Find Academic Supervisors

Katja HeinischView profile

Researcher

• international macroeconomics
• applied time series econometrics
• short-term forecasting
• +3 more

Katja Heinisch serves as Head of the Research Group "Econometric Tools for Macroeconomic Forecasting and Simulation" at the Halle Institute for Economic Research (IWH) since January 2013, and has been a member of the Department of Macroeconomics since September 2009. Her work focuses on developing advanced econometric methodologies for economic forecasting with practical applications for policymakers. Dr. Heinisch earned her diploma from Chemnitz University of Technology and University of Strasbourg, followed by a PhD from Osnabrück University. Her international research experience includes significant positions at the European Central Bank (ECB) and the International Monetary Fund (IMF), which have shaped her expertise in macroeconomic analysis across different institutional contexts. Her research program centers on international macroeconomics, applied time series econometrics, short-term forecasting, and macroeconometric modeling. A distinctive feature of her work is the rigorous evaluation of forecast accuracy, with particular attention to how assumption errors affect prediction quality. She has developed expertise in advanced statistical techniques including partial linear instrumental variable models and double machine learning approaches to address methodological challenges in forecast evaluation. Analysis of her recent publications reveals a consistent focus on improving forecasting techniques for the German economy and the European Union. Her work demonstrates sophisticated methodological approaches while maintaining strong policy relevance, particularly in evaluating EU Commission GDP forecasts and developing tools like the IWH forecasting dashboard. Her research on economic sentiment analysis has provided novel methods for disentangling private information from public knowledge in survey data, enhancing the utility of consumer surveys for economic forecasting. Developed advanced methods for forecast evaluation using machine learning techniques Created tools for real-time economic analysis and forecast comparison Contributed significantly to understanding the relationship between assumption errors and forecast accuracy Advanced methodologies for economic sentiment analysis in forecasting contexts Dr. Heinisch's leadership of her research group at IWH positions her at the forefront of methodological innovation in economic forecasting, with her work bridging theoretical econometrics and practical policy applications.

• Conversational AI
• Natural Language Processing (NLP)
• Human-Robot Interaction (HRI)
• +5 more

Dr Yanchao Yu is a Lecturer at the School of Computing Engineering and the Built Environment of Edinburgh Napier University. He earned a BSc from Beijing Information Science and Technology University, an MSc in Software Engineering (Distinction), and a PhD in Artificial Intelligence from Heriot-Watt University (UK). PhD in Artificial Intelligence (Heriot-Watt University) MSc in Software Engineering (Distinction) BSc in Computer Science His research focuses on building smart agents and robots that assist humans in daily tasks through natural conversations. Key areas include: Natural Language Understanding (NLU), Generation (NLG), and Dialogue Management (DM) Visually grounded language learning Human-Robot Interaction (HRI) Machine learning for interactive systems Recent publications emphasize context-aware dialogue systems, visual scene understanding, and ethical challenges in HRI. He leads the EPSRC-funded project Learning from Collaborative Storytelling (2025-2028, £439,581) to enhance robot perception through interactive storytelling. Director of Studies for Lewis Watson's PhD on AI-Driven Conversational Storytelling Co-supervisor for multiple PhD projects including medical training robotics and machine unlearning Previously worked at Heriot-Watt's Interaction Lab (postdoc) and industry roles at Voysis (acquired by Apple) and Alana AI.

• Internet of Things
• Cybersecurity
• Smart Cities
• +4 more

Dr. Oluwaseun Bamgboye is a Lecturer at Edinburgh Napier University's School of Computing Engineering and the Built Environment. His research focuses on Internet of Things (IoT) , Cybersecurity , and Semantic Technologies , with applications in Smart Cities, Data Quality, and Sensor Networks. He actively publishes in IoT data validation , XAI for cyberattack detection , and real-time analytics . Academic Affiliation: School of Computing Engineering and the Built Environment, Edinburgh Napier University Research Interests: Dr. Bamgboye's work bridges semantic modeling and machine learning to address data consistency in Smart Spaces, enhance cybersecurity through explainable AI (XAI) , and develop IoT-driven decision-making systems . His projects often integrate RDF stream processing and rule-based reasoning for real-time data quality assurance. Publication Trends: His recent articles (2023-2025) emphasize IoT network security , XAI frameworks , and smart city infrastructure . Earlier works (2015-2019) focus on distributed systems fault tolerance , sensor data uncertainty , and web accessibility tools . Key methodologies include neurosymbolic learning , ensemble models , and semantic stream management . Supervision: Dr. Bamgboye supervises PhD projects such as: Enhanced deep learning and semantic-based predictive analytics for reactive IoT streaming data (Talatu Adamu, 2024–present) AI-driven and IoT-assisted explainable smart banking (Fatima Mumtaz, 2024–present) Semantic models for sensor data uncertainty in smart systems (Tawanda Nyakudya, 2022–present) Web accessibility auditing (Collins Ovie, 2023–present)

• Textile Recycling
• Fiber Science
• Composite Materials
• +5 more

Nawar Kadi is a Professor of Textile Technology at the University of Borås, Sweden, affiliated with the Department of Textile Technology within the Faculty of Textiles, Engineering and Business. He leads research in sustainable textile recycling, fiber science, and composite materials. Previously, he worked at the University of Aleppo (2002–2016) and earned his PhD in 2001 from the University of Metz (France). His teaching focuses on yarn, non-woven fabrics, and weaving technologies. Research interests include textile recycling innovations, hemp fiber processing using deep eutectic solvents, and sustainable materials development. He supervises doctoral students in these areas and collaborates on projects funded by Vinnova and the Knowledge Foundation. Recent work emphasizes eco-friendly degumming techniques for hemp fibers, machine learning applications in fiber analysis, and advanced composites from biopolymers. His contributions span over 50 peer-reviewed publications, addressing topics from fiber structure optimization to circular economy strategies in textiles. Key grants include Vinnova Project 2021-03719 and Knowledge Foundation Grant 20210067. He advises on projects at the intersection of material science and textile engineering, advocating for sustainable industrial practices.

• Neuroimaging
• Reproducible Research
• Computational Neuroscience
• +3 more

Dorota Jarecka is a Research Scientist at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology (MIT). Her work focuses on developing open-source software tools and frameworks to enhance reproducibility and scalability in neuroimaging research. She is a core contributor to initiatives like BIDS Apps, NiMARE, and Pydra, which aim to standardize and streamline neuroimaging data analysis workflows. Her research interests span neuroinformatics, reproducible research practices, computational neuroscience, and the development of scalable data management solutions. She actively contributes to consortia such as the NMIND consortium and the BRAIN Initiative Cell Census Network, promoting collaborative approaches to neuroimaging challenges. Jarecka’s publications highlight her expertise in large-scale neuroimaging analysis, meta-analysis techniques, and the integration of open science tools like DataLad and Datalad. She emphasizes the importance of human-in-the-loop systems and agentic frameworks (e.g., STRUCTSENSE) to improve structured information extraction in scientific workflows. Her work has advanced reproducibility through ontologies and provenance tracking (e.g., NIDM Experiment) and has addressed technical challenges such as software variability across operating systems. She is also involved in educational efforts, including Software Carpentry workshops on version control with Git. Jarecka’s contributions bridge computational methods with neuroscience, fostering collaboration between researchers, developers, and institutions to tackle complex neuroimaging and atmospheric science problems.

• Learning Technologies
• Digital Education
• Educational Technology
• +3 more

Demetrios Sampson is a Professor of Digital Systems for Learning and Education at the University of Piraeus, Greece, since 2003. He holds leadership roles including Director of the MSc Program on Digital Learning and Director of the Research Laboratory Digital Systems in Learning and Education. Previously, he was a Professor of Learning Technologies and Director of Research at Curtin University, Australia (2015–2017), and a Senior Researcher at the Centre of Research and Technology–Hellas (2000–2003). He has been a Visiting Professor at 8 universities across 4 continents, including Peking University, University of Hong Kong, and University of Tartu. His research focuses on Learning Technologies and Digital Education, recognized globally, including being named in Stanford’s World’s Top 2% Scientists. He has authored/co-authored 314 publications (books, journal articles, chapters, conference papers), edited 22 books, and received 10 Best Paper Awards. He has supervised over 200 students and led 75+ research projects with €18M funding. Sampson co-authored groundbreaking works like Educational Data Literacy (Springer, 2022) and pioneered the first MOOC on Educational Data Analytics via edX, attracting 30,000+ participants globally. He initiated the open-access Educational Technology & Society Journal , now #4 in Google Scholar rankings. Leadership roles include Chair of IEEE Technical Committee on Learning Technologies (2008–2011, 2015–2018) and Editor-in-Chief of major journals. Awards include the IEEE Computer Society Distinguished Service Award and Golden Nikola Tesla Chain Award. He holds a PhD in Electronic Systems Engineering from the University of Essex (1995) and a Diploma in Electrical Engineering from Democritus University of Thrace (1989). Key contributions include promoting educational data literacy through the Learn2Analyse initiative, leading global conferences like IEEE International Conference on Advanced Learning Technologies, and advancing frameworks for teaching analytics, flipped classrooms, and MOOC design. His work bridges technology, pedagogy, and policy, shaping modern educational systems worldwide.

• Sentiment Analysis
• Multi-modal Analysis
• Data Science
• +3 more

Jichunyang Li is an Associate Lecturer at the School of Physics, Mathematics and Computing, The University of Western Australia. His research focuses on sentiment analysis, multi-modal data processing, and knowledge graph construction through innovative human-LLM collaboration methods. He holds a Master of Business Analytics (Distinction) from UWA Business School (2020-2021) and a Bachelor of Commerce with Honours in Management (2019-2019). His work bridges computer science and data science, emphasizing practical applications in opinion mining and disagreement detection. Recent contributions include advancements in multimodal sentiment analysis and scalable knowledge graph frameworks. He was part of a team honored with the 2023 Students Choice Award for enhancing learning experiences through academic initiatives. Collaborative research has explored interdisciplinary topics such as affective computing and heterogeneous document processing. Publications appear in top venues including the ACM Web Conference and IEEE Transactions on Affective Computing.

Nicolas BoulleView profile

Assistant Professor

• Numerical Analysis
• Numerical and Computational Mathematics
• Knowledge Representation
• +4 more

Nicolas Boulle is an Assistant Professor in Applied Mathematics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on the intersection of numerical analysis and deep learning, particularly in discovering mathematical models (e.g., partial differential equations) from data and developing theoretically grounded numerical techniques. Education: DPhil (PhD) in Numerical Analysis from the University of Oxford (2018–2022) Research interests include numerical analysis, operator learning, machine learning, and their applications in solving complex mathematical problems. His work emphasizes data-driven methods for Green’s functions and the development of rational neural networks for enhanced accuracy in deep learning. Recent publications explore topics like dynamic mode decomposition, Koopman operators, and large language models' behavior. He advises PhD students and visiting researchers on projects involving operator learning and neural networks. Boulle contributes to open-source projects such as GreenLearning (for PDE Green’s functions) and RationalNets (rational activation functions in neural networks), hosted on GitHub.

• Medical Physics
• Radiation Oncology
• Machine Learning in Medical Physics
• +2 more

Jonas Fontenot serves as President and Chief Executive Officer of Mary Bird Perkins Cancer Center (2023-present), following roles as Chief Operating Officer (2017-2022) and Chief of Physics (2015-2022). He maintains an academic affiliation as Adjunct Professor in Louisiana State University's Department of Physics and Astronomy since 2008. Education: MBA, Louisiana State University (2020) Ph.D., Medical Physics, University of Texas M.D. Anderson Cancer Center / University of Texas-Houston (2008) M.S., Medical Physics, same institutions (2005) B.S., Physics, University of Louisiana-Lafayette (2002) His research bridges medical physics and radiation oncology, specializing in machine learning applications for treatment planning quality assurance, knowledge-based VMAT techniques for prostate cancer, and solutions for complex scenarios like targets extending beyond skin boundaries. This work enhances precision in radiotherapy through computational modeling and dose optimization. Analysis of his 2018-2020 publications reveals consistent focus on prostate cancer radiotherapy optimization using knowledge-based planning systems, with emphasis on predicting dose-volume outcomes and improving treatment deliverability through machine learning. Awards: Health Policy Fellowship, American Society for Radiation Oncology (2019) No student advising details or research grants are documented in the source material, though his leadership roles imply oversight of operational and physics teams. As CEO and former Chief of Physics at Mary Bird Perkins Cancer Center, Fontenot directs multidisciplinary teams in cancer treatment delivery, physics operations, and strategic implementation of advanced radiotherapy systems.

Jan RamonView profile

Researcher

• Data Mining
• Machine Learning
• Graph-Structured Data
• +9 more

Jan Ramon is a senior researcher at INRIA Lille Nord Europe, France, and a core member of the MAGNET (Machine learniNG in large-scale information NETworks) research team. His work spans both theoretical and applied aspects of machine learning, with a focus on graph-structured data and privacy-preserving AI. He is actively leading multiple high-impact research projects in decentralized and trustworthy machine learning. His research interests include: Data Mining and Machine Learning on Graphs Algorithmic and Statistical Analysis of Network Data Privacy-Preserving Techniques (e.g., Federated Learning, Differential Privacy) Secure Multi-Party Computation Applications in Biomedicine and Traffic Explainable and Fair AI His research projects, such as REDEEM, FLUTE, TRUMPET, TIP, and TAILED, demonstrate a strong trend toward building resilient, decentralized, and privacy-preserving AI systems. These projects often involve cross-border collaborations and aim to exceed GDPR compliance while enabling impactful applications in healthcare, particularly in oncology and prostate cancer diagnosis. The work integrates theoretical rigor with practical deployment, often contributing to open-source libraries like TAILED. Jan Ramon is a principal investigator (PI) on several major grants funded by ANR, Horizon Europe, and INRIA. He supervises PhD students and collaborates with engineers and post-doctoral researchers. He is also a member of the editorial boards of the Machine Learning and Data Mining and Knowledge Discovery journals. He has advised or is currently advising the following students: Antoine Barczewski (PhD student) Marc Damie (PhD student) His research is supported by significant grants and collaborations with institutions across Europe, including KU Leuven, CEA, CNRS, and various European universities and hospitals. He leads the development of open-source tools for trustworthy AI and is deeply involved in setting standards for privacy in federated learning. Jan Ramon leads or has led the following research projects: REDEEM (PEPR IA, ANR-23-PEIA-0005, 2023–2027): Resilient, Decentralized, Privacy-Preserving ML FLUTE (Horizon Europe, 2023–2025): Federated Learning for Prostate Cancer TRUMPET (Horizon Europe, 2022–2025): Trustworthy Multi-site Privacy Enhancing Technologies TIP (2021–2025): Transparent AI preserving Privacy ADT-TAILED (2020–2023): Trustworthy AI Library for Decentralized Environments ANR PMR (2021–2024) ERC PoC SOM (2016–2018) IWT-SBO InSPECtor (2014–2017) Chist-ERANET Adalab (2015–2017)

Lingxiang Albert YunView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +11 more

Dr. Lingxiang (Albert) Yun is a tenure-track Assistant Professor in the Department of Industrial and Systems Engineering at the University of North Carolina at Charlotte since June 2025. Previously, he served as a tenure-track Assistant Professor at Texas A&M University-Corpus Christi (2023-2025) and held research roles at the University of Illinois Chicago (2018-2023). His research bridges artificial intelligence, optimization, and systems engineering to address challenges in modern manufacturing, energy systems, and sustainable operations. Ph.D. in Industrial Engineering and Operations Research, University of Illinois Chicago (2023) M.S. in Environmental Engineering, University of California, Irvine (2017) B.S. in Environmental and Resource Science, Zhejiang University, China (2015) Research focuses on three pillars: Energy Systems Management (smart microgrids, electric mobility integration, grid resilience), Deep Reinforcement Learning (DRL) for Manufacturing (adaptive control, transferable algorithms, real-time optimization), and Generative AI for Design (reinforcement learning-guided diffusion models for protein engineering and drug discovery). His 12 recent publications span DRL applications in energy-flexible manufacturing, cloud remanufacturing logistics, and AI-augmented demand response frameworks. Key trends include interdisciplinary AI integration , resilient energy systems , and generative design optimization . Best Paper Award, IISE Annual Conference (2025) TEES Research Collaborations Award (2024) US Department of Transportation grant for safety analysis in Corpus Christi As principal investigator for Texas A&M's Research Enhancement Program and co-investigator on multiple grants, he leads the IDEA Lab, which develops data-driven decision-making frameworks for smart manufacturing and energy systems. Teaching includes IEEN 2302 Engineering Economics , IEEN 3302 Operations Research , and CEEN 5322 Optimization .

Marta MoyanoView profile

Associate Professor

• Marine Ecology
• Fish Larval Development
• Ocean Acidification
• +5 more

Marta Moyano is an Associate Professor in Marine Ecology at the Department of Natural Sciences, University of Agder, Norway, where she has been employed since 2020. Her research focuses on marine ecology, particularly fish larval development, physiological responses to environmental stressors, and plankton-fish interactions. Dr. Moyano earned her PhD in Oceanography from Universidad de Las Palmas de Gran Canaria, Spain (2009), following an MSc in Oceanography from the same institution (2007) and a BSc in Biology from Universidad de León, Spain (2004). Prior to her current position, she worked as a Researcher at the University of Hamburg, Germany (2013-2020) and was an Alexander von Humboldt Postdoctoral Fellow there (2011-2013). She also served as a Research Assistant at the Spanish Oceanographic Institute, Gijón, Spain (2010) and was a PhD Fellow funded by the Spanish Ministry of Science (2005-2009). Dr. Moyano's research primarily investigates how environmental factors affect marine fish, particularly during early life stages. Her work spans physiological ecology, climate change impacts, and trophic interactions in marine ecosystems. She has published extensively on topics including fish larval development, metabolic responses to ocean acidification and warming, and plankton-fish interactions. Her recent research has increasingly incorporated advanced technologies such as machine learning for plankton classification and software development for larval fish measurement. Her publication record shows a strong focus on small pelagic fish species, particularly herring, and their responses to environmental change. The research demonstrates growing interest in anthropogenic stressors, including ocean acidification, warming, and novel climate intervention techniques like ocean alkalinity enhancement. Her work often combines field observations with physiological modeling to understand recruitment processes and population dynamics. Alexander von Humboldt Postdoctoral Fellowship Dr. Moyano teaches BIO204 - Zoophysiology at the BSc level in Biology. While specific information about her current advisees isn't provided in the text, her extensive publication record suggests she likely mentors graduate students. Her research appears to be supported by various grants, though specific funding sources aren't detailed in the provided text. Her work involves collaboration with multiple research groups across Europe, particularly studying North Sea and Atlantic ecosystems. Recent publications indicate involvement in projects examining the impacts of climate change interventions on marine ecosystems and the development of new technologies for marine ecological research.

• Machine Learning
• Natural Language Processing
• Multimedia Systems
• +3 more

Liao Lizi is an Assistant Professor and Lee Kong Chian Fellow at Singapore Management University's School of Computing and Information Systems. She received her Ph.D. from the National University of Singapore under Professor Tat-Seng Chua. Her research explores the intersection of Machine Learning, Natural Language Processing, and Multimedia, focusing on developing conversational AI systems. Key areas include: Proactive conversational agents that anticipate user needs Multimodal conversational search and recommendation systems Task-oriented dialogue frameworks Human-machine collaborative systems Emotional support and empathetic response generation Her publications demonstrate a strong focus on advancing conversational AI through novel architectures for dialogue planning, multimodal understanding, and knowledge-enhanced generation. Recent work shows increasing emphasis on LLM optimization, financial reasoning benchmarks, and emotional intelligence in dialogue systems. Scientific Awards: Lee Kong Chian Fellowship Google South Asia & Southeast Asia Research Awards 2023 She leads the CoAgent Lab and advises multiple PhD students including recipients of the SMU Presidential Doctoral Fellowship. Current teaching includes Visual Analytics for Business Intelligence (IS428) and Text Analytics and Application (ISSS609).

• Data Science
• Business Intelligence
• Data Mining
• +7 more

Sérgio Moro is a Full Professor at ISCTE - University Institute of Lisbon, where he serves as Director of the School of Technologies and Architecture starting January 2025. He is affiliated with the Department of Information Science and Technology and is an Integrated Researcher at ISTAR-Iscte, the Research Center in Information Sciences, Technologies and Architecture. He was previously Deputy Director of the Department and Vice-President of the Scientific Council of ISCTE (2023–2025). In 2024, he was appointed Honorary Professor at the University of Jordan. His academic qualifications include: Bachelor’s in Computer and Informatics Engineering from Instituto Superior Técnico, University of Lisbon (2003) Master’s in Information Systems Management from ISCTE-IUL (2011) PhD in Information Science and Technology from ISCTE-IUL (2015) Habilitation (Aggregation) in Information Science and Technology (2019) Sérgio Moro’s research focuses on data-driven decision-making, particularly in Business Intelligence, Data Mining, and Decision Support Systems . His work bridges technical data science with practical applications in Marketing, Banking, and Tourism . He applies advanced analytics to solve real-world problems such as customer churn prediction, credit risk modeling, fraud detection, and dynamic pricing in hospitality. His publication trends reflect a strong interdisciplinary approach, combining Information Systems with domain-specific insights in Marketing and Tourism . His recent articles emphasize hybrid models integrating machine learning with business rules, spatial data mining for tourism planning, and big data analytics in financial and service sectors. The consistent theme is leveraging data to enhance organizational intelligence and strategic decisions. His scientific recognition includes: Ranked among the top 2% of researchers worldwide (Scopus-based study, since 2020) Honorary Professor, University of Jordan (2024) Associate Editor, Expert Systems (Wiley, Q2) Former Editorial Board member, Tourism Management Perspectives (Q1) Sérgio has played a pivotal role in academic leadership and student guidance. He was the first Director of the Master’s in Data Science (2020–2024) and the Bachelor’s in Digital Technologies and Artificial Intelligence (2022–2024) at ISCTE. He has advised numerous students and led curriculum development in data science education. While specific grants are not listed, his extensive publication record in high-impact journals (96 peer-reviewed, 67 in Q1) suggests sustained research funding and project leadership. His industry background at Banco Montepio (2001–2016) as Business Intelligence and Analytics Coordinator further strengthens his applied research profile. He is actively involved in research through ISTAR-Iscte, where he contributes to interdisciplinary projects at the intersection of data science, information systems, and societal applications. His work integrates technical innovation with strategic decision-making frameworks, positioning him as a key figure in applied data science in Europe.

Kai-Hsiung ChangView profile

Professor

• Software Testing
• Software Comprehension and Visualization
• Software Complexity Metrics
• +7 more

Kai-Hsiung Chang is a Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. He has been a faculty member since 1986, progressing from Assistant to Associate Professor in 1991, and to full Professor in 1998. He served as Department Chair from 2006 to 2016 and was honored with the Alumni Professorship from 2004 to 2009. Ph.D., Electrical and Computer Engineering, University of Cincinnati, 1986 M.S., Electrical and Computer Engineering, University of Cincinnati, 1982 Diploma, Electrical Engineering, Taipei Institute of Technology, 1977 Dr. Chang's research focuses on software testing, software comprehension and visualization, complexity metrics, information assurance education, computer supported cooperative work (CSCW), and artificial intelligence in software engineering. His work integrates formal methods and usage profiles to enhance software quality and safety. He has led research sponsored by the National Science Foundation (NSF) and the Department of Defense (DoD), particularly in pervasive and mobile computing and information assurance education. His recent publications reflect a strong focus on software testing methodologies, formal specification-based validation, collaborative systems, and intelligent software tools. Themes include object-oriented testing, complexity measurement, CSCW, and AI-driven test generation, indicating a long-standing commitment to improving software reliability and development processes. Alumni Professorship, Auburn University (2004–2009) ACM Certificate of Appreciation for Outstanding Service (1997) IEEE Senior Member INFOSEC Professional Certification (2005) Multiple Outstanding Teaching Awards at Auburn University (1989, 1992, 1997) Summer Faculty Fellowship at NASA Marshall Space Flight Center (1995) Dr. Chang has advised over 50 graduate students, including numerous Ph.D. and Master’s candidates, many of whom now hold academic and industry positions. He has served on NSF and DHS fellowship review panels, contributed to ABET accreditation as a commissioner and program evaluator, and participated in the IEEE-Computer Society/ACM Curriculum Committee for Software Engineering. He also served as Vice President of the International Society of Applied Intelligence. He has led significant research initiatives, including NSF-sponsored REU programs in pervasive computing, and has been deeply involved in curriculum development and professional service. His lab and research group have focused on software quality, intelligent systems, and collaborative environments, fostering interdisciplinary work in software engineering and AI.