and imaging science, focusing on quantum imaging , correlation-based microscopy , plenoptic imaging , light-field technologies , and quantum information processing . He explores the use of intensity correlations in light to overcome classical limitations in resolution, depth of field, and imaging speed. His work also extends to quantum simulation of many-body systems (e.g., Schwinger model), bound states in the continuum , and light-matter interactions in waveguide QED platforms. The recent articles (2024–2025) reveal a strong trend toward quantum-inspired imaging techniques , particularly correlation plenoptic and hyperspectral imaging, with applications in turbulence-robust and real-time volumetric imaging. There is also a significant focus on quantum simulation of gauge theories using quantum computing platforms, dimensional reduction in field theories , and non-Markovian dynamics in quantum systems. The consistent use of correlation measurements, quantum error mitigation, and GPU-accelerated processing highlights a multidisciplinary approach combining theory, computation, and experimental design. Francesco V. Pepe has supervised Master’s students in Physics and is actively involved in collaborative research projects. While no formal scientific awards are listed in the provided text, his leadership in the INFN PICS project and extensive publication record underscore his prominence in the field. He has also contributed to advancements in quantum decay dynamics , spontaneous emission in dispersive media , and nonexponential decay phenomena , often in collaboration with leading researchers in quantum optics and condensed matter physics. He is associated with the QuOT Lab, which focuses on developing quantum optical technologies for imaging and sensing. The lab engages in both theoretical modeling and experimental implementation, particularly in correlation imaging, plenoptic microscopy, and quantum simulation. The team collaborates widely across Italy and internationally, contributing to projects in quantum 3D imaging, remote sensing, and Earth observation.
Claudio Lucchese is a Full Professor in the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. His research focuses on Machine Learning in Information Retrieval, specifically Learning to Rank, Adversarial ML, and Explainable AI. He has published over 100 articles and won awards including the 2015 ACM SIGIR Best Paper Award. He coordinates the Hospitality Innovation and e-Tourism degree program and leads the Data Mining and Information Retrieval Lab. Research Interests: Efficiency-Effectiveness Trade-offs in IR Adversarial Machine Learning Explainable AI Large-Scale Data Mining Recent Projects: Data Science for Mobility (Humco S.r.l., 2020) Flexymob (Currant s.r.l., 2022) Lucchese serves on editorial boards for ACM Transactions on Information Systems and Data Mining and Knowledge Discovery. His teaching spans Computer Science and Engineering Physics programs, emphasizing Massive Data Learning and High-Performance Computing.
Gabriele Santin is a Researcher at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics. He holds a PhD in Computational Mathematics from the University of Padua and has held postdoctoral positions at the University of Stuttgart and the Bruno Kessler Foundation. His research focuses on kernel-based approximation methods, numerical analysis, and applications in scientific computing, including partial differential equations and biomedical engineering. Research interests include kernel interpolation, greedy algorithms, convergence analysis, and data-driven modeling. He has contributed to advancing numerical techniques for solving PDEs, optimizing kernel methods, and analyzing stability in non-Lipschitz domains. His work bridges theoretical foundations with practical applications in fields like medical imaging, transportation systems, and epidemic modeling. Publications highlight contributions to kernel-based greedy algorithms, image interpolation, and surrogate modeling. He is affiliated with the Research Institute for Complexity and actively collaborates with institutions like SimTech (University of Stuttgart). His expertise spans numerical methods, machine learning, and interdisciplinary problem-solving.
