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Eric MedvetView profile

Associate Professor

• Evolutionary Computation
• Machine Learning
• Grammatical Evolution
• +3 more

Eric Medvet is an Associate Professor of Computer Engineering at the Department of Engineering and Architecture (DIA), University of Trieste, Italy. He leads the Evolutionary Robotics and Artificial Life Lab and co-leads the Machine Learning Lab. His research focuses on Evolutionary Computation, Machine Learning, and their applications to robotics, including Grammatical Evolution, Genetic Programming, and soft robotics. He received the Google Faculty Research Award 2019 for a project on modular soft robots and co-authored a best paper at EuroGP 2024. His recent work emphasizes interpretable control policies, quality-diversity optimization, and evolutionary learning of formal specifications. Medvet teaches advanced courses on machine learning, evolutionary robotics, and programming, consistently updating curricula to reflect cutting-edge research. His labs are hubs for interdisciplinary projects, blending theory with practical implementations in robotics and AI. Research Interests: Medvet’s research spans evolutionary algorithms applied to robotics, with a focus on modular systems. He explores how genetic programming and grammatical evolution can optimize robotic designs and controllers. His work on ‘totipotent neural controllers’ and ‘body-brain co-evolution’ exemplifies efforts to create adaptable, specialized robots. He also investigates formal methods (e.g., STL specifications) for system validation and interpretable AI models. Recent trends in his publications reflect a shift toward practical applications, such as wearable movement analysis and sim-to-real transfer in robotics. Awards: Google Faculty Research Award 2019, EuroGP 2024 Best Paper Award Labs: Evolutionary Robotics and Artificial Life Lab, Machine Learning Lab Teaching: Courses include Advanced Programming, Introduction to Machine Learning, and Evolutionary Robotics, with a focus on Java and practical software development. Medvet’s approach integrates theoretical rigor with hands-on experimentation, emphasizing open-source tools like JGEA and 2D-VSR-Sim. His work bridges computational intelligence and real-world robotics challenges, driving innovation in both fields.

Flavio CorradiniView profile

Professor

• Blockchain
• Process Mining
• Business Process Modeling
• +5 more

Flavio Corradini is a Full Professor at the Department of Computer Science, University of Camerino. His research focuses on blockchain, process mining, business process modeling, IoT systems, and digital twins. He has led projects like the SAFE platform for disaster response and ChorChain for blockchain-based process execution. His work bridges formal methods with practical applications in robotics, smart contracts, and environmental monitoring. Education details are not explicitly provided in the text, but his academic contributions span over a decade of publications in top journals and conferences. His research often combines theoretical rigor with real-world implementation, such as developing tools like BPMN Inspector and the μG graph neural network library. Research interests include: blockchain interoperability, process choreography, IoT system engineering, and formal verification of distributed systems. His work on digital twins integrates sensor data with simulation models for industrial and environmental applications. Recent articles highlight advancements in robotic process mining, smart contract analysis, and multimodal data clustering. He has contributed to frameworks like Tangramob for urban mobility simulation and FloWare for IoT application development.

Emanuela MerelliView profile

Professor

• Interactive Computation
• Topological Data Analysis
• RNA Structure Analysis
• +3 more

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.

Lea PetrellaView profile

Professor

• Quantile regression
• Graphical models
• Random Forest
• +3 more

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it

Roberto MarmoView profile

Professor

• Computer Vision
• Artificial Intelligence
• Social Media Mining
• +4 more

Roberto Marmo is a Professor of Computer Science at the University of Pavia since the 2022/23 academic year. His research spans multiple domains including Artificial Intelligence , Computer Vision , and Social Media Mining . He earned his PhD in Information Engineering at Pavia's Computer Vision and Multimedia Lab after completing a Computer Science degree at the University of Salerno. Core Expertise: Data Science, AI Algorithms, Scientific Visualization Industry Applications: Anti-fraud Systems, Tourism Analytics, Transportation Sign Recognition His academic work demonstrates consistent interdisciplinary innovation: 2020: Encyclopedia contributions on Cybersecurity and Social Engineering 2017: Market analysis through social media mining 2010: Social network architecture research Key collaborations include: IBM's Web 2.0 Security Encyclopedia Italian Tourist Railways Association (application of IT to heritage tourism) Academic-Industry Partnerships in transportation sign recognition His technical contributions include: Neural network applications in geological analysis Operational risk visualization frameworks SOS gesture recognition systems e-learning performance dashboards Available for consulting and training services in AI, data analysis, and scientific visualization.

• Bioinformatics
• Computational Biology
• Network Analysis
• +4 more

Simona Ester Rombo is a Full Professor in the Department of Mathematics and Computer Science at Università degli Studi di Palermo (UNIPA), Italy. Her research spans computational biology, bioinformatics, network analysis, and big data approaches applied to biological and social systems. Her primary research interests include: Bioinformatics and computational genomics, particularly focusing on lncRNA-disease associations and RNA editing Network analysis and graph algorithms for biological and social networks Development of software tools for large-scale data analysis in biology Machine learning applications in drug discovery and repurposing Big data approaches for genomic sequence analysis and social network analysis Professor Rombo's publications demonstrate a strong focus on developing computational methods for biological network analysis, with particular emphasis on protein-protein interaction networks, molecular interaction networks, and lncRNA-disease association prediction. Her work bridges computer science and biology, creating practical tools and algorithms that address real biological challenges. Her scientific contributions include: Development of DIAMIN, a software library for distributed analysis of molecular interaction networks Creation of FEDRO, a tool for discovering candidate ORFs in plants with RNA editing Research on topological properties of biological networks and their functional implications Applications of network analysis to drug discovery and repurposing Professor Rombo actively teaches courses related to big data management and software engineering, mentoring students in computer science and artificial intelligence programs. Her office is located at Via Archirafi 34, Floor 2, Room 220 at UNIPA, with office hours on Mondays from 9:30 to 13:30.

• Global Optimization
• Operations Research
• Computational Mathematics
• +3 more

Marco Locatelli is a Full Professor in the Department of Computer Engineering at the University of Parma, Italy. His research focuses on Global Optimization , Operations Research , and Computational Mathematics , with applications in Robotics and Optimization Algorithms . Education: Ph.D. in Computational Mathematics and Operations Research (University of Milano/Napoli, 1997); Laurea in Computer Science (University of Milano, 1992). Positions: Post-Doc (University of Trier, Firenze); Assistant/Associate Professor (University of Torino); Full Professor (University of Parma since 2010). His research spans Global Optimization over intervals, multistart algorithms, concave optimization, packing problems, and convex underestimators. Recent work includes Multi-Agent Pathfinding and Speed Planning with energy/time optimization, leveraging Machine Learning and Dynamic Programming . He has received distinguished accolades, including the Europt Fellow (2018), Best Paper in the Journal of Global Optimization (2016), and co-authored a SIAM-published book on Global Optimization (2013). He serves on editorial boards of top-tier journals like Computational Optimization and Applications and Operations Research Letters . Scientific Awards: Al Zimmermann's Programming Contest Winner (Circle Packing) AIRO Award for Daniele Depetrini's Master Thesis (2007) Chairman's Recognition of Outstanding Paper (2009 IEEE Congress) Europt Fellow (2018) His teaching includes Operations Research and Algorithms for Decision Support across multiple Italian universities (Torino, Parma, Firenze). He has contributed to Research Projects like MoSPRAS, MOST, and COSO, addressing real-world optimization challenges in healthcare, robotics, and transportation.

Giovanna GuerriniView profile

Associate Professor

• Data Management
• Large Scale Data Management
• Semantic Data Management
• +15 more

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.

Alfredo De SantisView profile

Professor

• Data Security
• Cryptography
• Digital Forensics
• +4 more

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.

Paolo FalcarinView profile

Associate Professor

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +5 more

Paolo Falcarin is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. His research focuses on cybersecurity, software engineering, and AI compliance, with notable contributions to software protection mechanisms, reverse engineering defenses, and cyber-physical systems. He actively participates in editorial roles for journals like Computers & Security and serves on program committees for IEEE/ACM conferences. Key Projects: ASPIRE (EU FP7), SERICS (PNRR), and Flexymob (Currant s.r.l.) Research Interests: Cybersecurity Knowledge Graphs, Runtime Security (Falco), AI Act Compliance, and Software Renewability His work bridges theoretical cybersecurity advancements with practical applications in distributed systems and IoT. Recent publications emphasize anomaly detection in large-scale systems and regulatory compliance frameworks for AI.

• Artificial Intelligence
• Health Informatics
• Semantic Web Technologies
• +5 more

Antonella Carbonaro is a **Confirmed Associate Professor** at the **Department of Computer Science and Engineering** of the University of Bologna. She holds a PhD in Intelligent Artificial Systems from the University of Ancona and has conducted post-doctoral research in AI. Her work focuses on **data and knowledge modeling for semantic representation**, particularly in healthcare domains. She leads major projects like the **DARE initiative** (digital lifelong prevention) and the **Cancer Virtual Lab**, leveraging knowledge graphs and AI to advance healthcare analytics. She is also involved in gender-balance initiatives like the **Ragazze Digitali** program and contributes to EU projects such as TEHDAS2 (European Health Data Space). Her research integrates semantic web technologies, FHIR standards, and digital twin systems to enhance healthcare interoperability and decision-making. She has been awarded the **National Scientific Qualification (ASN) for Full Professor** (2023). She serves on committees including the Emilia-Romagna Region’s AI in Healthcare Working Group and chairs multiple project work packages.

Markus ZankerView profile

Professor

• Information Systems
• Knowledge-based Decision Support Systems
• Personalization
• +3 more

Markus Zanker is a full professor at the Faculty of Computer Science , Free University of Bozen-Bolzano (Italy), where he previously served as vice dean for studies and director for study programmes. He held associate professorship at Alpen-Adria-Universitaet Klagenfurt, Austria (2011-2016). Research Focus : Knowledge-based decision support systems, personalized information filtering/retrieval, product recommendation, conversational sales advisor systems, and product configurators Current Projects : Causal discovery in recommender systems, medical recommendation systems (insulin dosing, drug combinations), explainable AI for tourism and news recommendation The 15 most recent publications (2016-2025) cover topics including: Deep reinforcement learning for medical applications Causal modeling in tourism and online decision-making Explainable recommendation frameworks Session-based news and video recommendations Evaluation of open-source recommendation libraries His work also explores user interaction patterns through studies on bimodal rating statistics, collaborative explanations, and proactive recommendation engagement.

• Machine Learning
• Biomedical Signal Processing
• Deep Learning
• +10 more

Luigi Borzi is a Fixed-Term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He is a member of the PolitoBIOMed Lab, focusing on biomedical engineering and digital health. His research spans applied artificial intelligence, machine learning, and deep learning for engineering problems, with a strong emphasis on human movement analysis, gait disorders, and wearable devices for Parkinson’s disease monitoring. His recent publications highlight advancements in freezing of gait detection , FMCW radar-based activity recognition , and graph-based multimodal indoor activity analysis . He actively teaches courses such as Machine Learning in Healthcare: From Theory to Practice at the PhD and master’s levels, alongside contributing to undergraduate courses in algorithms and computer science. His work aligns with ERC sectors in artificial intelligence, biomedical signal processing, and sensor networks. He contributes to the SMILIES research group and the PERSIMMON project (2024–2028), funded by the EU Horizon program, developing personalized smart patches for sustainable healthcare. His technical skills include scientific computing, time series analysis, and networked systems for medical applications.

• Wireless sensor networks
• Internet of Things (IoT)
• Air pollution monitoring
• +11 more

Renato Ferrero is an Associate Professor at the Department of Control and Computer Science (DAUIN), Politecnico di Torino (Polito) , with key roles as contact person for training activities and member of the PIC4SeR Interdepartmental Center for Service Robotics . His research spans Wireless Sensor Networks (WSN) , Internet of Things (IoT) , and Environmental Monitoring , supported by competitive grants like AGRITech Spoke 6 (2022-2025) and MIUR funding (2017). He has published extensively on topics including air pollution monitoring , quantum-inspired security , and agricultural technology , with recent work focusing on deep learning for mask/respirator detection and biofertilizer analysis . As an IEEE Access Associate Editor and program committee member for conferences like COMPSAC and RFID-TA, he contributes to academic governance. His teaching includes Computer Architecture (2019-2025) and Ubiquitous Computing (2019-2021) at Polito. He advises PhD students Chiara Panico and Nicola Dilillo , with projects in Data Science , Computer Vision , and AI Life Sciences .

Gabriele TolomeiView profile

Associate Professor

• Machine Learning
• Explainable AI
• Adversarial Machine Learning
• +5 more

Gabriele Tolomei is an Associate Professor of Computer Science at Sapienza University of Rome. He leads the HERCOLE Lab (Human-Explainable, Robust, and Collaborative Learning) and focuses on advancing AI systems that are interpretable, robust, and decentralized. His research spans machine learning, computer security, and adversarial learning. PhD in Computer Science, Ca' Foscari University of Venice (2011) MSc in Computer Science, University of Pisa (2005) BSc in Computer Science, University of Pisa (2002) His research interests are centered on Explainable AI , Robust Machine Learning , and Collaborative Learning . He has contributed to areas such as counterfactual explanations for graph neural networks, community membership privacy, and fairness in graph algorithms. His work intersects Web Search and Mining with Computational Advertising , emphasizing user engagement and post-click satisfaction. He teaches courses like Operating Systems for BSc Computer Science and Big Data Computing for MSc Computer Science. Current PhD students under his supervision include Edoardo Gabrielli, Fabiano Veglianti, Flavio Giorgi, Matteo Silvestri, and Vittoria Vineis. The HERCOLE Lab, established in 2021, promotes interdisciplinary research on human-centered AI, adversarial resilience, and edge computing. Collaborators include Fabrizio Silvestri (Full Professor), Federico Siciliano (Postdoctoral Researcher), and Ziheng Chen (Research Scientist at Walmart Labs).