جست‌وجوی استاد و پژوهشگر

Sonia Haiducمشاهده پروفایل

دانشیار

• Software Engineering
• Software Maintenance and Evolution
• Program Comprehension
• +۶ مورد دیگر

Sonia Haiduc is an Associate Professor in the Department of Computer Science at Florida State University, where she leads the SERENE (Software Engineering: Evolution and Maintenance) research lab. Her academic career spans over a decade of contributions to software engineering research, with particular expertise in software maintenance, evolution, and program comprehension. Education: Ph.D. in Computer Science, Wayne State University (2013) M.Sc. in Computer Science, Wayne State University (2009) B.Sc. in Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania (2006) Her research focuses on innovative approaches to assist software developers in understanding and maintaining complex software systems. Key areas include software evolution, program comprehension, concept location, source code search, empirical software engineering, and the application of information retrieval and natural language processing techniques to software engineering problems. Her work bridges theoretical foundations with practical applications to improve developer productivity and software quality. Recent publications demonstrate a growing interest in AI-assisted software development, particularly the application of large language models to program repair, commit message assessment, and code search. Her research shows a consistent trajectory from traditional software engineering topics toward integrating modern AI techniques to solve longstanding challenges in software maintenance and evolution. Scientific Awards: ACM SIGSOFT CAPS Travel award (2012, 2010) Google Anita Borg Scholarship (2011) Wayne State University Travel Awards (multiple years) Outstanding Graduate Research Assistant in Computer Science (2010) Dr. Haiduc actively mentors doctoral students, currently advising three Ph.D. candidates while serving on committees for additional graduate students. She has secured research funding including an NSF SHF grant for 'Text Retrieval in Software Engineering 2.0' and an FSU CRC grant for 'Query-Specific Source Code Search Engine Configuration.' She founded and leads the SERENE lab at Florida State University, which focuses on novel approaches to software maintenance, evolution, program comprehension, mining software repositories, and applications of natural language processing in software engineering.

Yuan Tianمشاهده پروفایل

استادیار

• Software Engineering
• Applied Machine Learning
• AI for Software Engineering
• +۷ مورد دیگر

Dr. Yuan Tian is an Assistant Professor in the School of Computing at Queen's University, Canada. Her research focuses on applying artificial intelligence and machine learning techniques to solve software engineering challenges, particularly in the areas of code analysis, technical debt management, and developer productivity enhancement. Dr. Tian received her Ph.D. in Information Systems from Singapore Management University in May 2017 under the supervision of Prof. David Lo (IEEE/ACM fellow). Prior to joining Queen's University, she worked as a data scientist at the Living Analytics Research Centre (LARC) in Singapore. She has also conducted research visits at Carnegie Mellon University in 2015 and Inria Paris in 2013. Dr. Tian's research spans several key areas in software engineering with AI: Automatic technical debt, bug, and code change management LLM applications for code transformation and generation Human-AI collaboration in software development Analysis of developer interactions with AI tools like ChatGPT Mining software repositories for insights into development practices Her recent work has increasingly focused on leveraging Large Language Models to address software engineering challenges, with publications examining code translation, technical debt identification, and the dynamics of developer-AI interactions. Her research demonstrates a strong empirical approach, often analyzing large datasets from GitHub and other software development platforms. Dr. Tian has received recognition for her work, including the Best Research Paper Award at AI Foundation Models and Software Engineering (Forge), 2024 for her paper "Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation." Dr. Tian leads the RISE research lab at Queen's University, which currently includes 5 PhD students, 2 MSc students, and 2 undergraduate research assistants. She has successfully supervised several graduate students to completion, with alumni now working at institutions including Duke University and Veeva Systems. Her research is supported by funding including an NSERC Alliance-Mitacs project on "Pragmatic Automated Code Transformation Leveraging Large Language Models" in collaboration with industry partner Ross Video. The RISE lab (Goodwin 621) is dedicated to developing reliable and intelligent support for software engineering. The lab's current research focuses on three main thrusts: automatic technical debt/bug/code change management, LLM for code transformation, and human-AI collaboration in software development.

Rahul Gomesمشاهده پروفایل

دانشیار

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Rahul Gomes, Ph.D., is an Associate Professor of Computer Science and Ramsey Research Professor (2025-2028) at the University of Wisconsin–Eau Claire within the College of Arts and Sciences. His work bridges artificial intelligence, machine learning, and computational science with applications in healthcare, geospatial analytics, and cybersecurity. He maintains active research collaborations with clinical, scientific, and community partners while mentoring undergraduate and graduate students in cutting-edge projects. His educational background includes: Ph.D. in Computer Science from North Dakota State University, Fargo M.S. in Computer Science from Sikkim Manipal University, India B.Ed. and B.S. from St. Xavier's College Kolkata, India Gomes leads research in multiple high-impact domains with a focus on developing interpretable AI systems that address real-world challenges. His biomedical informatics work spans medical imaging analysis (particularly for IVCF detection and pancreatic cancer diagnosis), single-cell RNA sequencing, and retrieval-augmented generation for clinical workflows. In geospatial analytics, he develops deep learning approaches for remote sensing and land cover classification. His cybersecurity research investigates AI-based threats and defense mechanisms, including analysis of large language model vulnerabilities. The interdisciplinary nature of his work is reflected in publications spanning computer science, medical journals, and domain-specific applications. His publication record demonstrates consistent productivity across multiple domains, with recent work showing particular strength in medical AI applications, geospatial deep learning, and cybersecurity analysis. The research shows a clear trajectory toward increasingly complex multimodal approaches, with recent papers integrating vision transformers, domain adaptation techniques, and specialized architectures for specific medical and geospatial challenges. Many publications involve undergraduate student co-authors, reflecting his commitment to research-based education. His scientific recognition includes: Ramsey Research Professorship (2025-2028) NSF REU grant as PI ($459,810) for advancing HPC undergraduate research (2025-2028) RET Site grant as Co-PI to bridge gaps in high school computing education (2024-2027) Gomes actively mentors students through research projects that often lead to publications and conference presentations. His research group develops advanced methods for medical image analysis, spatial transcriptomics, and AI-driven healthcare decision support. He has successfully secured significant grant funding that supports undergraduate research experiences, demonstrating his commitment to involving students in meaningful research. His TARCC RET Site project specifically aims to enhance computing education at the high school level through teacher training. His research lab focuses on several key projects including IVCF Filter Detection using AI (employing Swin-UNet for CT scan analysis), PDAC Diagnosis using Deep Learning, scRNA-seq Analysis for pancreatic cancer, CABG Outcomes analysis, and RAG applications in healthcare. These projects involve interdisciplinary teams of students working with technologies including PyTorch, MONAI, LangChain, and various high-performance computing resources.

Seong Joon Ohمشاهده پروفایل

استاد

• Trustworthy AI
• Explainable AI
• Robust Machine Learning
• +۷ مورد دیگر

Seong Joon Oh is a Professor at the University of Tübingen leading the Scalable Trustworthy AI (STAI) research group. He also advises Parameter Lab alongside his main academic position. His research career spans both academia and industry, having previously served as a research scientist at NAVER AI Lab for 3.5 years. Oh received his PhD in computer vision and machine learning from the Max-Planck Institute for Informatics in 2018 under the supervision of Bernt Schiele and Mario Fritz. His doctoral research focused on privacy and security implications of computer vision and machine learning. He completed his earlier education at the University of Cambridge, earning a Master of Mathematics with Distinction in 2014 and a Bachelor of Arts in Mathematics as a Wrangler in 2013. Professor Oh's research concentrates on training reliable AI models with emphasis on explainability, robustness, and probabilistic modeling. His work explores methods to obtain necessary human supervision in a cost-effective manner, with significant contributions to uncertainty quantification, out-of-distribution generalization, and privacy-preserving techniques. His research intersects computer vision, natural language processing, and machine learning theory, particularly focusing on compositionality, trustworthy AI, and model interpretability. Analysis of Oh's recent publications reveals a strong focus on practical challenges in deploying trustworthy AI systems. His work spans multiple domains including computer vision, natural language processing, and foundational machine learning. Key themes include compositionality in vision-language models, privacy concerns in large language models, domain adaptation techniques, and methods for improving model robustness. His research demonstrates a consistent trajectory toward building AI systems that are not only high-performing but also reliable, interpretable, and secure. Professor Oh has made significant contributions to several research communities as evidenced by his publication record across top-tier conferences including NeurIPS, ICML, ICLR, CVPR, and ACL. His work on membership inference attacks, object-centric learning, and trustworthy machine learning has influenced the field. He co-authored the textbook "Trustworthy Machine Learning" which covers out-of-distribution generalization, explainability, uncertainty quantification, and evaluation of trustworthiness in machine learning models. Oh leads the Scalable Trustworthy AI research group at the University of Tübingen, focusing on developing AI systems that are explainable, robust, and probabilistic. His team investigates fundamental questions about how AI models understand compositionality, handle distribution shifts, and maintain privacy when processing sensitive information. The group's work bridges theoretical insights with practical applications, aiming to create AI systems that can be safely deployed in real-world scenarios where reliability is critical.

Georg Rehmمشاهده پروفایل

استاد مدعو

• Language Technology
• Computational Linguistics
• Artificial Intelligence
• +۴ مورد دیگر

Georg Rehm holds an Adjunct Professorship in Computational Linguistics and Language Technology at Humboldt-Universität zu Berlin's Faculty of Philosophy. He is a Principal Researcher and Research Fellow at DFKI Berlin's Speech and Language Technology Lab, and Head of the W3C Germany/Austria Chapter. His roles include coordinating major EU initiatives like the European Language Grid (ELG) and QURATOR. Rehm's work spans Language Technology, AI, and multilingual systems, with a focus on digital language equality, curation technologies, and open science infrastructure. He leads projects such as HIVEMIND (2025-2027) and Language Data Space (2023-2025), and has contributed over 275 publications in computational linguistics and related fields. His research addresses challenges in multilingual NLP, fake news detection, and ethical AI deployment. Rehm oversees strategic initiatives like the META-NET network and chairs conferences such as ACL Industry Track and Sci-K workshops. He advises on EU policy frameworks for language technology and collaborates with organizations like the DIN Presidential Committee and FOCUS.ICT. His academic network spans global institutions, and he actively participates in program committees for top venues including ACL, NAACL, and COLING. Rehm's lab develops tools like the Knowledge Storage Ecosystem and contributes to open-source LLM projects like Occiglot and OpenGPT-X. He holds a PhD and has over 25 years of industry-research experience. His research interests include semantic storytelling, interoperable metadata, and AI ethics. He is affiliated with multiple institutes, including the DFKI Lab Berlin and the W3C, and serves on editorial boards for journals like Language Resources and Evaluation.

• Large Language Models
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Benjamin Roth is a Professor at Saarland University holding dual affiliations in the Faculty of Computer Science (Research Group Data Mining and Machine Learning) and the Faculty of Philological and Cultural Studies (Department of European and Comparative Literature and Language Studies). His research focuses on natural language processing, large language models, machine learning, and computational linguistics. He leads multiple active research projects including 'Understanding Language in Context' (2025–2033) and 'Linguistic Methods for the Detection of Implicit Abuse' (2024–2027). Notable collaborations span cross-functional studies on LLM behavior, clinical text analysis, and knowledge graph integration. He actively participates in conference organization and has presented at venues like the Konferenz zur Verarbeitung natürlicher Sprache (2024). His work emphasizes methodological innovation in weak supervision, model calibration, and multimodal reasoning. Recent contributions include studies on persona effects in LLMs, specification overfitting mitigation, and zero-shot temporal relation extraction. He has co-authored over 20 peer-reviewed publications since 2020, with a focus on advancing ethical AI, model interpretability, and NLP education.

Manfred Hauswirthمشاهده پروفایل

استاد

• Distributed Systems
• Internet of Things (IoT)
• Stream Processing
• +۴ مورد دیگر

Prof. Manfred Hauswirth is the Managing Director of Fraunhofer Institute for Open Communication Systems (FOKUS) and holds the Chair for Open Distributed Systems at Technical University of Berlin. His research focuses on distributed systems, IoT, stream processing, quantum computing, and blockchain. He has held roles including Vice Director at Digital Enterprise Research Institute (DERI) and professor at National University of Ireland, Galway. He leads multiple strategic initiatives, including the Fraunhofer Quantum Technologies Research Field and the Weizenbaum Institute. His work bridges academia and industry, emphasizing digitalization, quantum computing, and IoT. Education: Dipl.-Ing. (1993), Dr. techn. (1999) in Computer Science from Vienna University of Technology. Postdoctoral work at École Polytechnique Fédérale de Lausanne (EPFL). Research Interests: Prof. Hauswirth’s work spans distributed systems, semantic web technologies, quantum algorithms, and IoT edge computing. He emphasizes real-world applications like smart cities, autonomous driving, and secure data management. Recent trends in his publications include quantum programming frameworks (e.g., Qrisp), scalable graph distillation, and edge-based AI systems. Awards: Not explicitly listed, but his work has been recognized through leadership roles in IEEE, ACM, and Fraunhofer committees. Advising & Grants: Active in funding initiatives like the Berlin Institute for Learning and Data (BIFOLD) and Einstein Center Digital Future (ECDF). Leads projects on quantum benchmarking, energy flexibility markets, and semantic stream processing. Labs/Teams: Directs the Fraunhofer High Performance Center for Digital Networking and chairs the Quantum Computing Competence Network, integrating interdisciplinary teams across quantum computing, IoT, and AI domains.

Barbara Reمشاهده پروفایل

دانشیار

• Process Mining
• Blockchain Technology
• IoT Systems Engineering
• +۴ مورد دیگر

Barbara Re is an Associate Professor at the University of Camerino, specializing in interdisciplinary research at the intersection of blockchain technology, process mining, and IoT systems engineering. Her work emphasizes integrating formal modeling approaches with real-world applications in robotics, digital twins, and smart contract analysis. Key research areas include: Development of BPMN-based frameworks for IoT and multi-robot systems Blockchain applications for choreography-based systems and smart contract auditing Process mining techniques for analyzing robotic and Ethereum-based systems Design of digital twin architectures for industrial and environmental monitoring Recent work highlights include: SAFE Platform : A disaster response system integrating IoT and process-driven protocols ChorChain : Blockchain framework for trustworthy multi-party business processes Fluidware : Model-driven approach for cross-platform IoT applications Her contributions span over 50 academic publications since 2020, with a focus on formal verification, adaptive system design, and interdisciplinary ICT solutions for complex operational environments.

• Biosystems Engineering
• Chemical Engineering
• Data Modeling
• +۳ مورد دیگر

Dr. Jason Morrison is an Associate Professor and Associate Head (Undergraduate) in the Department of Biosystems Engineering at the University of Manitoba. He holds degrees in Mechanical Engineering, Computer Science, and Computational Science from McMaster and Carleton Universities. His research focuses on creating simplified data models, optimizing model fits, and analyzing process outcomes, particularly in chemical spectra modeling, biological material analysis, and 3D printer performance optimization. He has received grants in both Information Technology and Materials and Chemical Engineering, emphasizing reproducible open-source tools for additive manufacturing and spectroscopy data analysis. Dr. Morrison’s work addresses challenges in proprietary data formats that hinder reproducibility and interoperability in these fields. His teaching awards reflect his dedication to student success in engineering education. Recent research spans agricultural applications (e.g., Fusarium detection in wheat), biomaterials development (e.g., hemp fiber processing), and medical device sterilization (3D-printed plastics). His publications (2015–2021) highlight interdisciplinary approaches bridging engineering, biology, and computer science. Education: B.Eng. in Mechanical Engineering, McMaster University (1993) B.Sc. Honours in Computer Science, McMaster University (1995) M.Comp.Sc. in Computational Science, Carleton University (1997) Ph.D. in Computational Science, Carleton University (2002) Research Interests: Optimization of mathematical models and data simplification Spectroscopic analysis of chemical/biological materials Additive manufacturing (3D printing) tooling and sterilization Ergonomics of agricultural machinery design Non-destructive testing via hyperspectral imaging Grants & Awards: Grants in Information Technology and Materials Engineering (details unspecified) Teaching awards (specific names unspecified) Labs/Teams: Not explicitly mentioned in profile; focus on interdisciplinary collaborations in biosystems engineering.

• Information Retrieval
• Natural Language Processing
• Biomedical Informatics
• +۳ مورد دیگر

Hong Yu is an Adjunct Professor at UMass Chan Medical School, affiliated with the Department of Medicine and the Bioinformatics and Integrative Biology program. She holds positions at both UMass Chan Medical School and the T.H. Chan School of Medicine. Her work focuses on information retrieval, natural language processing (NLP), and human-computer interaction with applications in biomedical informatics. Education: Bachelor of Science in Biomedical Engineering, Huazhong University of Science and Technology, China Master of Science in Physiology, Beijing Normal University, China Master of Arts and Master of Philosophy in Physiology & Molecular Biophysics, Columbia University, USA PhD in Biomedical Informatics, Columbia University, USA Research & Contributions: Developed systems like AskHERMES (clinical QA system), ADErepository (pharmacovigilance), and FigureSearch (biomedical figure search) Focus on NLP applications for medical text summarization, clinical question answering, and figure-based information retrieval PI of NIH-funded projects and editorial board member of the Journal of Biomedical Informatics Awards & Recognition: NLM pre-doctoral grant recipient and 1 of 6 'star trainees' at NLM's 175th anniversary Best Paper Nomination (AMIA 1999) and IMIA Yearbook recognition (2011) Featured in Science, Nature, and Pulitzer-winning publications Labs & Teams: Leads teams developing clinical decision support systems, NLP tools for electronic health records, and patient-aided medical communication platforms like ClinicalNotesAid.

• Quantum Physics
• Materials Science
• Machine Learning
• +۳ مورد دیگر

Dr. Andreas Leitherer is a Postdoctoral Researcher in the Department of Quantum Information Theory at the Institute of Photonic Sciences (ICFO). He holds a PhD in Physics from Humboldt University (Germany). His research focuses on interdisciplinary areas combining quantum systems analysis, materials science, and machine learning applications in crystallography and microscopy. Leitherer's work bridges theoretical quantum physics with computational methods, particularly in open quantum systems and algebraic quantum gravity. He also pioneers AI-driven approaches for automated crystal structure identification and materials characterization using electron microscopy. His publications since 2017 reflect a progression from foundational quantum gravity studies to applied machine learning in materials science. His research interests include quantum dynamics, computational materials science, Bayesian deep learning for crystallography, and data-centric materials discovery. Notably, he has contributed to developing robust machine learning frameworks for feature classification in atomic resolution images and exploring materials dataspaces through hybrid learning models. No scientific awards or formal student advisees are listed. His current research is affiliated with ICFO's Quantum Information Theory group, where he applies advanced computational techniques to solve problems at the intersection of quantum physics and materials innovation.

Preetha Chatterjeeمشاهده پروفایل

استادیار

• Software Engineering
• Empirical Software Engineering
• Software Analytics
• +۷ مورد دیگر

Preetha Chatterjee is an Assistant Professor in the Department of Computer Science at Drexel University, where she leads the SOftware Engineering and Analytics Research (SOAR) Lab. Her research lies at the intersection of software engineering, machine learning, and natural language processing, with a focus on improving developer tools and environments through data mining and empirical studies. PhD and MS in Computer Science, University of Delaware Former Software Engineer with over 5 years of industry experience Her research interests include software analytics, mining software repositories, empirical software engineering, emotion and toxicity detection in developer communications, and enhancing LLM-assisted software development. She investigates how data-driven techniques can extract actionable knowledge from software artifacts and improve developer productivity and collaboration. Her recent publications focus on leveraging large language models for bug resolution, detecting security risks in LLM-generated code, mining emotions and moral foundations in open-source communications, and extracting valuable information from developer chat platforms like Slack and Discord. Her work combines empirical studies with state-of-the-art NLP and ML techniques. Notable scientific recognitions include the Distinguished Reviewer Award at FSE 2023 and research excellence awards for her lab members. She has served on the program committees of top conferences including ICSE, FSE, ASE, and MSR, and on the editorial board of the Journal of Systems and Software (JSS). She advises multiple PhD, MS, and undergraduate students. She co-leads the Drexel Programming Systems Seminar and actively mentors students in research. Her lab has received recognition for research excellence and leadership. She teaches courses in software engineering and software analytics at both undergraduate and graduate levels at Drexel University. She maintains active research collaborations and has led the creation of several public datasets and tools for the software engineering community, including datasets on disentangled Slack chats, Discord conversations, and annotated GitHub issue threads.

• Knowledge Engineering
• Semantic Web
• Data Infrastructures
• +۷ مورد دیگر

Christoph Lange is the Head of the Data Science and Artificial Intelligence department at the Fraunhofer Institute for Applied Information Technology (FIT) and a senior researcher at the Chair of Information Systems 5 (DBIS) at RWTH Aachen University, Germany. He has been affiliated with RWTH Aachen since 2019 and with Fraunhofer since 2014. He received his Ph.D. from Jacobs University Bremen in 2011 for work on enabling collaboration on semiformal mathematical knowledge through semantic web integration. His research focuses on knowledge engineering, data infrastructures, and semantic technologies, particularly aligned with Linked Data and FAIR principles (Findable, Accessible, Interoperable, Reusable). Applications span enterprise data integration, research infrastructures, and cross-organizational data spaces, with contractual R&D conducted for leading German and international automotive and industrial enterprises. His research interests include Knowledge Engineering , Semantic Web , Knowledge Graphs , Ontology Engineering , FAIR Data , and Intelligent Computer Mathematics . He has co-supervised 10 PhD students and taught courses such as the Knowledge Graph Lab at RWTH Aachen. He has organized numerous conferences, workshops, and training events in semantic web and intelligent mathematics domains and contributed to W3C and OMG international standards. Recent publications (2023–2025) demonstrate a strong trend towards integrating Large Language Models with semantic technologies for FAIR data spaces, ontology development, and explainable AI. His work also advances tools for knowledge graph creation (e.g., KGraphX), RDF validation, federated data catalogues, and semantic interoperability in manufacturing and scientific domains. h-index 30 (Google Scholar) Christoph Lange has served as an expert reviewer for international funding bodies and has led impactful collaborations across academia and industry. His work bridges theoretical advances in knowledge representation with real-world applications in data-intensive sectors. He is actively involved in research projects such as NFDI4DS and FAIR Data Spaces, contributing to next-generation data ecosystems. He leads the development of semantic tools and infrastructures, including contributions to Wikidata and CEUR-WS semantification. His lab work includes the Knowledge Graph Lab series, focusing on practical implementation and teaching of semantic technologies. Future work is expected to further explore the synergy between generative AI and formal knowledge representation to enhance data quality, reuse, and machine understanding.

Paweł Ksieniewiczمشاهده پروفایل

استاد

• Pattern Recognition
• Data Streams
• Hyperspectral Image Processing
• +۷ مورد دیگر

Paweł Ksieniewicz is a Professor at Wrocław University of Science and Technology, affiliated with the Faculty of Electronics and the Department of Computer Systems and Networks. He is a member of the Computer Networks Team and the Machine Learning Team, and serves as a project manager for SWAROG (2021–2025) and contributor to IDSTREAM and GEOM projects. His research interests include: Pattern Recognition Data Streams Hyperspectral Image Processing Imbalanced Data Classification Fake News Detection Classifier Ensembles Ksieniewicz's recent publications focus on challenges in data stream mining, particularly in dynamically imbalanced environments, prior probability estimation, and ensemble methods. His work spans applications in cybersecurity (fake news, IoT attack detection) and healthcare (glaucoma diagnosis). He has contributed to open-source tools like the Stream-learn Python library, emphasizing practical and reproducible machine learning research. His scientific awards include: Minister of Education and Science Scholarship for Outstanding Young Scientists Award of the Polish Society for Artificial Intelligence for Best Doctoral Thesis Multiple Rector's Awards for scientific achievements (2019–2022) Best Paper Award at ICAISC 2021 Winner of the Secundus and Primus research incentive programs Appointment to Academia Iuvenum (2024–2026) Ksieniewicz actively supervises students and has collaborated with researchers such as Michał Woźniak, Paweł Zyblewski, and Robert Burduk. He has secured research grants through national projects and serves on the Discipline Council for Technical Computer Science and Teleinformatics. He has also participated in public outreach, including lectures during Science Week and World Information Society Day. He leads and contributes to multiple research teams, including: Machine Learning Team Computer Networks Team Advanced Data Analysis Methods Team Metaheuristics Team

• Recommender Systems
• Music Information Retrieval
• Data Science
• +۳ مورد دیگر

Eva Zangerle is a Professor at Universität Innsbruck, Austria, with a focus on recommender systems, music information retrieval, and data science. She actively contributes to multi-method evaluation frameworks and collaborative research initiatives like PAN (Plagiarism Authorship Verification) workshops. Key research areas: Recommender Systems Evaluation, Music Emotion Recognition, Authorship Analysis Major collaborations with institutions like Zenodo, ACM, CEUR-WS.org, and RecSys conferences Her recent work explores graph neural networks for music recommendation, style change detection in multi-author texts, and cross-domain user modeling. Articles emphasize temporal modeling, multimodal data fusion, and ethical considerations in algorithmic systems. Eva leads tasks in PAN workshops and contributes to open-access datasets. She collaborates with researchers like Christine Bauer, Alan Said, and Günther Specht on improving evaluation practices in recommender systems.