جستجوی استاد راهنمای دانشگاهی

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +۷ 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.

Nicole Novielliمشاهده پروفایل

دانشیار

• Affective Computing
• Software Engineering
• Natural Language Processing
• +۵ مورد دیگر

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.

• Artificial Intelligence
• Semantic Web
• Ontology Engineering
• +۲ مورد دیگر

Asunción Gómez Pérez is a Spanish computer scientist and Full Professor at the Technical University of Madrid (UPM) . She currently serves as Vice-Rector for Research, Innovation and Doctoral Studies at UPM and holds a seat at the Real Academia Española . She has authored over 300 publications and accumulated 20,000 citations. Education : PhD in Computer Science (UPM, 1993), MBA (Comillas Pontifical University) Leadership Roles : Director of the Department of Artificial Intelligence (2008–2016), Academic Director of AI Master’s/PhD programs (2009–2016), Executive Director of UPM’s Artificial Intelligence Lab (1995–1998) Her research focuses on Semantic Web and Ontology Engineering , with applications in knowledge representation, machine-machine communication, and multilingual data integration. She pioneered methods for ontology validation, metadata licensing, and AI-driven social inclusion. Key publication trends include: Ontology evaluation frameworks (e.g., OOPS!) Linked Data quality models and validation tools Multilingual and cross-lingual AI applications Interoperability solutions for smart cities and healthcare Machine Learning for social exclusion prediction Ontology-driven library and lexicography systems Scientific Awards Fellow of the European Academy of Sciences Ada Byron Prize She has led projects like the NeOn Methodology for ontology development and contributed to the European framework for linked data rights (LD Terms). Her work bridges theoretical research with practical implementations in AI and Semantic Technologies.

• Applied Geophysics
• Glaciology
• Geophysical Data Integration
• +۳ مورد دیگر

Alberto Godio is a Full Professor at the Politecnico di Torino, affiliated with the Department of Environmental, Land and Infrastructure Engineering (DIATI). He coordinates Latin America relations under the University Strategic Plan and is a member of the Interdepartmental Center Photonext for Applied Photonics. His research focuses on Applied Geophysics, Geophysical Data Integration, and Glaciology. He graduated in Mining Engineering (1988) and earned a PhD in Underground Resources Engineering (1993). He has been an Associate Professor (2005-2024) and Full Professor (2024-present) at PoliTO, leading projects funded by EU (FP7, LIFE, Horizon), MIUR (PRIN, FIRB), and regional bodies. His recent publications explore geophysical methods for subsurface modeling, glacial systems, and environmental remediation, with keywords spanning Geophysics, Hydrology, and Climate Science. His work emphasizes ground-penetrating radar, seismic noise analysis, and hybrid modeling techniques. Scientific awards include the Best Paper Award at the Near Surface Geoscience Conference (2008). He has supervised PhD students on fiber optic sensors and GPR optimization, advised projects on digital twins, and led EU-funded research on biogas enhancement in landfills.

Gemma Catolinoمشاهده پروفایل

استادیار

• Software Engineering
• Empirical Software Engineering
• Human Factors in Software Development
• +۱۰ مورد دیگر

Gemma Catolino is an Assistant Professor at the Department of Computer Science, University of Salerno, and affiliated with the Software Engineering (SeSa) Lab. She has also served as an Assistant Professor at Tilburg University and Eindhoven University of Technology through the Jheronimus Academy of Data Science from September 2022 to December 2023, and previously as a Postdoctoral Researcher at Delft University of Technology and Tilburg/Eindhoven institutions. PhD in Computer Science, University of Salerno (2020), supervised by Prof. Filomena Ferrucci MSc in Management and Information Technology, University of Salerno (2016, magna cum laude) BSc in Computer Science, University of Molise (2014) Her research centers on empirical software engineering, focusing on both technical and social aspects affecting software development. Key areas include code smells, defect prediction, testability, changeability, and the emerging concept of “Community Smells”—social dysfunctions in developer teams. She investigates how human factors, team diversity (especially gender), and developer experience influence software quality and maintenance effort, often using mining software repositories and machine learning techniques. Her recent publications span high-impact journals and conferences such as IEEE TSE, EMSE, JSS, ICSE, and ICSME, with a strong trend toward integrating social and technical metrics for just-in-time defect prediction in mobile applications, analyzing community dynamics, and applying software quality metrics to cybersecurity contexts like dark web analysis. She has also contributed to MLOps and serverless computing. She has received several honors including a DEI research grant (2020), Best Technical Paper at BENEVOL 2019, first and second place in ACM Student Research Competitions (2018, 2017), and the Best Master Thesis award from the Italian Software Metrics Association (2017). Gemma Catolino has been actively engaged in academic service as a referee for top journals like IEEE TSE, EMSE, JSS, and IST, guest editor for special issues, and program/organizing committee member for major conferences including ICSE, MSR, SANER, and MobileSoft, where she served as Program Co-Chair in 2022. She has also contributed as a teaching assistant, lecturer, and course coordinator in machine learning and software engineering courses. She leads and contributes to research projects involving international collaborations, particularly with researchers such as Prof. Filomena Ferrucci, Prof. Andy Zaidman, Prof. Willem-Jam van den Heuvel, and Prof. Alexander Serebrenik. Her work bridges empirical software engineering with practical tool development and socio-technical analysis, positioning her at the forefront of modern software engineering research.

• Software Engineering
• Energy-Efficient Systems
• Software Architecture
• +۴ مورد دیگر

Ivano Malavolta is an Associate Professor in Software Engineering at the University of Urbino , focusing on energy-efficient software, software architecture, and model-driven engineering (MDE). His research bridges robotics, mobile systems, and sustainability, emphasizing empirical methods and industrial applicability. Key Research Areas: Energy-efficient software, microservices, robotics systems, collaborative modeling, mobile application performance. Affiliations: Department of Software Engineering, University of Urbino. His recent publications highlight empirical studies on energy consumption patterns in robotics, mobile apps, and AI systems. He has contributed to frameworks like the Green Software Measurement Model (GSMM) and tools for architectural technical debt analysis. Notably, he has co-authored 15+ peer-reviewed articles in venues such as Journal of Systems and Software , Information and Software Technology , and ACM/IEEE conferences . His work often involves cross-platform comparisons (e.g., Electron vs. Web, Pandas vs. Polars) and systematic mappings of software engineering practices.

Sandro Luigi Fioreمشاهده پروفایل

دانشیار

• Data Science
• Big Data
• Scientific Data Management
• +۱۳ مورد دیگر

Sandro Luigi Fiore is an Associate Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy. He has held academic positions at the University of Salento and visiting scientist roles at the University of Chicago and Lawrence Livermore National Laboratory. His research integrates data science, big data, and high-performance computing with climate informatics and open science. Education: Ph.D. in Innovative Materials and Technologies, University of Lecce, 2004 Master of Science in Computer Engineering (with honors), University of Lecce, 2001 His research interests span Data Science, Big Data, Scientific Data Management, Artificial Intelligence, and Distributed/Cloud/Parallel Computing , with a strong application focus on Climate Change and Open Science . He develops FAIR-enabled data analytics solutions and contributes to large-scale data infrastructures such as ESGF, EOSC-hub, and INDIGO-DataCloud. His work emphasizes provenance, reproducibility, and interoperability in scientific computing. The articles reflect a consistent trajectory in large-scale scientific data systems , evolving from Grid-DBMS and parallel computing to modern applications in climate informatics, AI, and FAIR data. Key themes include distributed data management, middleware, exascale software, and reproducible workflows in HPC environments. Scientific Awards: Earth System Grid Federation Team Award (2017) UNIDATA Community Equipment Award (2011) Best Student Paper Award, ITCC2003 Fiore has advised on and led multiple national and European projects including EOSC-hub, IS-ENES, and BARRACUDA (RDA-Europe3). He actively supports the adoption of FAIR data principles in scientific repositories, advising on data policies, architecture, provenance, and interoperability. He is a member of the FAIR Champions group and serves on the Advisory Board of FAIRsFAIR. He is involved in key research teams and projects such as the Earth System Grid Federation (ESGF) , Globus Lab (University of Chicago) , and PCMDI/LLNL . His work contributes to climate model intercomparison (CMIP5/6) and the development of next-generation data infrastructure for open science.

Filippo Lanubileمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence
• Human-Computer Interaction
• +۲ مورد دیگر

Filippo Lanubile is a Full Professor and Head of the Department of Computer Science at the University of Bari, where he leads the Collaborative Development Research Group (Collab). He co-founded the academic spin-off PeoplewareAI and previously served as a research associate at the University of Maryland. His research spans human factors in software engineering, AI-augmented development, engineering of AI-enabled systems, and health informatics. Major publications focus on MLOps, emotion recognition in development, and LLM applications in software engineering. Lanubile received the NASA Group Achievement Award (1996), IBM Eclipse Innovation Award (2006), IBM Faculty Award (2008), and Microsoft Research Software Engineering Innovation Foundation Award (2011). He chaired the ACM/IEEE ESEM symposium (2020-2021) and IEEE Software Advisory Board (2020-2023).

Barbara Russoمشاهده پروفایل

استاد

• Machine Learning
• Software Engineering
• Systems Reliability
• +۴ مورد دیگر

Barbara Russo is a Full Professor at the Faculty of Computer Science of the Free University of Bozen-Bolzano, Italy. She is a member of the International Software Engineering Research Network (ISERN) and has held visiting researcher positions at the Max-Planck Institute for Mathematics (Germany) and the University of Liverpool (UK). Her research focuses on Machine Learning in Software Engineering, Software Reliability/Testing, and Systems Engineering. She founded and coordinated the Software and Systems Engineering (SwSE) research group (2014–2017) and is involved in initiatives like the European Masters Program in Software Engineering (EMSE). Awarded the 2007 Best Public-Private Collaboration Award for establishing a dual BSc program in Computer Science. Established a makerspace (now FabLab) at her faculty and contributed to projects like the Euregio Trustworthy AI Lab. Research spans DevOps processes, embedded systems vulnerability, and collaborative AI assurance. Her work emphasizes empirical software engineering and practical industry collaboration, with notable contributions to scalability testing, performance modeling, and vulnerability detection.

• Explainable AI
• Personal Data Mining
• Clustering
• +۷ مورد دیگر

Riccardo Guidotti is an Assistant Professor (RTD-B) at the Department of Computer Science, University of Pisa, and a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group with the Information Science and Technology Institute of the National Research Council (ISTI-CNR) in Pisa, Italy. His research spans explainable AI, personal data mining, clustering, and transactional data analysis. PhD in Computer Science, University of Pisa Master's Degree in Computer Science, University of Pisa (110/110 cum laude) Bachelor's Degree in Computer Science, University of Pisa (110/110 cum laude) His research interests include Explainable AI , Personal Data Mining , Clustering , and Transactional Data Analysis . He focuses on making AI systems interpretable and understandable, especially through local and global explanation techniques applied to behavioral and mobility data. His work bridges theoretical models with real-world applications in retail, mobility, and social networks. The recent publications highlight a strong trend in explainability of black-box models , with emphasis on rule-based and exemplar-driven methods. His work extends to temporal pattern discovery , privacy risk assessment , and software engineering analytics , demonstrating interdisciplinary reach across AI, data science, and human-centric systems. Scientific awards received: IBM Fellowship Award (2014) ISTI Young Researcher Award (2016, 2017, 2018, 2019) Next Generation Data Scientist Award (NGDS 2018) He has contributed to teaching courses such as Data Mining, Logic Programming, and Advanced Data Mining topics. He has been involved in major EU-funded projects including SoBigData++, XAI, AI4EU, and HumanE-AI-Net, where he contributes to advancing explainable and ethical AI. He collaborates extensively within the KDDLab, focusing on knowledge discovery and data-driven decision-making. His lab work emphasizes scalable, interpretable, and socially aware data mining systems.

Luigi Ceruloمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Genomics
• Machine Learning
• +۳ مورد دیگر

Luigi Cerulo is an Associate Professor in Computer Science at the Department of Science and Technology (DST) , University of Sannio in Benevento. He co-directs the bioinformatics research group with Francesco Napolitano, focusing on integrating computational methods into life sciences. His teaching includes advanced topics like Computational Genomics and Statistics and Bioinformatics for Master's programs in Biotechnology and Biology. Academic Rank: Associate Professor Department: Science and Technology, University of Sannio Email: lcerulo@unisannio.it Research Interests: Driven by cross-disciplinary innovation, his work bridges machine learning and bioinformatics. Key areas include reverse engineering gene regulatory networks using deep graph neural networks, predicting ligand-receptor interactions via graph autoencoders, and exploring non-coding RNA functions with self-supervised learning. His team also investigates drug synergy prediction and AI applications in biological data. Recent Publications: His 15 most recent works span 2022 to 2007, emphasizing machine learning in gene network reconstruction (Chang et al., 2020), deep learning for RNA classification (Noviello et al., 2020), and foundational contributions to software engineering tools (Canfora et al., 2007-2008). Research Infrastructure: The group utilizes high-performance systems including a Superdome Flex server (224 CPU cores, 1.5 TB RAM) and a GPU cluster at Biogem Institute. Advising and Grants: He mentors PhD students in bioinformatics and leads projects such as GENOMA E SALUTE (EU-funded) and Non-Coding RNA Explosion (Ministry of Research grant). Collaborations span institutions like the University of Naples and Biogem.

• Algorithm Engineering
• Health IT
• Information Systems
• +۸ مورد دیگر

Umberto Nanni is a full Professor of Information Management Systems at the University of Rome "La Sapienza" since November 2005. He has held significant leadership roles including Director of the Research Center for Distance Learning and Technologies for Learning at Telematic University Unitelma Sapienza (2015-2022) and President of the Area Council in Information Engineering at the Latina location of University of Rome "La Sapienza" (2014-2020). Professor Nanni's research spans multiple domains with primary focus on Algorithm Engineering (particularly path algorithms and combinatorial problems), Health IT, Information Systems (with applications in Transport and Logistics, Cultural Heritage), and Technology Enhanced Learning. His work integrates Big Data, Graph and Text mining, Incremental Algorithms, Dynamic Data Structures, and Internet of Things technologies to address complex challenges in these areas. His recent publications demonstrate a strong trend toward biomedical applications, particularly in biospecimen management and digital twins for precision medicine, while maintaining his foundational work in algorithm design and educational technology. The research shows increasing interdisciplinary collaboration, especially between computer science and healthcare domains, with notable publications in Cancer Genomics & Proteomics and other high-impact journals. He has advised over 400 undergraduate and master's theses along with 2 PhD students He has led numerous significant research projects including "Artificial Intelligence algorithms to track and detect Covid-19 vaccine-related infodemic on social media" (2023-2025) He coordinated the "ADCATER - Advanced Digital Solutions for Professional Food and Nutrition Catering Service" project (2021-2023) He served as Principal Investigator for the eLF-eLearning Fitness project (2011-2014) with 19 partners and 42 associated partners Professor Nanni co-founded and was instrumental in developing SPRECware, software tools for Standard PREanalytical Code labeling to improve biospecimen management. His work has resulted in over 100 scientific papers with an h-index of 27, demonstrating substantial impact across computer science, healthcare informatics, and educational technology domains.

Fabio Calefatoمشاهده پروفایل

دانشیار

• Collaborative Software Engineering
• Social Software Engineering
• Mining Software Repositories
• +۷ مورد دیگر

Fabio Calefato is an Associate Professor at the Department of Computer Science, University of Bari "A. Moro" since 2022, holding national habilitation as Full Professor in academic disciplines 01/B1 (since Dec. 2023) and 09/H1 (since Mar. 2025). He serves as CEO & co-founder of PeoplewareAI academic spin-off (2021-present), Rector's delegate of GARR network (2023-present), and Director's delegate for student traineeships (2023-present). Previously, he was Assistant Professor at the Department of Computer Science (2019-22) and at the Jonian Department, Taranto (2015-19), and conducted post-doctoral research at the Department of Computer Science (2007-08; 2010-14). Dr. Calefato received his Ph.D. in Computer Science in May 2007 from the University of Bari with a thesis titled "Supporting Synchronous Communication in Distributed Software Teams," which was listed on SIGSOFT Selected Ph.D. Dissertations in Software Engineering and published by VDM Publishing. He also holds an MSc in Computer Science from the University of Bari (2002). His research focuses on collaborative and social software engineering, mining software repositories and technical Q&A sites, SE4AI, psychometric and sentiment analyses from communication channels in the social programmer ecosystem, collaboration in online creative communities, community-driven knowledge sharing, and computer-mediated communication theories and tools. His work bridges software engineering with social psychology and AI, particularly examining how developers interact, collaborate, and express emotions in online platforms. Analysis of his recent publications reveals a strong trajectory toward AI integration in software engineering practices, with increasing focus on AutoML applications, sentiment analysis in developer communities, and the impact of generative AI on open-source development. His research spans both theoretical foundations and practical tools, with several software implementations including EMTk (Emotion Mining Toolkit), Apache-Big5 (developer personality profiler), and Pynblint (Jupyter notebook quality assurance). Best Paper Award at ICGSE'18 SIGSOFT Selected Ph.D. Dissertations in Software Engineering Dr. Calefato actively advises doctoral students, with Kelly Azevedo Borges Leal (current, researching AutoML for Software Engineering), Luigi Quaranta (graduated 2022, thesis on collaboration around computational notebooks), and Giuseppe Iaffaldano (graduated 2020, thesis on dynamics of online collaborative communities). His research has been supported through various grants enabling international collaborations with institutions including Carnegie Mellon University, University of Victoria, and TU Wien. He leads the COLLAB research group at the University of Bari, which develops tools and conducts empirical studies on collaborative software development. The group maintains several active GitHub projects including Apache-Big5, mini-IPIP test, EMTk, and pySOreputation, alongside experimental datasets used in multiple published studies. His academic spin-off PeoplewareAI focuses on applying AI to human aspects of software development.

Fabrizio Fornariمشاهده پروفایل

استادیار

• Internet of Things (IoT)
• Digital Twins
• Business Process Management (BPMN)
• +۶ مورد دیگر

Fabrizio Fornari is an Assistant Professor in Computer Science at the University of Camerino (Italy), focusing on cutting-edge technologies like Internet of Things (IoT), Digital Twins, and Business Process Management (BPMN). His work bridges theoretical modeling with practical implementations, emphasizing formal verification and modern software development practices. Research Pillars: IoT programming paradigms (Fluidware, FloWare), Digital Twin applications for business processes, and formal BPMN verification. Technical Contributions: Development of model-driven frameworks (X-IoT, FloBP), 3D simulation tools for IoT platforms, and BPMN analysis utilities (BPMN Inspector, BProVe). Domain Applications: Earthquake response systems (SAFE), intelligent transportation data flows, and educational IoT test environments. Methodological Focus: Community detection algorithms for routine extraction, CI/CD optimization for databases, and systematic literature reviews on IoT-aware BPMN modeling. Collaborative Efforts: Active involvement in MIDas4CS workshops and repository platforms for process model sharing (RePROSitory).

Carmine Gravinoمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence
• Cybersecurity
• +۴ مورد دیگر

Carmine Gravino is a Full Professor at the Department of Computer Science at the University of Salerno. His research focuses on software engineering, artificial intelligence (AI), and cybersecurity, with notable contributions to requirements engineering, functional size measurement, and AI-driven educational technologies. He leads initiatives in metaverse applications for education (e.g., SENEM) and has pioneered work on blockchain-based security in digital learning environments. His academic journey includes extensive exploration of AI in healthcare (e.g., diabetes prediction models) and cybersecurity methodologies. He actively contributes to international collaborations via Erasmus+ programs, fostering educational exchanges and research partnerships in software engineering and emerging technologies. Gravino’s publications emphasize practical solutions like RECOVER (requirements generation from stakeholder conversations) and Echo (use case quality enhancement via LLMs). His work bridges theoretical advancements with real-world applications, such as green computing optimizations using GPUs and model-driven development frameworks for web applications. He maintains a strong presence in academic service, overseeing teaching, research, and laboratory activities. His research agenda includes advancing explainable AI, ethical guidelines for emotion recognition systems, and fostering innovation in software quality assurance.