Roberto De Marchis is a Researcher at Sapienza University of Rome, affiliated with the Department of Methods and Models for Economics, Territory, and Finance. His academic career focuses on quantitative approaches to financial and mathematical problems, with teaching responsibilities in Financial Mathematics for Economics and Finance programs. Role: Ricercatore (Researcher) Department: Methods and Models for Economics, Territory, and Finance Email: roberto.demarchis@uniroma1.it Research Interests: His work spans optimization models, data quality, and knowledge management. Specific areas include numerical methods for integral equations, financial option pricing, ruin probability analysis, and fractional calculus applications. Publication Trends: He specializes in mathematical finance and applied mathematics, particularly in computational approaches for financial derivatives and risk modeling. His publications demonstrate interdisciplinary applications of mathematical theory to economic and financial systems. Teaching: Coordinates Financial Mathematics courses (channels N-Z and A-L) and co-authored a key textbook in the field.
Stefano Patri' serves as a Professor in the Department of Methods and Models for Economy, Territory and Finance at Sapienza University of Rome. He teaches foundational mathematics courses for business sciences, advanced mathematics for finance (in English for the FINASS Master's program), and computer science laboratories for the MANIMP Master's degree. His institutional affiliation is consistently maintained through Sapienza University's academic structure. Professor Patri's research centers on deterministic and stochastic optimization theory with applications in economic modeling. He specializes in mathematical programming techniques including Hamilton-Jacobi-Bellman equations and Kuhn-Tucker conditions applied to economic problems. His work spans public debt optimization, game-theoretic approaches to environmental agreements, and innovative applications in insurance mathematics and urban mobility systems. His recent publications demonstrate strong interdisciplinary connections between mathematics and economics, with notable contributions in 2023 on public debt correction mechanisms and pay-as-you-drive insurance models. The research consistently bridges theoretical mathematics with practical economic policy applications, particularly in fiscal management and strategic decision-making frameworks. Teaching activities remain central to his role, with current responsibilities including: Mathematics Foundation Course for Business Sciences Mathematics for Finance (Master's Degree FINASS in English) Laboratory of Computer Science (MANIMP Master's Degree) Mathematics for PhD School Physics Curriculum Student engagement occurs through scheduled office hours by appointment and comprehensive digital resources including video lessons and exercise materials hosted on his institutional web page.
Giovanna Guerrini is an Associate Professor in the Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS) at the University of Genoa, Italy. She is actively involved in research, teaching, and academic service, with leadership roles in major conferences such as EDBT (Executive Board Member and Treasurer), SOFSEM (Track Chair), and UMAP (Workshop Chair). Her research interests include: Data Management Large Scale and Semantic Data Management Approximate and Adaptive Query Processing Linked Data and Ontology Matching Graph Matching and Geospatial Ontologies Spatio-Temporal Data and Location Inference Computer Science Education and Computational Thinking Her recent publications show a growing focus on computer science education, particularly in using gamified and extended reality environments, Sonic Pi for teaching concurrency, and AI-driven tools for enhancing computational thinking. This reflects a shift toward pedagogical innovation and student-centered learning methodologies. She has been recognized through active participation in top-tier program committees (SIGMOD, ISWC, ICDE) and organizing key workshops and schools (EDBT School 2017, APCSE at UMAP). No formal scientific awards are listed in the provided text. She advises multiple PhD students, both current and past, and contributes to academic grants and collaborative projects, particularly in database education and data science initiatives. She is affiliated with research groups including the DaMA Research Group, Data Science and Engineering Research Program, Big Data Interest Group UniGe, and CINI Big Data Research Lab.
Emma Perracchione is an Associate Professor in the Department of Mathematical Sciences "G.L. Lagrange" (DISMA) at Politecnico di Torino, where she conducts research at the intersection of approximation theory, machine learning, and scientific computing. Her work focuses on kernel-based methods, inverse problems, and applications in space weather and solar physics. She is actively involved in teaching and research leadership, including PhD supervision and national projects. PhD in Mathematics, University of Turin (2017, cum laude) M.Sc. and B.Sc. in Mathematics, University of Turin (2013, 2011) Her research interests center on approximation theory and its applications, particularly greedy methods and two-layered kernel machines used for feature reduction in geomagnetic storm forecasting and optimal sampling for solar nanosatellites. These efforts contribute significantly to advancements in inverse problems and scientific computing . Her expertise spans machine learning , imaging , and data-driven modeling , with applications in climate action and space weather. The most recent publications highlight a strong trend in developing and analyzing variably scaled kernels , feature selection via greedy algorithms, and machine learning applications in solar and astrophysical contexts. These works integrate numerical analysis with real-world data from solar wind and imaging instruments, demonstrating a blend of theoretical rigor and practical relevance. Scientific awards received include: GNCS Young Researchers Funding (2020) GNCS Young Researchers Funding (2016) "Luciana Picco Botta" Study Award (2015) COST Short Term Scientific Mission (STSM) grant (2015) Emma Perracchione supervises PhD student Matteo Trombini in the Mathematical Sciences program (40th cycle, 2025–ongoing) and leads the PRIN-funded project GOSSIP – Greedy Optimal Sampling for Solar Inverse Problems (2025–2027). She has taught various courses including Linear Algebra and Geometry , Numerical Methods and Scientific Computing , and advanced topics on Kernels for Machine Learning in aerospace, automotive, and computer science engineering programs. She is a member of the Space Weather Italian Community (SWICo) and the National Scientific Computing Group (GNCS-INdAM) , and serves as Guest Editor for Dolomites Research Notes on Approximation . She has also participated in organizing major conferences such as DWCAA24 and GIMC-SIMAI Young.
Salvatore Orlando is a Full Professor and Director of the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. He holds academic roles including membership in the University Scientific Instrumentation Service Center (CSA) Management Committee and the Academic Senate. His research focuses on machine learning, information retrieval, and data mining, with notable contributions to learning-to-rank algorithms, decision tree ensembles, and adversarial machine learning. Key areas include efficient algorithms for large-scale systems, model interpretability, and fairness in AI. Orlando’s work bridges theoretical advancements with practical applications in database technology and cybersecurity. His recent publications emphasize optimizing ranking models, enhancing algorithm resilience, and addressing ethical considerations in AI systems. He is actively involved in organizing international conferences and serves on program committees, further contributing to the academic community.
Karl Tuyls is a Professor of Computer Science at the University of Liverpool's School of Electrical Engineering, Electronics and Computer Science, with additional research affiliation at DeepMind. His work bridges theoretical game theory and practical multi-agent reinforcement learning systems. His research focuses on cooperative multi-agent systems, where he pioneers value decomposition techniques and emergent communication protocols. Key contributions include frameworks for handling team reward structures, analyzing inequity aversion in social dilemmas, and developing differentiable game mechanics that separate potential and Hamiltonian game dynamics. Analysis of his 2017-2019 publications reveals dominant trends in cooperative multi-agent reinforcement learning, particularly value decomposition architectures and emergent communication systems. These works span artificial intelligence, game theory, and behavioral economics, with specific focus on fair reward allocation, relational inductive biases in deep learning, and open-source framework development for experimental validation. Professor Tuyls leads collaborative research between the University of Liverpool and DeepMind, directing projects on multi-agent coordination and social dilemma modeling through frameworks like OpenSpiel that enable reproducible experimentation in complex game-theoretic scenarios.
Andrea Tacchino serves as a Research Fellow within the Department of Mathematics (DIMA) at the University of Genoa, where he specializes in Numerical Analysis under the Italian scientific disciplinary sector MATH-05/A. His research centers on numerical methods and computational algorithms for solving complex mathematical problems, with applications spanning scientific computing and engineering simulations. This work focuses on developing efficient approximation techniques where analytical solutions are infeasible, contributing to advancements in computational mathematics.
Alessandro Savino is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di TORINO. He serves as an academic advisor for Bachelor’s and Master’s degree programs in Computer Engineering (Ingegneria Informatica) and contributes to PhD programs in Artificial Intelligence and Computer Engineering. Research Interests: Approximate computing, Cybersecurity (including automotive systems), Dependability, Parallel computing, Reliability analysis, and Neuromorphic architectures. Key Projects: Leads RESCHIP4EU (2024-2028), NEUROPULS (2023-2027), and commercial contracts focused on real-time OS validation and avionics design. Publications: Recent work spans hardware security (e.g., VeriSide for leakage assessment), spiking neural networks (SpikeExplorer, SpikingJET), and automotive cybersecurity (CARACAS, CAN-MM). Teaching: Instructs courses on Parallel and Distributed Computing, Hardware & Wireless Security, and System Programming across Politecnico di TORINO and Scuola IMT Alti Studi - LUCCA. Research Group: Leads the SMILIES group, focusing on resilient computer architectures and life sciences.
Cosimo Vinci is a Fixed-Term Researcher (type B) in Computer Science at the Department of Mathematics and Physics "Ennio De Giorgi" at the University of Salento in Lecce, Italy. His office is located at Ex-Collegio Fiorini, first floor, room 426, with contact number +39 0832 29 7470. Dr. Vinci's research focuses on theoretical aspects of computer science with particular emphasis on algorithm design and analysis. His work spans Algorithmic Game Theory (including congestion games and envy-free allocation problems), Approximation/Online/Randomized Algorithms (such as load balancing and influence maximization problems), Stochastic Optimization (involving adaptive optimization and online learning), and Combinatorial Optimization. His research bridges theoretical computer science with practical applications in artificial intelligence and mathematics. Best Italian Doctoral Thesis in Theoretical Computer Science - year 2019 Best Young Italian Researcher in Theoretical Computer Science - year 2023 Dr. Vinci has served on program committees for prestigious conferences including WINE 2025 and ECAI 2025. His recent work includes a paper titled "Optimal Competitive Ratio for Optimization Problems with Congestion Effects" accepted at the International Conference on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2025). He has taught various courses at multiple institutions including the University of Salento, University of Salerno, Gran Sasso Science Institute, University of L'Aquila, and Charles IV University of Prague, and has supervised doctoral students.
Aris Anagnostopoulos is a Professor at the Department of Computer, Control, and Management Engineering (Dipartimento di Ingegneria Informatica, Automatica, e Gestionale) at Sapienza University of Rome since April 2012. His academic journey includes a Marie-Curie fellowship at Sapienza University and a postdoctoral position at Yahoo! Research in Santa Clara, CA. His educational background includes: Ph.D. in Computer Science, Brown University, Providence, RI Sc.M. in Applied Mathematics, Brown University, Providence, RI Sc.M. in Computer Science, Brown University, Providence, RI Diploma in Computer Engineering and Informatics, University of Patras, Patras, Greece Professor Anagnostopoulos's research focuses on the design and analysis of algorithms with applications in data mining and data science. His work spans stochastic analysis of dynamic processes, social network modeling and mining, WWW algorithms, randomized and approximation algorithms, information retrieval, and information security. His research has evolved to address contemporary challenges in federated learning, knowledge graphs, and ethical AI considerations in recommendation systems. His recent publications demonstrate a strong trend toward addressing real-world applications of data science and machine learning, particularly in healthcare, social media analysis, and privacy-preserving techniques. His work shows increasing interdisciplinary collaboration, especially with medical researchers, while maintaining strong theoretical foundations in algorithm design. Among his notable scientific awards are: Google Focused Research Award (1 of 6 PIs), 1M USD Junior Fellow, School for Advanced Studies, Sapienza University of Rome Personal research grant, Swedish Research Foundation, 200K euro, 2011 (declined) Best Poster Award, 4th International Conference on Web Search and Data Mining (WSDM 2011) Marie Curie International Incoming Fellowship, 160K euro, 2010 Paris Kanellakis Fellowship, Brown University Runner Up, Best Paper Award, 14th International World Wide Web Conference 2005 (WWW 2005) Professor Anagnostopoulos serves as the academic responsible for mobility (RAM) for the Data Science master's program and has developed comprehensive teaching materials for data science education. He teaches courses including Social Networks and Online Markets, Algorithmic Methods of Data Mining, Data Mining, and Algorithm Design. His teaching approach emphasizes both theoretical foundations and practical applications, with extensive use of AWS and Python-based tools to prepare students for industry certification.
Alberto De Santis is an Associate Professor in the Department of Computer, Automatic and Management Engineering A. Ruberti at the University of Rome La Sapienza, Faculty of Information Engineering, Computer Science and Statistics. He has maintained this position since 1998 in the sector ING-INF04 - Automatica, following his progression from researcher positions at both the National Research Council and the university's Department of Computer and System Engineering. His educational background includes a degree in Electronic Engineering from University of Rome La Sapienza (1984, with honors) and a Specialization in Control Systems and Automatic Computing Engineering (1985-86). His academic journey included a visiting scholar position at UCLA's School of Engineering and Applied Mathematics (1990-91) and research fellowships at the Institute of Systems Analysis and Informatics. Professor De Santis teaches Fundamentals of Automatic Control for Management Engineering undergraduate programs and Modeling and Identification for Master's degree students. His research spans theoretical and applied domains with particular emphasis on filtering and control theory, signal processing, and system identification. He's also a member of Continuous Optimization research group and since 2011 has been associated with the university spin-off ACTOR SRL focused on Analytics, Control Technologies and Operations Research. His recent publications (2022-2024) demonstrate remarkable interdisciplinary reach, connecting traditional control engineering with aerospace systems, nutrition science, healthcare optimization, and sports medicine. This reflects a research trajectory that has evolved from core control theory to practical applications across diverse fields including aircraft formation, sustainable diet planning, emergency department operations, and dietary supplement usage patterns. He maintains active academic engagement through regular teaching (with documented 2024/25 course schedules), ongoing research collaborations, and participation in university spin-off initiatives. His office is located in room A204 at the university's Department of Computer, Automatic and Management Engineering.
Stefano Vigogna is an Associate Professor in the Department of Mathematics at the University of Rome Tor Vergata with significant contributions to theoretical machine learning. He is affiliated with the Rome Center on Mathematics for Modeling and Data Sciences (RoMaDS), focusing on the mathematical foundations of learning algorithms. His research expertise spans: Machine Learning Statistical Learning Theory Harmonic Analysis Professor Vigogna's publication record demonstrates deep theoretical work connecting advanced mathematics to machine learning. His research investigates the spectral properties, geometric structure, and convergence behavior of neural networks using functional analysis and harmonic analysis techniques. Notable publications include his 2022 ICML paper on multiclass learning with exponential convergence rates and numerous works exploring the mathematical properties of deep learning systems through reproducing kernel spaces. He teaches Statistica for the Master's program in Environmental Biology and Statistical Learning for the Master's program in Pure and Applied Mathematics, reflecting his dual expertise in mathematical theory and practical data science applications. Professor Vigogna maintains active collaborations with leading researchers including Lorenzo Rosasco and Ernesto De Vito, advancing our fundamental understanding of learning algorithms through rigorous mathematical analysis. His work represents an essential bridge between pure mathematics and the theoretical foundations of modern artificial intelligence.
Giulia Cereda serves as Associate Professor in the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) at the University of Florence since 2025. Her academic journey includes prior roles as Fixed-term Researcher (RTD-b, 2022-2024), Research Fellow (2021-2022), and Swiss National Science Foundation Postdoc Mobility Fellow (2019-2021) at Leiden University and University of Florence. She holds a Joint PhD in Statistics from Leiden University and University of Lausanne (2011-2016), complemented by Master's and Bachelor's degrees in Mathematics from the University of Milan. Her research spans forensic statistics with focus on rare type match problems in DNA evidence evaluation, medical statistics applied to SARS-CoV-2 pandemic modeling, and machine learning implementations for biogeographical ancestry prediction. Recent publications demonstrate methodological innovations in Bayesian approaches for forensic evidence, compartmental modeling of epidemic dynamics, and supervised learning applications in population genetics. Analysis of her 14 most recent publications (2020-2025) reveals dual research thrusts: (1) forensic statistics addressing DNA mixture interpretation and rare haplotype matching through Bayesian frameworks, and (2) epidemiological modeling of smoking dynamics and SARS-CoV-2 transmission using compartmental models with uncertainty quantification. Her work bridges theoretical statistics with practical public health and forensic applications, frequently employing machine learning for complex prediction tasks. Supported by the Swiss National Science Foundation for postdoctoral research (2019-2021), she has contributed to pandemic response through Tuscan regional modeling and school-based screening strategies. Current office hours are Thursdays 3:00-4:00 PM by appointment, with ongoing research in forensic identification systems and epidemic forecasting methodologies.
