Andrea Bottino is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) at the Polytechnic University of Turin. He has been actively involved in the Computer Graphics and Vision Group and leads various VR@POLITO initiatives. Chair of the Master HUMANAIZE program Coordinator of multiple Machine Learning for Vision and Multimedia courses Research Focus : Augmented and Virtual Reality for education and safety Computer Vision with applications to medical imaging and kinship analysis Human-Computer Interaction in immersive environments Multimodal Learning systems XR for Cultural Heritage Publication Trends : Recent works focus on AI applications , XR training systems , and computer vision techniques applied to medical diagnostics and cultural preservation . Leadership Roles : Scientific Director for MEI - Interactive Egyptian Museum Coordinator of PNRR Mission 4 projects Principal Investigator for Holo-BLSD and ALPTECH initiatives Labs and Collaborations : Active in Visionary LAB and VR@POLITO Collaborates with Balletto Teatro di Torino for cultural XR applications Partners with Fondazione Museo Egizio and Robin Studio
Santa Di Cataldo is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino. His research focuses on computer vision, pattern recognition, digital image processing, and medical image processing, with applications in industrial systems and AI for manufacturing. Scientific Branch: IINF-05/A - Information Processing Systems ERC Sectors: PE6_8 - Computer graphics, computer vision, multi media, computer games ERC Sectors: PE6_11 - Machine learning, statistical data processing His work includes developing AI-driven anomaly detection frameworks, physics-informed neural networks for additive manufacturing optimization, and neuro-symbolic approaches for Industry 4.0 applications. He supervises PhD students in Artificial Intelligence and Computer Engineering programs, collaborating on projects like BIG (Blue Is Green) and PNRR-Complementary Plan. Premio Donna Innovazione (2010) He leads courses such as Machine Learning in Applications and Applied AI and Machine Learning , while contributing to bioinformatics and robotics-related teaching. His research is supported by IAM@PoliTo and EDA groups, utilizing LADISPE laboratory facilities.
Bartolomeo Montrucchio is a Full Professor of Information Processing Systems (ING-INF/05) at the Department of Control and Computer Engineering (DAUIN) of the Polytechnic University of Turin. He is a member of the Interdepartmental Center Photonext - PoliTo Interdepartmental Center on Applied Photonics and serves as deputy director at the Interuniversity Center of Regional Interest for the Training of Secondary School Teachers (CIFIS) since July 2012. Additionally, he has held an adjunct professor position at the University of Illinois at Chicago during July 2008. Professor Montrucchio's research spans several cutting-edge areas with a primary focus on quantum computing, computer vision, and sensor networks. His work encompasses image processing, scientific visualization, parallel and distributed systems, and wireless sensor networks. He actively contributes to European research initiatives including the EQUO (European QUantum ecOsystems) project as Scientific Responsible. His research bridges theoretical computer science with practical applications across multiple industries. His publication record shows a strong trajectory toward quantum technologies, with numerous recent publications focusing on quantum machine learning, quantum algorithms for financial applications, and quantum applications in cybersecurity. His work demonstrates increasing emphasis on practical implementations of quantum computing in real-world scenarios, particularly in industrial settings and telecommunications. Best student paper award at BIOSIGNAL2002, conferred by EURASIP, Italy (2002) Associate Editor of IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019-present) Professor Montrucchio actively supervises numerous PhD students working on quantum computing applications across various domains including finance, cybersecurity, traffic optimization, and industrial use cases. His teaching portfolio includes courses on Quantum Computing, Parallel and Distributed Computing, and Image Processing and Computer Vision across multiple degree programs including Computer Engineering, Biomedical Engineering, and Quantum Engineering. He leads multiple research projects funded by both competitive calls and commercial contracts, with a significant focus on quantum technologies since 2019. His patent portfolio includes several inventions related to tire manufacturing processes and visual rehabilitation for telemedicine.
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 .
Dr. Danilo Giordano is an Associate Professor at the Department of Control and Computer Science (DAUIN) within Polytechnic University of Turin. He actively contributes to the SmartData@PoliTO Center, focusing on big data and data science applications. Research Interests: Big data, machine learning, cybersecurity, predictive maintenance, smart cities Scientific Awards: Best Student Paper Award ITC (2015), IETF Applied Research Prize coauthor (2017) His academic work spans machine learning for network security , big data analytics in industrial contexts , and smart city infrastructure optimization . Notably, his recent publications examine satellite network performance, darknet visibility enhancement, and language model applications in cybersecurity. As an editorial board member of COMPUTER NETWORKS (since 2024), he has also organized multiple conferences including the Data Challenge sessions at PHME conferences and served as publication chair for ACM CoNEXT workshops.
Angelo Spognardi is an Associate Professor in the Department of Computer Science at Sapienza University of Rome, leading the Network Security Lab group since March 2020. He teaches courses including Practical Network Defense and Programming Unit 2 for Computer Science and Cybersecurity programs. His research spans information security , with emphasis on fake phenomena in social media , fake content analysis in review systems, adversarial machine learning , and network security for resource-constrained devices . His work integrates bio-inspired models for bot detection and focuses on resilient metrics against disinformation campaigns. Recent publications explore LLM-powered bot detection and IPv6 security. He leads the Prebunking research project predicting coordinated inauthentic behaviors in social media. His lab promotes initiatives like CyberX Mind4Future , offering cybersecurity training with virtualized labs and hackathons. Master's students in Cybersecurity under his guidance achieve 100% placement with top salaries in Italy. Spognardi maintains active collaborations with the Sysma group at IMT Lucca and previously worked with DTU IoT Center and CNR's Institute of Informatics and Telematics. His industry impact includes frameworks like SafeDroid for Android malware detection and analyses of IoT broker vulnerabilities.
Blandino Alberto is a Fixed-term Researcher in Forensic Medicine at Vita-Salute San Raffaele University in Milan, Italy. He holds active research and teaching appointments through September 2026, continuing a series of academic positions at the university since at least 2020. His research focuses on forensic medicine with particular expertise in: Death investigation methodologies and mechanical asphyxia analysis Suicide patterns among vulnerable populations, especially youth Homicide studies, including child and adolescent victims Application of artificial intelligence in forensic science Forensic identification techniques including microbiome analysis Healthcare violence and workplace dynamics Dr. Alberto's publication record demonstrates a strong interdisciplinary approach, combining traditional forensic pathology with computational techniques and social science perspectives. His work spans high-impact journals in forensic science, legal medicine, and nursing, with numerous 2024-2025 publications indicating active research productivity. He teaches multiple courses including 'Medicina legale e sanità pubblica' for medical students across various sections, 'Public health, Global health & Legal medicine' for international medical programs, and specialized forensic science courses. His teaching responsibilities span both academic semesters, indicating substantial educational commitment.
Andrea Fronzetti Colladon is a Professor of Business Leadership and Intelligence at Roma Tre University, where he leads the Business and Collective Intelligence Lab. His academic career bridges rigorous research with practical business applications, focusing on how connections drive value and innovation in complex organizational environments. His research interests span Network Science, Natural Language Processing, Machine Learning, Social Network Analysis, Text Mining, Brand Analytics, Change Management, and Executive Coaching . He combines methods from network science and computational linguistics with theoretical frameworks from social sciences to advance understanding of management and human behavior. His work has practical applications in brand analytics, change management, and organizational transformation. Developed the Semantic Brand Score methodology for brand performance analysis Created the OCEAN Change Management Model for organizational transformations Authored influential books including Leading Meaningful Change and Social Network Analysis and Text Mining for Big Data Collaborates with leading institutions including MIT Center for Collective Intelligence, Northeastern University, and Université du Québec à Montréal His recent publications reveal a strong trend toward integrating text mining and network analysis to solve business problems, particularly in brand management, venture capital funding, and organizational change. His work consistently demonstrates how linguistic patterns and network structures can predict business outcomes and inform strategic decisions. Among his notable recognitions is the 2020-2021 Best Paper award from the International Journal of Forecasting . His research has been published in high-impact journals and books with publishers including Edward Elgar, Springer, and Routledge. As an educator and consultant, Fronzetti Colladon develops specialized courses on Social Network Analysis, Text Mining, Change Management, Problem Solving, and Business Analytics. He has worked with major organizations including TIM, Genpact, Enel, SACE, Renault Italia, Saatchi & Saatchi, Sky Italia, and GalaxyAdvisors, translating academic insights into practical business solutions. The Business and Collective Intelligence Lab he leads serves as a hub for interdisciplinary research, bringing together computer scientists, linguists, social scientists, and business experts to decode complexity and empower decision-making through innovative methodologies.
Carla Limongelli is an Associate Professor at Roma Tre University's Department of Civil, Computer and Aeronautical Engineering within the School of Engineering. Her academic work bridges computer science with educational applications, focusing on intelligent systems for learning environments. Her research interests center on artificial intelligence applications in education, with particular expertise in concept mapping systems, learning management platforms, and technology-enhanced museum experiences. Dr. Limongelli has developed innovative approaches to adaptive learning, social robotics in educational contexts, and multimodal learning analytics that track both physiological responses and behavioral patterns. Her recent publication trends reveal a strong focus on leveraging large language models for educational purposes, with increasing attention to multimodal applications combining visual, textual, and physiological data streams. This work spans from automated question generation to social robot interactions in museum settings. Dr. Limongelli has contributed significantly to the development of systems that support teachers in course building, concept map creation, and personalized learning path configuration, with applications extending from traditional educational settings to cultural heritage environments.
Francesca Condino serves as Associate Professor of Statistics (SECS-S/01) at the University of Calabria's Department of Economics, Statistics and Finance since 2020, following her tenure as Researcher from 2012. She teaches graduate courses including Multivariate Data Analysis and Statistical Methods for Business Strategies within the Statistics for Data Science and Data Science for Business Analytics programs. Her academic qualifications include: Bachelor's degree with honors in Statistical and Actuarial Sciences from University of Calabria (2002) Master's in Economy and Statistics of Territory from G. Tagliacarne Institute, Rome (2003) Ph.D. in Statistics from University 'Federico II' of Naples (2010) Condino's research centers on dynamic classification algorithms, copula-based dependence modeling, and novel probability distributions. She develops statistical frameworks for analyzing income/consumption data, hydrological phenomena, and medical diagnostics, with particular expertise in density-valued symbolic data classification and spectral biomarker analysis. Her methodological innovations bridge theoretical statistics with applications in economic policy and clinical neuroscience. Analysis of her recent publications reveals three dominant research streams: economic inequality studies using Lorenz curves and share-density clustering; medical diagnostics through FTIR spectroscopy and multivariate analysis for multiple sclerosis/epilepsy; and theoretical contributions to unit interval distributions and copula-based modeling. Her work demonstrates consistent interdisciplinary collaboration between statisticians, economists, and medical researchers. No scientific awards were documented in the provided materials. Her research has been supported by CNR research grants (2004-2010), Calabria Region funding (2008), and University of Calabria research assignments (2012). Teaching experience spans 23 years across multiple institutions, complemented by national scientific qualification for Associate Professorship (2017). She contributes to the 'Statistica & Demografia' research group and utilizes the departmental Statistical Informatics Laboratory for computational work.
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.
Alessandro Giuseppi is an Assistant Professor in Tenure Track at the University of Rome La Sapienza's Department of Computer, Control, and Management Engineering (DIAG). He leads research in intelligent control systems and smart networks at the Network Control Laboratory, while serving as CTO of the startup Automation Intelligence and Control (AICO). As Associate Editor for IEEE Transactions on Automation Science and Engineering and International Journal of Control, Automation, and Systems , he bridges academic research with practical applications. M.Sc. & Ph.D. in Automatic Control from La Sapienza National Scientific Qualification for Associate Professor (2023) Active in EU/National funded projects since 2016 His research spans intelligent systems, network control, and AI integration in automation. Publications emphasize federated learning, deep learning applications, and control theory advancements across domains like autonomous vehicles, healthcare, and industrial automation. Key awards include the Minerva Prize (twice) and Telespazio's T-TeC. Recent publications address: (1) Federated learning with adaptive topologies, (2) Healthcare AI for diabetes management and portal hypertension diagnosis, (3) Industrial AI for manufacturing quality control, and (4) Smart network control in 5G/6G telecommunications. 2023 Premio Minerva - Best Postdoctoral Researcher 2021 Premio Minerva - Best PhD Candidate 2021 Best ETRI Journal Paper 2020 Telespazio Technology Contest Winner As course instructor, he teaches Intelligent and Hybrid Control, Automazione, and Laboratorio di Automatica. His leadership extends to co-founding startup AICO and collaborating with CRAT research consortium on Horizon Europe projects.
Domenico Amato is a Researcher (INFO-01/A) at the University of Palermo in the Department of Mathematics and Computer Science . He holds regular office hours on Mondays from 3:00 PM to 4:00 PM at Via Archirafi 34, Room 203, Second Floor. His research focuses on machine learning, biomedical image analysis, and data structure optimization. His recent work includes: Explainable AI for medical imaging (brain MRIs, histopathology) Graph Neural Networks in biomedical applications Development of Learned Indexes and efficient search algorithms Deep learning applications in data analysis and classification The trends in his publications show a strong emphasis on: Medical imaging analysis (gliomas, diabetic maculopathy, histopathology) Neural network interpretability and transparency Optimization of data structures through machine learning techniques Applications of AI in both healthcare and fundamental computer science
Tomaso Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT) and holds a position at the Artificial Intelligence Laboratory. He serves as Co-Director of the Center for Biological and Computational Learning (CBCL) and is an Investigator at the McGovern Institute for Brain Research, a role he assumed in 2000 after joining the MIT faculty in 1981 following a decade at the Max Planck Institute for Biology and Cybernetics in Tubingen, Germany. A foundational figure in computational neuroscience, Poggio pioneered models of the fly's visual system and human stereovision, introduced regularization theory to computational vision, and made seminal contributions to the biophysics of computation and learning theory. His research bridges neuroscience and artificial intelligence, with significant impact on deep learning, pattern recognition, and computational vision. He is recognized as one of the most cited researchers in his field due to his influential model of visual cortex recognition. Dr. Poggio's distinguished honors include: the Otto-Hahn-Medaille Award of the Max-Planck-Society the Max Planck Research Award (with M. Fahle) from the Alexander von Humboldt Foundation the MIT 50K Entrepreneurship Competition Award the 2003 Gabor Award the 2009 Okawa prize He holds memberships in the Italian Academy of Sciences as a Foreign Member and the American Academy of Arts and Sciences as a Fellow. His leadership at CBCL and the McGovern Institute drives interdisciplinary research integrating biological principles with computational models to advance artificial intelligence and neural understanding.
Prof. Nick Jennings is the inaugural Regius Professor of Computer Science in the Department of Electronics and Computer Science at the University of Southampton and Chief Scientific Adviser to the UK Government. He also advises Aerogility and leads the Agents, Interaction and Complexity Group, an internationally recognized research team in agent-based computing.
