جستجوی استاد راهنمای دانشگاهی

Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.

• Affective Computing
• Software Engineering
• Natural Language Processing
• +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

Daniele Apiletti is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Control and Computer Engineering (DAUIN). He serves as a member of the Interdepartmental Center SmartData@PoliTO and acts as Academic Advisor for the Master's degree program in Data Science and Engineering. Research Groups: DBDM - Database and Data Mining Group (DAUIN) ERC Sectors: Algorithms, Artificial Intelligence, Machine Learning, Web and Information Systems Research Interests span Big Data Analytics, Data Science, Machine Learning, Computer Vision, and Quantum Computing. His work focuses on integrating data-driven and theory-guided approaches for heterogeneous data querying, cloud continuum machine learning, and spatio-temporal models for crisis management. Recent Publications highlight trends in medical image segmentation, predictive industrial modeling, and fault-tolerant data systems. Key subfields include AI in healthcare, scalable manufacturing analytics, and vision-language models for game tutorials. Teaching roles include course ownership of Big Data: Architectures and Data Analytics and Internships across multiple academic years. He has collaborated on courses in Data Science, Database Technologies, and Data Management. PhD Students Supervised: Etibar Vazirov (Cloud Continuum Machine Learning) Gabriele Scaffidi Militone (Cloud Storage Microservices) Daniele Rege Cambrin (Spatio-Temporal Ecology Models) Simone Monaco (Theory-Guided Data Science) Research Projects include commercial contracts on: - Natural language querying of corporate research archives - National tourism ecosystem platforms - AI for thermotechnical system design - Machine Learning in clinical trials and supply chains

• Big data analytics
• Data analysis
• Data management
• +۸ مورد دیگر

Domenico Cicchella is an Associate Professor in the Department of Science and Technology at the University of Sannio, Italy, specializing in Geochemistry and Volcanology (GEO/08). With over two decades of academic experience, he has established himself as a leading researcher in environmental geochemistry, particularly in soil and water contamination studies in the Campania region of southern Italy. His research interests span multiple critical areas of environmental geochemistry: Urban and regional geochemical mapping Heavy metal and potentially toxic element (PTE) analysis in soils Environmental risk assessment methodologies Application of advanced statistical techniques to geochemical data Water pollution monitoring and assessment Integration of compositional data analysis with machine learning approaches Geochemical background determination Analysis of his publication record reveals a strong focus on developing innovative methodologies for environmental assessment, particularly through the "Campania Trasparente" project - a comprehensive multimedia monitoring initiative. His work increasingly incorporates advanced data analysis techniques including compositional data analysis, multivariate statistics, and machine learning applications to address complex environmental challenges in urban and regional settings. His research spans from fundamental geochemical studies to practical applications for environmental management and public health protection. Professor Cicchella maintains an active research profile with numerous collaborations across Italy and internationally, contributing significantly to our understanding of environmental contamination patterns and developing practical approaches for risk assessment and management. His work has appeared in leading journals including Journal of Geochemical Exploration, Applied Geochemistry, and Science of the Total Environment, demonstrating both academic rigor and practical relevance to environmental challenges.

• Geochemistry
• Environmental Monitoring
• Soil Contamination
• +۷ مورد دیگر

Corrado Loglisci is an Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research focuses on Temporal Data Mining , Machine Learning , and Quantum Computing , with applications in bioinformatics, medical informatics, and cybersecurity. He earned his Ph.D. in Computer Science with a thesis on temporal projection in longitudinal data. Research Highlights : Temporal Learning, Textual Data Mining, Quantum-Classical Hybrid Systems Collaborations : IRSTEA Research Institute (France), Aristotle University of Thessaloniki (Greece) His publications address dynamic network analysis , emotion detection in social media , and quantum-enhanced classification . He contributes to program committees and journal editorial work, including a special issue on Mining Complex Patterns in the Journal of Intelligent Information Systems . Notable contributions include the jKarma framework for change detection and studies on concept drift robustness in intrusion detection systems. His work spans European/National research projects, leveraging machine learning for tasks like mobile crowd sensing trustworthiness prediction (2020) and investor behavior analysis (2023-2025).

• Temporal Data Mining
• Temporal Learning
• Textual Data Mining
• +۱۳ مورد دیگر

Paolo Garza is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he also serves as Coordinator of the College of Computer, Film and Mechatronics Engineering. He is a member of the DBDM research group and the SmartData@PoliTO laboratory, and actively contributes to academic governance through roles in teaching coordination and PhD program committees. Education: Bachelor’s in Computer Engineering, Polytechnic University of Turin (2001) PhD in Computer and Systems Engineering, Polytechnic University of Turin (2005) His research centers on data science, big data analytics, data mining, and machine learning , with applications in emergency management, real-time communications, Earth observation, and cybersecurity. He has led and participated in numerous national and commercial research projects, including AI4CTI and NODES (PNRR), and has collaborated with industry partners like Cisco Systems. His work bridges theoretical algorithm development and practical deployment in critical systems. The recent publications reflect a strong trend toward multimodal AI, crisis informatics, and intelligent networking . Articles span computer vision for environmental monitoring (e.g., burned area detection, canopy estimation), ML for real-time communication quality, multimodal document understanding, and crisis response systems. His team leverages deep learning, transformers, and vision-language models across diverse domains. Scientific Service: Associate Editor, Knowledge and Information Systems (2024–) Associate Editor, Expert Systems with Applications (2022–) General Co-Chair, IEEE AICT Conferences (2022, 2023) He mentors several PhD students and leads funded research initiatives focused on AI for sustainable industry and cyber threat intelligence. His teaching includes graduate courses on big data processing, distributed architectures, and data science lab methods. He has also directed commercial training programs and research contracts in machine learning and cybersecurity. Research Labs & Teams: DBDM - Database and Data Mining Group (DAUIN) SmartData@PoliTO - Big Data and Data Science Laboratory LAB 5 - Research Laboratory (DAUIN)

• Big data
• Data science
• Data analytics
• +۱۲ مورد دیگر

Umberto Villano is a Full Professor at the Department of Engineering of the University of Sannio in Italy. His academic specialization is in the field of Information Processing Systems (ING-INF/05), where he conducts research and teaching activities focused on cybersecurity, machine learning applications for security, and network analysis. Professor Villano's research interests span multiple cutting-edge areas in computer science and security. His primary focus is on cybersecurity , particularly in the domains of intrusion detection systems, IoT security, and cloud security. He has made significant contributions to the application of machine learning techniques for security purposes, especially deep learning approaches using autoencoders for anomaly detection. Another major research stream involves fake news and misinformation analysis , where he applies topic modeling and graph-based approaches to understand information propagation patterns. His work bridges theoretical foundations with practical implementations in real-world security systems. An analysis of Professor Villano's recent publications (2023-2025) reveals a strong focus on advanced security techniques using artificial intelligence. His work demonstrates a consistent pattern of addressing contemporary security challenges through innovative machine learning approaches. The publications show particular emphasis on intrusion detection systems, with numerous papers exploring deep learning methods, especially autoencoders, for identifying network anomalies. There's also a significant thread of research on misinformation analysis, where he applies graph theory and topic modeling to understand fake news propagation. His work often bridges multiple domains, such as combining cybersecurity with IoT systems or applying AI techniques to cloud security challenges. Professor Villano has supervised numerous graduate students through their research in cybersecurity and related fields. His research has been supported by various grants focused on cybersecurity, machine learning applications, and information systems security. His work has contributed to the development of practical security tools and methodologies that address real-world security challenges in networked systems. Professor Villano leads or participates in research groups focused on cybersecurity and machine learning applications. These teams work on developing advanced security solutions, creating benchmark datasets for security research, and investigating novel approaches to information security challenges. His laboratory environment emphasizes both theoretical research and practical implementation, with projects often resulting in open-source tools and publicly available datasets that benefit the broader security research community.

• Cybersecurity
• Intrusion Detection Systems
• Machine Learning for Security
• +۳ مورد دیگر

Vincenzo Deufemia is a Full Professor at the Department of Computer Science of the University of Salerno . His work focuses on cybersecurity, IoT security, data analytics, machine learning, and end-user development in smart environments. He has been actively involved in interdisciplinary research spanning database systems, human-computer interaction, and ethical AI applications. Key research interests include: Automation rule security and coherence Unsupervised learning for IoT interference detection End-user customization of smart systems Functional dependency discovery in data streams Social media analysis for geopolitical perception studies Recent work has explored leveraging generative AI for ethical phishing defense mechanisms and developing tools like EFESTO-5W for non-expert security rule specification. His contributions also include visualization frameworks for dependency analysis and spatial integrity constraints verification. No formal awards are explicitly listed in the provided material, though his extensive publication record indicates significant contributions. His research has practical applications in both academic and industrial contexts, particularly in improving end-user security awareness and system usability in smart environments.

• Cybersecurity
• IoT Security
• Data Analytics
• +۷ مورد دیگر

Chiara Epifanio is a researcher (INFO-01/A) at the University of Palermo , based in the Department of Mathematics and Computer Science . She holds regular office hours on Tuesdays 14:30–17:00 in Room 104, first floor, Via Archirafi 34, and can be reached at chiara.epifanio@unipa.it . Over the past fifteen years she has designed and taught a diverse portfolio of courses spanning Programming , Bioinformatics , Advanced Programming , Teaching Methodologies and Techniques for Computer Science and Pattern Discovery for Life Sciences . These offerings serve degree programmes in Mathematics, Computer Science, Statistics & Data Science, and the recently launched curricula in Data, Algorithms & Machine Intelligence and Computer Science & Artificial Intelligence . Her research lies at the intersection of combinatorics on words , string algorithms , and bioinformatics . She has made sustained contributions to the design of alignment-free distances for biological data, the theory of Sturmian words and their associated graphs, suffix automata tolerant to mismatches, and compact data structures such as linear-size suffix tries. The work repeatedly draws on deep results from formal language theory, automata theory, and discrete mathematics to solve practical problems in sequence analysis. Publications trend: From 2004 onward she has produced a steady stream of peer-reviewed articles that move from foundational combinatorial results on Sturmian words and critical factorization theorems toward application-driven studies on approximate string matching and genomic data mining. A marked acceleration is visible in the 2023 publications addressing k-Hamming and k-edit distances, reflecting current demands in large-scale biological data analytics. Scientific awards: None explicitly mentioned in the provided material. Advising & grants: No PhD or Master’s students are listed in the supplied pages, and no funded project descriptions are available. Laboratories & teams: While no specific lab is named, her teaching and research are embedded within the Department of Mathematics and Computer Science, which hosts groups in algorithms, discrete mathematics, and bioinformatics, and provides access to the university’s ATeN Center and other research infrastructures.

• Algorithms on strings
• Formal language theory
• Bioinformatics
• +۳ مورد دیگر

Giovanna Castellano is a Full Professor at the University of Bari Aldo Moro, Italy, where she serves in the Department of Computer Science. She coordinates the Computational Intelligence Laboratory (CILab) and has been active in academia since at least May 2015. Her extensive publication record with 354 publications demonstrates her significant contributions to the fields of Computational Intelligence, Computer Vision, and Explainable AI. Dr. Castellano earned her PhD in Computer Science from the University of Bari Aldo Moro between September 1988 and March 1993. Her academic journey has led her to become a prominent researcher in artificial intelligence applications across various domains including healthcare, education, and digital art analysis. Her research interests span a wide range of computational intelligence topics, with current focus on Computer Vision, Deep Learning, Explainable AI (XAI), and Fuzzy Systems. Dr. Castellano's work demonstrates a strong interdisciplinary approach, applying AI techniques to solve real-world problems in healthcare diagnostics, precision agriculture, educational analytics, and cultural heritage preservation. Her research consistently emphasizes model interpretability, recognizing the importance of transparent AI systems, particularly in critical domains like healthcare where trust in AI decisions is paramount. Analysis of her recent publications reveals a consistent trajectory toward more interpretable and explainable AI systems across multiple application domains. Her work spans medical imaging analysis, stress prediction in educational contexts, art analysis, and precision agriculture. A notable trend is the integration of large language models with computer vision techniques to enhance both performance and explainability of AI systems, particularly in medical and artistic contexts. Excellent Presentation Award at 1st International Conference on Soft Computing and Intelligent Systems 2002 Dr. Castellano leads the Computational Intelligence Laboratory (CILab) at the University of Bari, where she supervises research projects and collaborates with numerous researchers including Gabriella Casalino, Gennaro Vessio, and Gianluca Zaza. Her research group has secured various grants for projects in healthcare AI, educational data mining, and digital art analysis. The CILab research team works on diverse projects including medical diagnostics using AI, analysis of digital art collections, educational data mining, and precision agriculture applications. Their work often involves collaboration with healthcare institutions, cultural organizations, and agricultural technology companies, demonstrating the practical impact of their theoretical research.

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

Annalisa Appice is an Assistant Professor at the Department of Computer Science, University of Bari, Italy. She holds a Ph.D. in Computer Science from the same institution, with her thesis focusing on 'Learning Relational Model Trees'. Her international research experience includes positions at the University of Bristol (UK) and Jozef Stefan Institute (Slovenia). Her research spans: Knowledge discovery and data mining Big data analytics and stream data mining Spatio-temporal data mining and remote sensing analysis Network data analysis and process mining Artificial Intelligence applications in cybersecurity and environmental monitoring She develops innovative machine learning approaches including deep neural networks, vision transformers, and ensemble methods for applications ranging from forest health monitoring to cybersecurity threat detection. Appice has significant leadership experience in the academic community, having served as Program co-Chair for major conferences including DS 2020, ECML-PKDD 2015, and ISMIS 2017. She regularly contributes to program committees of premier conferences such as AAAI, IEEE ICDM, KDD, ICML, and IJCAI. As an editorial board member for the Journal of Intelligent Information Systems, she oversees publications in intelligent information systems. She has also organized six international workshops and guest-edited three special journal issues.

• Knowledge Discovery
• Data Mining
• Big Data Analytics
• +۷ مورد دیگر

Paolo Mignone is an Assistant Professor in the Department of Computer Science at the University of Bari, Italy. His academic career includes serving as a Research Fellow at the same department from December 2019 to December 2020, followed by his current position as Assistant Professor since December 2020. He earned his PhD in Computer Science from the University of Bari in February 2020. Dr. Mignone's research spans multiple domains within computer science and data analytics. His primary interests include Big Data Analytics, Bioinformatics, Data Mining and Knowledge Discovery, Link Prediction, and Transfer Learning. He also specializes in Positive Unlabeled Learning, Anomaly Detection, Physical Threat Intelligence, Handwriting Signature Verification, Cyber Security, and Social Network Analysis. His work demonstrates a strong interdisciplinary approach, connecting computer science methodologies with applications in bioinformatics, security, and environmental monitoring. His publication record shows a clear trend toward distributed computing approaches for transfer learning and anomaly detection, with significant contributions to bioinformatics applications. Mignone frequently collaborates with Michelangelo Ceci, Gianvito Pio, and other researchers at the University of Bari. His work bridges theoretical machine learning advancements with practical applications in areas like gene regulatory network reconstruction, physical threat intelligence, and public transport systems. As an active member of the academic community, Mignone serves on program committees for major conferences including IJCAI-PRICAI (2020-2025), ECML-PKDD (2020-2025), ECAI (2024-2025), and CIKM (2024-2025). He has also been involved in organizing events such as the International Spring School on Deep Learning (DeepLearn 2023) and the New Frontiers in Mining Complex Patterns workshops at ECML-PKDD (2022-2025). Dr. Mignone contributes significantly to academic service through extensive reviewing activities for top journals including Bioinformatics, IEEE Transactions on Human-Machine Systems, Machine Learning, and numerous IEEE and ACM publications. He teaches courses ranging from Big Data and Data Mining to Cyber Security and Database Systems at both undergraduate and graduate levels. His research is conducted within the KDDE lab at the University of Bari, where he works on projects related to STEAL (Source TargEt ALignment) and BioSfer (Biological tranSfer learning) software frameworks. His work demonstrates strong connections between theoretical machine learning advancements and practical applications across multiple domains.

• Big Data Analytics
• Bioinformatics
• Data Mining
• +۹ مورد دیگر

Vincenzo Pasquadibisceglie is an Assistant Professor (non tenure) at the Department of Computer Science, University of Bari Aldo Moro. He holds a PhD and is affiliated with the Knowledge Discovery and Data Engineering research group within the LACAM Laboratory. His research focuses on Data Mining, Process Mining, Deep Learning, and their applications in predictive analytics and business process optimization. Key research areas include: Predictive process monitoring using machine learning and computer vision techniques Adaptive models for concept drift in streaming data Explainable AI approaches for customer behavior prediction Integration of LLMs with process event logs Publications emphasize innovative methods like GANDALF (LLM-based environmental analysis), PROPHET (graph neural networks for process prediction), and DARWIN (online deep learning for process drift). Recent work addresses retail analytics (CENTAURO, TSUNAMI) and healthcare applications (contactless vital sign monitoring). His contributions bridge theoretical advancements with practical implementations, including the STARDUST process mining framework and the FOX neuro-fuzzy model. Current work explores LLM applications in healthcare and environmental monitoring.

• Data Mining
• Knowledge Discovery
• Process Mining
• +۳ مورد دیگر

Roberto Corizzo is a Research Fellow at the Department of Computer Science, University of Bari, Italy. He holds a Ph.D. and is affiliated with the LACAM Laboratory. His research focuses on big data analytics, data mining, predictive modeling for sensor networks, energy prediction in smart grids, and anomaly detection. He has conducted research internships at INESC TEC (Portugal) under Prof. João Gama and at the American University (Washington D.C.) under Prof. Nathalie Japkowicz. Key research interests include: Big Data Analytics Predictive Modeling for Sensor Networks Energy Prediction in Smart Grids Anomaly Detection in Dynamic Systems His work spans over 15 publications from 2014–2019, addressing challenges in data stream analysis, renewable energy forecasting, and distributed computing. Notable contributions include Spark-GHSOM for large-scale data clustering and DENCAST for multi-target regression. He has organized conference challenges (e.g., ECML/PKDD Discovery Challenges) and contributed to projects like VIPOC for renewable energy prediction. No scientific awards are explicitly listed. He collaborates with academic institutions globally and participates in interdisciplinary projects involving energy systems and semantic services in big data platforms.

• Big Data Analytics
• Data Mining
• Predictive Modeling
• +۳ مورد دیگر

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (2023–present), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Free University of Bozen-Bolzano (2018) and a Diploma in Media Informatics from Ludwig Maximilian University of Munich (2014). Previously, he worked as a Research Associate at King's College London (2021–2023) and a Research Fellow at the University of Warwick (2018–2021). His academic journey includes roles as a Meta-Reviewer for NeurIPS and ICDT Proceedings Chair (2024), with extensive service as a reviewer for top conferences like ICML, NeurIPS, ICLR, and journals such as TKDE and IEEE T-IFS. Research Interests: Focuses on algorithms and data structures for managing large/sensitive datasets, including differential privacy, random sampling (e.g., Hidden Shuffle Method), and multidimensional data summarization (e.g., DigitHist). His work bridges theoretical guarantees with practical applications in data management and machine learning. Notable contributions include privacy-preserving top-k selection, efficient sampling over joins, and error-bounded data summaries. Publications: Over 15 peer-reviewed articles in top venues including SIGMOD, ICDE, EDBT, AISTATS, and PVLDB. Key works include 'Streaming Weighted Sampling over Join Queries' (EDBT'23), 'Sequential Random Sampling Revisited' (AISTATS'21), and 'Sparse Prefix Sums' (Information Systems'19, which won an ADBIS award). Grants/Awards: Received the ADBIS'17 Award for Sparse Prefix Sums. Active in organizing conferences and mentoring early-career researchers through reviewing roles. Current projects include a PhD studentship on privacy-preserving algorithms for medical data sharing. Labs/Teams: Leads the Privacy-Preserving Algorithms initiative at Queen Mary, collaborating with international research networks in database systems and data privacy.

• Data Management
• Database Systems
• Differential Privacy
• +۵ مورد دیگر