جستجوی استاد راهنمای دانشگاهی

Elena Maria Baralis is a Full Professor at the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She serves as Pro-Rector, member of the Board of Directors (without voting rights), member of the Academic Senate (without voting rights), and coordinator of the University's Permanent Observatory for Monitoring the Academic Sector. She chairs the Control and Computer Engineering Department and previously chaired the Computer Engineering School from October 2012 to October 2018. Her research interests focus on database systems and data mining, specifically explainable AI, bias detection in data analytics, and machine learning algorithms for big data. Her work spans various application domains including predictive maintenance, Industry 4.0, and healthcare. Recent publications demonstrate her expertise in speech processing, bias mitigation, and innovative neural network architectures like Kolmogorov-Arnold Networks. Her research output shows a clear trend toward addressing fairness and explainability in AI systems while exploring novel approaches to speech and language understanding. Professor Baralis has received significant recognition including becoming a Fellow of the Academy of Sciences of Turin in 2017. She has served as Editor-in-Chief for IEEE Internet of Things Journal (2016-2019) and Knowledge and Information Systems (2014-present). She actively mentors doctoral students including Claudio Savelli (researching Machine Unlearning), Eleonora Poeta, Giuseppe Gallipoli, Alkis Koudounas, and others. Her research is supported by numerous projects including AI4CTI (Artificial Intelligence for Cyber Threat Intelligence, 2025-2028), Smart manufacturing driven by Machine Learning in Industry 4.0 (2019-2020), and I-REACT (2016-2019).

• Data Mining
• Database Systems
• Explainable AI
• +۵ مورد دیگر

Fabio Aurelio D'Asaro is a Researcher at the Department of Human Sciences , University of Verona. His research focuses on Logic , Non-Standard Logics (non-monotonic, paraconsistent, modal, and probabilistic reasoning), and Explainable AI , with interdisciplinary connections to Philosophy of Science and Computational Epistemology . He explores how transparent AI systems can enhance trust, reliability, and ethical considerations in human-machine interactions, addressing topics like autonomy, control, and moral responsibility. D'Asaro is involved in international collaborations with institutions such as UCL KIDS , Imperial SPIKE , and LUCI Unimi . He also serves as an editor and organizer for journals and conferences on Explainable AI , AI Ethics , and Formal Argumentation . Research Grants : Development of Artificial Intelligence methods to support insurance policy sales (2022–2025) Scientific Awards : Premio italiano di Pedagogia 2025 (SIPED prize) for the book Il feedback a scuola. Strategie per promuovere l'apprendimento Teaching Roles : Big data epistemology (Master's Degree in Data Science) Computational epistemology and philosophy (Master's Degree in Artificial Intelligence) Logic and philosophy of science (Bachelor's Degree in Philosophy) Clinical logic and philosophy of science in physiotherapy (Bachelor's Degree in Physiotherapy programs across Vicenza, Rovereto, and Verona) Committee Memberships : ETHICS COMMITTEE – Human Sciences Research Policy Committee Seminar and Publications Committee

• Logic
• Philosophy of Artificial Intelligence
• Non-Standard Logics
• +۳ مورد دیگر

Gian Antonio Susto is an Associate Professor at the Department of Information Engineering , University of Padova . With a Ph.D. in Information Technology and post-doctoral experience at National University of Ireland, Maynooth, he leads research in Machine Learning , Semiconductor Manufacturing , and Industrial IoT . His work bridges Anomaly Detection , Continual Learning , and Algorithmic Fairness with applications in Hydroelectric Power Plants , Particle Accelerators , and Smart Mobility . B.Sc. and M.Sc. in Controls Engineering, University of Padova (cum laude) Ph.D. in Information Technology, University of Padova (2013) Post-Doc at National University of Ireland, Maynooth (2012-2013) Assistant Professor at University of Padova (2013-2021) His research focuses on Explainable AI , Virtual Metrology , and Deep Learning for manufacturing and infrastructure monitoring. Recent projects include the AIMS5.0 (AI for Manufacturing Sustainability) and MICS (Circular Economy in Italy) initiatives. His publications span Engineering Applications of Artificial Intelligence , IEEE Transactions , and Information Processing & Management , with 15+ recent papers on topics like Fault Diagnosis , Continual Learning , and Fair Ranking . Key scientific awards include: IEEE CCTA Best Student Paper Award (2021) IP&M 2020 Ph.D Paper Award Best Industry Paper Award, European Workshop on Advanced Control and Diagnosis (ACD 2019) He has supervised Ph.D. students on projects involving Particle Accelerators , Plant Behavior Modeling , and Explainable AI , with alumni now at institutions like Max Planck Institute , IBM , and Scripps Research . Current teaching includes Reinforcement Learning and Explainable Machine Learning at graduate and Ph.D. levels.

• Machine Learning
• Deep Learning
• Predictive Maintenance
• +۶ مورد دیگر

Gaetano Valenza is an Associate Professor of Bioengineering at the University of Pisa, Italy, where he leads the Neuro-Cardiovascular Intelligence Lab at the Enrico Piaggio Research Centre. He holds affiliations with the Neuroscience Statistics Research Laboratory at MIT and has served as a Research Fellow at Harvard Medical School and Massachusetts General Hospital. His academic work spans bioengineering, computational physiology, and affective computing. His research focuses on statistical and nonlinear biomedical signal and image processing , cardiovascular and neural modeling , and physiologically interpretable artificial intelligence . He develops wearable systems for physiological monitoring, with applications in autonomic nervous system assessment, brain-heart interactions, and mental health. His work has led to novel metrics such as the Sympathetic and Parasympathetic activity indices derived from ECG. The 15 most recent publications reflect a consistent trend in brain-heart interplay , complexity analysis of physiological signals , explainable AI in healthcare , and virtual reality applications in mental health . His work integrates advanced signal processing, nonlinear dynamics, and machine learning to decode emotional and cognitive states from physiological data. Dr. Valenza is a Senior Member of IEEE and serves on several technical committees. He is an active editorial leader, currently serving as Associate Editor for IEEE-EMBC , Plos One , Complexity , and Scientific Reports , and has guest-edited special issues in Philosophical Transactions of the Royal Society A and IEEE Journal of Biomedical and Health Informatics . He has led or participated in numerous international research projects, including FP7 and H2020 initiatives such as NEVERMIND and EXPERIENCE. He teaches courses in Biostatistics, Probability & Biostatistics, and Advanced Image Processing at the University of Pisa. As lab head and project coordinator, he leads a multidisciplinary team working on neuro-cardiovascular intelligence, wearable systems, and AI-driven mental health interventions.

• Biomedical Signal and Image Processing
• Cardiovascular and Neural Modeling
• Physiologically-interpretable Artificial Intelligence systems
• +۵ مورد دیگر

Prof. Dr. Chunyang Chen is a full professor in the Department of Computer Science at the Technical University of Munich (TUM) in Heilbronn, Germany, and an adjunct professor at Monash University, Australia. He is a core member of the Munich Data Science Institute, board member of Heilbronn Data Science Center, and fellow at Fortiss. His research spans Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), focusing on AI/ML, NLP, and program analysis applications. PhD from Nanyang Technological University (NTU), Singapore His research explores three primary domains: AI-Driven Mobile App Development : Focused on requirement elicitation, UI design, code generation, and GUI testing using LLMs, NLP, and lightweight program analysis. Software Repository Mining : Involves library/API migration, collaborative editing, query reformulation, and thesaurus construction. Deep Learning Security : Examines robustness and security in mobile apps, including on-device model attacks, backdoor attacks, phishing attacks, and defense mechanisms. His recent publications highlight trends in LLM applications for software testing, bug replay, accessibility, and dataset construction. Awards include Best Paper Honorable Mention at CHI 2024, DECRA, ACM SIGSOFT Early Career Award, multiple Distinguished Paper Awards, and the Chinese Government Award for Outstanding Self-financed Students Abroad. PhD students: Shengcheng Yu, Yanqi Su, Mark Huasong Meng, Wenchao Gu, Zhen Tao, Kai Huang, Sidong Feng, Yujin Huang, Jian Gu Master/Bachelor students: Dustin Minh Duc Vu, Jason Siu, Ruiqi Dong, Minh Nguyen, An-Chieh Chang, Chao Wu, Yujin Huang, Ishan Joshi, Xinyan Li Chunyang Chen has supervised numerous students across institutions including TUM, Monash University, Chinese Academy of Sciences, ANU, and others. His teaching portfolio includes courses on Generative AI, software engineering, and programming foundations. He actively contributes to academic service as a guest editor, program committee member, and reviewer for top-tier conferences and journals.

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +۷ مورد دیگر

Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.

• Big Data Analytics
• Social Network Analysis and Mining
• Analysis of Human Mobility
• +۱۷ مورد دیگر

Antonio De Rosa is an Associate Professor in the Department of Decision Sciences at Bocconi University, Italy. Previously, he held positions at the University of Maryland, College Park (2020–2024), and the Courant Institute of Mathematical Sciences, New York University (2017–2020). He earned his Ph.D. in Mathematics from the University of Zurich in 2017 under Camillo De Lellis and Guido De Philippis. Education: Ph.D. in Mathematics, University of Zurich (2017). His research spans Geometric Analysis , Partial Differential Equations , Calculus of Variations , Geometric Measure Theory , Optimal Transport , and Non-convex Optimization . Recent work focuses on anisotropic geometric variational problems, including existence, regularity, and uniqueness of anisotropic minimal surfaces and CMC (constant mean curvature) surfaces. He has also contributed to interdisciplinary applications in Explainable Risk Assessment and Data Analysis . The 15 most recent articles highlight advancements in anisotropic surfaces , min-max theory , optimal transport , and mathematical programming (e.g., K-means clustering, linear programming). Key trends include the intersection of geometric measure theory with nonlinear PDEs and applications in machine learning and transportation networks . Scientific Awards and Grants: 2023 Maryland Research Excellence Carlo Ciliberto Prize (2019) ERC Starting Grant ANGEVA (101076411, 2023–2028) Air Force Office of Scientific Research (AFOSR) grant (FA9550-23-1-0123) NSF CAREER Award (DMS-2143124) NSF DMS Awards (DMS-1906451, DMS-2112311) AMS Simons Travel Grant Antonio actively supervises research and teaches courses such as Optimization and Introduction to Partial Differential Equations . His work is supported by significant funding totaling approximately €3 million.

• Geometric Analysis
• Partial Differential Equations
• Geometric Measure Theory
• +۳ مورد دیگر

Giuseppe Santucci is an Associate Professor at the Department of Computer, Control and Management Engineering Antonio Ruberti at Sapienza University of Rome. He teaches courses on Fundamentals of Computer Science, Software Engineering, and Visual Analytics. His office is located in Room B218 at Via Ariosto 25, Rome, and his contact email is santucci@diag.uniroma1.it. Dr. Santucci's research focuses on Visual Analytics, Information Visualization, Human-Computer Interaction, and Information Retrieval. His work spans theoretical aspects of visual query languages for semantic models to practical applications in visual analytics for cybersecurity, cryptocurrencies, and deep learning explainability. He has published over 130 articles in international journals and conferences, demonstrating his significant contributions to these fields. His recent publications show a strong trend toward applying visual analytics to increasingly complex domains including cybersecurity, cryptocurrencies, and explainable AI. The work demonstrates an evolution from theoretical foundations of visual query systems to practical applications that help users understand complex data and systems. His research bridges the gap between theoretical computer science and practical user-centered solutions. Dr. Santucci has received notable recognition including: IEEE VizSec 2018 Best Paper Award Human-Computer Interaction Cybersecurity Awards 2018 He actively mentors students through thesis projects focused on information visualization and visual analytics. His PROMISE project provides a framework for students to engage in cutting-edge research in information retrieval and visual analytics. He has supervised work on topics including visual evaluation techniques, visual mappings optimization, and user studies for Infovis systems. Dr. Santucci leads the A.WA.RE (Advanced Visualization & Visual Analytics REsearch) group at Sapienza University. This group conducts research on visual analytics tools for information retrieval evaluation, cybersecurity analysis, and deep learning explainability. Their work includes developing frameworks like CryptoComparator for cryptocurrency analysis and BUCEPHALUS for cybersecurity platform analysis.

• Visual Analytics
• Information Visualization
• Human-Computer Interaction
• +۳ مورد دیگر

Diego Calvanese is a Full Professor in Computer Engineering at the Faculty of Engineering of the Free University of Bozen-Bolzano, Italy. He serves as Spokesperson of the Institute of Computer Science and Artificial Intelligence and Director of the Smart Data Factory technology transfer lab at NOI Techpark. As Coordinator of the Intelligent Integration and Access to Data (In2Data) research group, part of the Research Centre for Knowledge and Data (KRDB), he leads significant research initiatives in knowledge representation and data management. Calvanese's research focuses on virtual knowledge graphs for data access and integration, ontology-based data access, description logics, semantic web technologies, graph data management, and verification of data-aware processes. His work bridges theoretical foundations with practical applications through the Ontop framework, which enables SPARQL query answering over OWL 2 QL ontologies connected to external data sources. His research has substantial practical impact, powering the South Tyrol Open Data Hub Knowledge Graph and supporting numerous European and national research projects. With more than 400 refereed publications and over 39,000 citations (h-index 80), Calvanese's recent work demonstrates continued leadership in virtual knowledge graphs, ontology-based data federation, explainable AI through knowledge representation, and integration of complex data types including 3D city models and raster data. His publications show a clear trajectory from theoretical foundations toward increasingly practical and applied research addressing real-world data integration challenges across multiple domains. ACM Fellow (2019) EurAI Fellow (2015) AAIA Fellow Program Chair of PODS 2015 and KR 2020 General Chair of ESSLLI 2016 Calvanese has secured significant research funding through numerous competitive projects including EU H2020 INFRAEOS Project (INODE), Italian PRIN Project (HOPE), FESR Project (IDEE), and EU FP7 IP Project (Optique), totaling close to 6.4M Euro. As an originator and co-founder of Ontopic, the first spin-off of the Free University of Bozen-Bolzano, he has successfully translated research into commercial applications. He serves as Associate Editor of Artificial Intelligence (AIJ) and has participated in over 200 program committee roles for international conferences. As Director of the Smart Data Factory technology transfer lab and coordinator of the In2Data research group, Calvanese bridges academic research with industry applications, focusing on practical implementations of knowledge graph technologies. His work with the KRDB Research Center has established Bozen-Bolzano as a significant hub for knowledge representation and data management research in Europe.

• Knowledge Representation and Reasoning
• Virtual Knowledge Graphs
• Ontology-based Data Access and Integration
• +۷ مورد دیگر

Dr. Gianluca Demartini is a leading researcher in Human-in-the-loop AI Systems with significant contributions to Crowdsourcing , Information Retrieval , and Generative AI applications. His work bridges Machine Learning and Human-Computer Interaction , focusing on Bias Management , Fact-Checking , and Ethical AI . Major Affiliations : L3S Research Center, ScienceWISE platform, and collaborations with institutions like University of Queensland and University of Padua Over 15 years, his research has explored Crowdsourcing Quality Control (Mechanical Cheat 2012), Entity Ranking (2008-2013), and Semantic Search . Recent work (2024-2026) focuses on Generative AI Impacts in domains like Media Literacy , Data Curation , and Visual Analytics . Scientific Recognition : Best Paper Award (Top 1.4%) at ICTIR 2023 Best Short Paper Award (Top 0.6%) at ECIR 2020 Honorable Mention (Top 2%) at CSCW 2020 Best Demo Award at ISWC 2011 3rd Best Paper at LA-WEB 2008 His 15 most recent publications (2024-2026) demonstrate expertise in LLM-based Content Moderation , Immersive Data Visualization , and Trustworthy AI Systems . He has pioneered methods for Bias Detection in Wikipedia (2013), Entity Ranking (2008-2013), and Human-AI Collaboration frameworks. His work consistently addresses ethical challenges in AI for Social Good and Responsible Data Science .

• Information Retrieval
• Crowdsourcing
• Human-in-the-loop AI
• +۵ مورد دیگر

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

• Computational Intelligence
• eHealth
• Data Stream Mining
• +۵ مورد دیگر

Salvatore Ruggieri is a Full Professor in the Department of Computer Science at the University of Pisa, where he teaches in the Master Programme in Data Science and Business Informatics. He is affiliated with the KDD LAB, a joint research group of ISTI-CNR and the University of Pisa, and actively contributes to national and European AI initiatives such as XAI, NoBIAS, TAILOR, and SoBigData.eu. His research focuses on data mining and knowledge discovery, with a strong emphasis on ethical AI. Key areas include discrimination discovery and prevention, fairness, privacy, explainable AI (XAI), causal inference, and classification algorithms. He has led significant projects such as ENFORCE, a national FIRB project on legal and computational enforcement of non-discrimination and privacy rights in ICT systems (2010–2014), and has served as program chair for the XIII Italian Symposium on Artificial Intelligence (2014). The recent publications (2018–2023) highlight a consistent trend in interpretable and fair machine learning, including selective classification, stability of interpretable models, and causal reasoning for fairness. His work often involves collaboration with leading researchers like Dino Pedreschi and Riccardo Guidotti, and appears in top venues such as AAAI, IEEE TKDE, and WIREs. His scientific honors include the award for the best Ph.D. thesis in Theoretical Computer Science from the Italian Chapter of EATCS. Best Ph.D. Thesis in Theoretical Computer Science, Italian Chapter of EATCS He advises and collaborates with numerous researchers in the KDD LAB and has contributed to major grants and research initiatives in AI and data science. He is involved in educational programs, including the National Ph.D. in Artificial Intelligence - Society, and promotes interdisciplinary research at the intersection of computer science, law, and ethics. Ruggieri is a member of the KDD LAB, where he leads research in ethical and transparent AI. He is also part of large collaborative networks such as SoBigData.eu and HumanE-AI-Net, which aim to build socially responsible and human-centered AI systems.

• Data Mining
• Knowledge Discovery
• Discrimination Discovery and Prevention
• +۹ مورد دیگر

Federico Becattini is a Tenure-Track Assistant Professor at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He is an active member of the Siena Artificial Intelligence Lab (SAILab), where he contributes to cutting-edge research in computer vision, deep learning, and artificial intelligence. His work spans multiple interdisciplinary domains, including autonomous driving, human behavior understanding, cultural heritage, neuromorphic vision, and fashion recommendation. His research interests center on memory-based neural networks , which he has applied in numerous publications at top-tier venues such as CVPR, ECCV, IEEE TPAMI, and ACM TOMM. He has also delivered tutorials on this topic at international conferences including ICIAP 2022 and ACM MM 2022, and taught a Ph.D. course at the University of Florence. His recent work is aligned with the Collectionless AI paradigm, which emphasizes continual learning and interaction with dynamic environments. The recent publications highlight a strong trend in human-centric AI , focusing on understanding people through multimodal analysis of face, body, and clothing, as well as generating 3D virtual avatars. There is also a clear emphasis on memory-augmented architectures for temporal reasoning, explainability, and adaptive learning. His editorial role as Associate Editor of the International Journal of Multimedia Information Retrieval further underscores his standing in the research community. Associate Editor, International Journal of Multimedia Information Retrieval (IJMIR) Organizer, Workshop on Facial and Body Expressions (ICPR2020) Co-organizer, T-CAP Workshop (ICIAP2021, ICPR2022) Co-organizer, MCFR Workshop (ACM MM 2022) Co-organizer, WCPA Workshop and Challenge (ECCV 2022) Federico Becattini actively advises students and researchers within SAILab, particularly in the context of Ph.D. theses and research projects related to Collectionless AI and memory-based models. While specific grants are not mentioned, his extensive publication record and leadership in workshops and editorial roles suggest involvement in funded research initiatives. He collaborates with both academic and international research communities, serving as a reviewer for top-tier conferences and journals. He is a core member of the SAILab research group, which is pioneering the Collectionless AI initiative—a framework for continual learning over time, interacting with humans and agents without relying on pre-built static datasets. This lab serves as a hub for innovation in adaptive and sustainable AI systems.

• Computer Vision
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Maria Luce Lupetti is a Fixed-term Assistant Professor at the Department of Architecture and Design (DAD) at Politecnico di Torino . She actively contributes to the College of Architecture and Design and is a member of the College of Mechanical, Aerospace and Automotive Engineering . Her academic role involves teaching Cognitive Ergonomics and HMI in the Automotive Engineering master's program and collaborating on Design and Communication courses. Editorial Roles: Associate Editor for the journal INTERACTIONS since 2024 Conference Leadership: Participating in organizing committee for CHI 2026 Research Projects: Scientific Director of PARJAI (2025-2028) on Participatory Design Justice for Ethical AI Transitions Research Focus : Maria's work bridges Design with Human-Robot Interaction , focusing on Ethical AI , Speculative Design , and Urban Robotics . Her recent publications explore: Speculative design for sustainable digital futures Contextual adaptability challenges in urban robotics Design taxonomies for demystifying AI Trustworthy embodied agents in healthcare Creative applications of AI in HRI Ethical frameworks for energy futures Design Philosophy : She emphasizes participatory approaches, critical design thinking, and transdisciplinary collaboration to address societal challenges at the intersection of technology, ethics, and human factors. Her work appears in leading venues including CHI , HRI , and Frontiers in Neurorobotics , while also contributing to edited volumes and journal special issues.

• Design
• Human-Robot Interaction
• Speculative Design
• +۷ مورد دیگر

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

• Machine Learning
• Data Mining
• Bioinformatics
• +۱۱ مورد دیگر