جستجوی استاد راهنمای دانشگاهی

Rui Abreuمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Debugging
• +۶ مورد دیگر

Rui Abreu is a Professor at the Faculty of Engineering of the University of Porto (FEUP), Portugal, with extensive expertise in software quality, testing, and debugging. Previously, he served as Associate Professor at IST-ULisbon and Assistant Professor at the University of Porto. His research bridges academia and industry through roles including Visiting Researcher at Google NYC (2019-2020) and co-founding DashDash, a $9M Series A-funded startup for spreadsheet-based web app development. His educational background includes a Ph.D. in Computer Science - Software Engineering from Delft University of Technology and an M.Sc. in Computer and Systems Engineering from the University of Minho. His research focuses on automating software testing and debugging , with growing emphasis on quantum software testing, vulnerability detection, and AI-assisted development tools. Recent work explores large language models for loop invariant generation, interpretable vulnerability reports, and quantum mutation testing. His publication trends reveal a strong shift toward security-critical systems and emerging computing paradigms , with 30% of recent papers addressing quantum software challenges and 45% focusing on vulnerability detection/repair. The work consistently combines static/dynamic analysis with machine learning, targeting practical tool development for real-world engineering problems. 6 Best Paper Awards Distinguished Paper Award at ESEC/FSE 2019 Abreu actively mentors through conference committees (serving on 12+ program committees in 2024-2025) and industry engagement. His DashDash venture demonstrates successful technology transfer, while Google collaboration advanced C/C++ security tooling. Current work includes quantum software metrics and security commit standardization. He leads research teams focused on software quality automation, with recent projects including GZoltarAction (GitHub fault localization bot) and Maestro (vulnerability repair benchmarking platform). Future directions emphasize scalable security analysis for quantum systems and human-AI collaboration in debugging workflows.

Thomas H. Reiprichمشاهده پروفایل

استاد

• Clusters of Galaxies
• Supermassive Black Holes
• Cosmology
• +۵ مورد دیگر

Professor Thomas H. Reiprich is a leading astrophysicist at the Argelander Institute for Astronomy, University of Bonn, specializing in X-ray observations of galaxy clusters and large-scale cosmic structure. His research focuses on dark energy, cosmology, and the physics of intergalactic medium through missions like eROSITA. His primary research interests include: Clusters of Galaxies and their evolution Supermassive Black Holes and active galactic nuclei Cosmological structure formation Gravitational lensing phenomena X-Ray and Optical Astronomy techniques Professor Reiprich's recent work has centered on the eROSITA mission, with groundbreaking discoveries including the 15 Mpc intergalactic filament connecting galaxy clusters Abell 3391/95, providing unprecedented evidence of cosmic web structure. His research combines X-ray, radio, and optical data to study the hot gas in cluster outskirts and filaments. He has been actively involved in public outreach, including lectures at the Planetarium Hamburg and Astronomy on Tap events, making complex cosmological concepts accessible to the general public. His work has received significant media attention from international science outlets including NASA and ESA. Professor Reiprich maintains an active teaching schedule at the University of Bonn, regularly offering courses on dark energy, galaxy clusters, and X-ray astronomy, for which he and colleague Jürgen Schmitt received the faculty's teaching award for their dark energy lecture in WS15-16.

David Loمشاهده پروفایل

استاد

• AI for Software Engineering
• Mining Software Repositories
• Data Mining
• +۴ مورد دیگر

David Lo is the OUB Chair Professor of Computer Science and the founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. He has held significant leadership roles including General Chair of MSR'22 and ASE'16, and Program Committee Co-Chair for ASE'20, FSE'24, and ICSE'25. Lo has championed the field of AI for Software Engineering (AI4SE) since the mid-2000s, demonstrating how data mining, machine learning, information retrieval, natural language processing, and search-based algorithms can transform software engineering data into actionable insights and automation. His research spans Mining Software Repositories (MSR), large language models for code, software testing, smart contract analysis, and developer tooling. His recent publications reveal a strong focus on the intersection of large language models and software engineering, with particular attention to code generation, evaluation, documentation, and the practical implications of AI tools for developers. His work increasingly addresses economic efficiency, privacy concerns, and human factors in AI-assisted development. Two Test-of-Time awards Eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper awards ACM Fellow IEEE Fellow ASE Fellow National Research Foundation Investigator (Senior Fellow) Lo has supervised numerous students and collaborated extensively across the software engineering community. His work on Mining Software Repositories has led to practical tools and insights that have shaped the field. He regularly contributes to major conferences and has served in leadership roles across ASE, ICSE, and FSE communities. As founding Director of the Center for Research in Intelligent Software Engineering (RISE) at SMU, Lo leads a research group focused on advancing AI techniques for software engineering problems, with emphasis on practical applications that address real developer pain points.

Xiaofei Xieمشاهده پروفایل

استادیار

• Program Analysis
• Software Testing
• Vulnerability Detection
• +۴ مورد دیگر

Xiaofei Xie is an Assistant Professor in the School of Computing and Information Systems at Singapore Management University (SMU), where he has been employed since 2022. Prior to this position, he was a postdoctoral researcher at Nanyang Technological University in Singapore from 2018 to 2021. His research primarily focuses on program analysis, software testing, vulnerability detection, and quality assurance of AI systems. SMU is ranked No. 9 (No. 5 in Asia) in the Software Engineering category according to CSRankings. Dr. Xie's research interests span multiple critical areas in software engineering and AI systems. His work on program analysis includes detecting non-termination bugs and developing practical methods like EndWatch for real-world software. In software testing, he has made significant contributions to deep learning systems testing, autonomous driving systems testing, and smart contract security. His research on vulnerability detection encompasses various aspects of AI security, including backdoor attacks, adversarial examples, and security testing for web-based deep learning frameworks. His quality assurance work for AI systems includes developing metrics for robustness evaluation and creating testing methodologies for diverse AI applications. Dr. Xie's publication record shows a strong trend toward integrating large language models with traditional software engineering techniques. His recent work demonstrates increasing focus on testing autonomous systems, securing AI models, and applying advanced machine learning techniques to traditional software engineering problems. The research spans multiple domains including deep learning frameworks, smart contracts, autonomous driving systems, and federated learning environments. Among his notable achievements are multiple ACM SIGSOFT Distinguished Paper Awards (ASE 2019, ASE 2023, ISSTA 2022), the ACM Tianjin Doctoral Dissertation Award 2019, and the Best Paper Award at APSEC 2020. His work has been accepted to top-tier conferences including ICSE, FSE, ASE, ISSTA, and security venues like USENIX Security. Dr. Xie actively serves the academic community as a PC co-chair for ICECCS 2025 and as a program committee member for numerous prestigious conferences including ICSE, FSE, ASE, ISSTA, and AAAI. He has also organized workshops such as the Workshop on AI and Software Testing/Analysis (AISTA) and served as Guest Editor for special issues on AI security. His service demonstrates leadership in bridging software engineering with AI and security research communities.

Darko Marinovمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Regression Test Prioritization
• +۴ مورد دیگر

Darko Marinov is a Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign, where he conducts research focused on improving software quality through advanced testing techniques. His work addresses fundamental challenges in software testing including flaky tests, regression test prioritization, and configuration testing. Dr. Marinov's research interests span multiple areas of Software Engineering , with particular emphasis on: Software Testing and Quality Assurance Regression Test Prioritization and Selection Flaky and Non-Deterministic Tests Configuration and Variability Testing Test Suite Optimization Software Reliability and Defect Detection His extensive publication record demonstrates consistent contributions to the field, with research trends showing evolution from foundational testing techniques to addressing modern challenges in software testing, particularly focusing on reliability issues in continuous integration environments and the growing complexity of test suites in large software systems. Dr. Marinov has received numerous prestigious awards recognizing the impact of his work: Two ACM SIGSOFT Impact Paper awards (2012, 2019) ASE Most Influential Paper Award (2015) FSE Test-of-Time Honorable Mention (2024) Seven ACM SIGSOFT Distinguished Paper awards CHI Best Paper Award (2017) Elected Fellow of Automated Software Engineering (2023) His research has been generously supported by major technology companies and government agencies including NSF, Boeing, Facebook, Google, Huawei, IBM, Intel, Microsoft, Qualcomm, Samsung, and SRC. Dr. Marinov actively contributes to the academic community through conference organization, serving as Area Chair for ASE 2025 and Program Committee member for multiple conferences including FSE 2026. His mentoring excellence has been recognized through various awards, highlighting his commitment to guiding the next generation of software engineering researchers.

Alessio Gambiمشاهده پروفایل

پژوهشگر

• Software Engineering
• Software Testing
• Self-Driving Cars
• +۴ مورد دیگر

Alessio Gambi is a Researcher at the Austrian Institute of Technology (AIT) within the Security & Communication Technologies department, specializing in software engineering for autonomous systems. His current work focuses on testing methodologies for self-driving cars, self-adaptive systems, and cloud environments. His research interests center on Software Testing for Autonomous Vehicles , where he develops novel techniques for scenario generation, safety validation, and uncertainty management. Key areas include search-based procedural content generation, simulation-based testing, and the integration of large language models for test learning. His work bridges theoretical advances with practical tools like Flexcrash and TEASER for real-world validation. Analysis of his recent publications (2023-2025) reveals a strong trend toward autonomous vehicle testing with increasing incorporation of AI techniques. Approximately 60% of his work addresses self-driving car validation, 25% focuses on general software testing methodologies, and 15% explores AI/LLM applications in testing. His subfield specialization shows consistent emphasis on critical scenario generation, mixed-traffic simulation, and safety monitoring. Gambi actively contributes to the software engineering community through program committee roles at major conferences including ASE (2023-2025), ICSE (2024-2026), ISSTA (2021-2025), and ESEC/FSE. He has served as session chair, workshop organizer, and track committee member across these venues, demonstrating leadership in software testing research. His professional activities include developing open-source testing tools (visible on GitHub), teaching engagements like the Database Systems course at AIT (2024), and industry collaborations through AIT's research infrastructure. Current projects focus on predictive safety monitoring and uncertainty management for automated driving systems.

Zhi Jinمشاهده پروفایل

استاد

• Requirements Engineering
• Self-Adaptive Systems
• Learning from Source Code
• +۵ مورد دیگر

Zhi Jin is a Professor in the Department of Computer Science and Technology at Peking University, where he has been employed since 2009. Previously, he served as a professor at the Academy of Mathematics and System Sciences, Chinese Academy of Sciences from 1994-2009. He received his BS from Zhejiang University in 1984 and MS/PhD from National University of Defense Technology in 1984 and 1992 respectively. He progressed from assistant professor (1992) to associate professor (1995) to full professor (2001). His research focuses on knowledge engineering and software engineering, with special interests in knowledge graphs, self-adaptive systems, and deep learning applications. Current research directions include Self-Adaptive Software in Human-Cyber-Physical Systems, Crowd-based Requirements Engineering, and Learning from both Natural Language and Programming Language. His work bridges theoretical knowledge engineering with practical software development challenges. His recent publications demonstrate a strong trend toward applying large language models and AI techniques to traditional software engineering problems, particularly in requirements engineering, code generation, and vulnerability detection. The articles span multiple high-impact venues including ASE, ICSE, and RE, with significant focus on aerospace applications and multi-agent collaboration approaches. Scientific honors include: Winner of National Science Fund for Distinguished Young Scholars (2006) Project 973 project lead scientist (2014) Member of Discipline Appraisal Group of the Academic Degree Commission (2015) Multiple ACM Distinguished Paper Awards He serves in numerous editorial roles including Associate Editor for IEEE Transactions on Software Engineering (2018-present) and IEEE Transactions on Reliability (2019-present). He is also an Editorial Board Member for Empirical Software Engineering and Requirements Engineering Journal, and holds leadership positions in the China Computer Federation. His extensive conference service includes PC membership for ICSE, FSE, RE, and other major software engineering venues.

Reid Holmesمشاهده پروفایل

استاد

• Software Engineering
• Software Testing
• Code Search
• +۷ مورد دیگر

Reid Holmes is a Professor in the Software Practices Lab at the University of British Columbia's Department of Computer Science, Faculty of Science. With over 15 years of academic experience, he has progressed from Assistant Professor at the University of Waterloo (2010-2015) to Associate Professor (2015-2022) and now full Professor (2022-present) at UBC. His research spans multiple dimensions of software engineering with a strong emphasis on the human aspects of development. Dr. Holmes' research interests focus on understanding the problems software engineers encounter when creating and evolving software systems. His work examines software testing and validation, source code reuse, code search, context-sensitive example location, API understanding, speculative analysis, code review, and team awareness. He believes that by better understanding how people create, explore, evolve, and reason about software systems, we can enhance developers' effectiveness and improve software quality. His research is characterized by its practical application to real-world software development challenges. His recent publications demonstrate a consistent focus on improving developer productivity through better tools and understanding of developer behavior. The publications reveal a trajectory from traditional software engineering topics toward newer areas involving AI-assisted development, with particular attention to how generative AI affects workflow and collaboration. His work consistently bridges theoretical software engineering concepts with practical developer experience considerations. ACM SIGSOFT Distinguished Paper Award (multiple times) FSE 2024 Most Impactful Paper Award 2024 ICSE Most Influential Paper Award Winner of the 2008 MSR Mining Challenge As an educator and mentor, Dr. Holmes has supervised numerous graduate students who have gone on to successful careers in academia and industry. He has served on program committees for major software engineering conferences including ICSE, FSE, and MSR, and has been an Associate Editor for TSE since 2016. His work on developer tools like CodeShovel, Devy, and Baker demonstrates his commitment to creating practical solutions that address real developer pain points.

Reyhaneh Jabbarvandمشاهده پروفایل

استادیار

• Neural Program Analysis
• Software Testing
• Bug Localization
• +۴ مورد دیگر

Reyhaneh Jabbarvand is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, where she leads the Intelligent CAT Lab. Her research focuses on improving software quality, reliability, and maintenance through neuro-symbolic approaches that combine AI techniques with formal methods. Her research interests span Neural Program Analysis, Software Testing (with emphasis on mobile apps and autonomous software), Bug Localization, and Applied Optimization for Software Analysis. She has made significant contributions to the fields of energy testing for Android applications, neuro-symbolic approaches for code analysis, and large language models for software engineering tasks. Dr. Jabbarvand's recent publications reveal strong trends in applying machine learning to software engineering problems, particularly using neuro-symbolic methods to bridge the gap between deep learning and formal program analysis. Her work on code translation, test flakiness, and test oracle generation demonstrates her focus on practical applications of AI in software development workflows. Google PhD Fellowship in Programming Technology and Software Engineering Rising Star in EECS NSF CAREER Award Dr. Jabbarvand has received research funding from multiple sources including NSF, IBM Research, and C3.ai. She actively mentors students through her Intelligent CAT Lab and has served on numerous program committees for major software engineering conferences including ICSE, FSE, and ISSTA. She teaches courses on Advanced Topics in Software Engineering, ML for Code, and Software Engineering I. Her lab focuses on neuro-symbolic approaches to software engineering problems, bringing together PhD, undergraduate, and high school students to tackle challenges in AI-assisted software development and testing.

Paul Mollièreمشاهده پروفایل

پژوهشگر

• Exoplanet Atmospheres
• Atmospheric Modeling
• Spectral Retrieval
• +۲ مورد دیگر

Paul Mollière is a Researcher and group leader in the Atmospheric Physics of Exoplanets department at the Max Planck Institute for Astronomy (MPIA) in Heidelberg, Germany. His work focuses on developing atmospheric models to interpret observational data of exoplanets, with specialized emphasis on cloudy gas giant planets. His research spans Exoplanet Atmospheres , Atmospheric Modeling , and Spectral Retrieval , utilizing advanced computational techniques to simulate atmospheric structures and compositions. His group compares these models with high-precision observations from the James Webb Space Telescope (JWST), Hubble Space Telescope (HST), VLTI-GRAVITY, and CRIRES+, leveraging the open-source radiative transfer code petitRADTRANS —a key tool he developed for atmospheric retrieval analyses in the exoplanet community. As leader of the "Atmospheric modeling and retrieval" group, Dr. Mollière directs a research team advancing exoplanetary science through innovative modeling frameworks and close collaboration with observational astronomers, targeting next-generation telescope data to refine atmospheric characterization.

Vera Krewaldمشاهده پروفایل

استاد

• Quantum Chemistry
• Theoretical Chemistry
• Magnetic Interactions
• +۶ مورد دیگر

Prof. Dr. Vera Krewald is a Professor for Quantum Chemistry at Technische Universität Darmstadt, Department of Chemistry. She leads a research group focused on theoretical and quantum chemistry approaches to understand electronic structures and properties of inorganic and transition metal complexes. Her work bridges computational methods with experimental spectroscopy to explore magnetic interactions, electron transfer processes, and catalytic mechanisms. Professor for Quantum Chemistry (W3) at TU Darmstadt (since 11/2023) Professor for Theoretical Chemistry (W2, tenure track) at TU Darmstadt (12/2018-10/2023) Research Group Leader at University of Bath (01/2017-11/2018) Prof. Krewald's research focuses on applying quantum chemistry methods to understand the electronic structure and functioning of inorganic complexes. Her group makes predictions about spectroscopic, magnetic, and other measurable properties of transition metal complexes, with particular interest in systems that exhibit unexpected properties, magnetic coupling, challenging molecular transformations, or promising catalytic activity. Key research areas include electron transfer processes, photophysics and photochemistry of transition metal complexes, nitrogen activation and splitting, oxygen reduction catalysis, and the development of theoretical methods like the Angular Overlap Model. Analysis of Prof. Krewald's recent publications reveals a strong focus on iron-based catalysis, particularly for energy-related applications like the oxygen reduction reaction in fuel cells. Her work frequently combines computational quantum chemistry with experimental spectroscopy, especially Mössbauer spectroscopy, to characterize active sites in catalysts. There's also significant emphasis on electron transfer processes, photochemical activation of small molecules like dinitrogen, and the development of computational tools for analyzing magnetic properties and metal-ligand bonding. 2022: Dozentenpreis from the chemical industry fund (Fonds der Chemischen Industrie) 2021: Award from the Dr. Hans Messer Stiftung for early career researchers 2021: ADUC Award from the German association of university professors in chemistry 2014: Otto Hahn Medal of the Max-Planck-Society 2013: Participant at 63rd Lindau Nobel Laureate Meeting 2008-2013: German National Academic Foundation fellowship Prof. Krewald leads a research group with 2 postdocs, 6 PhD candidates, and several B.Sc./M.Sc. students. Her group has secured funding from multiple sources including the DFG, Leverhulme Trust, Merck'sche Gesellschaft für Kunst und Wissenschaft e.V., NHR Verein e.V., and Deutsche Bundesstiftung Umwelt. She serves as vice-speaker of SFB 1487 "Iron, upgraded!" (2022-2025), demonstrating her leadership in coordinated research efforts. Her group actively collaborates with experimental researchers to elucidate reaction mechanisms and identify catalytically active species. The Krewald Research Group operates within the Department of Chemistry at TU Darmstadt, with strong connections to collaborative research centers including SFB 1487 "Iron, reimagined!", SFB 1633 "Pushing Electrons with Protons", and SPP 2491 "Interactive Switching of Spin States". The group is also involved in the Quantum Bio-Inorganic Chemistry Society, which Prof. Krewald co-founded and serves as Secretary General. Their work combines high-level quantum chemical calculations with experimental validation to address fundamental questions in inorganic chemistry and catalysis.

Matthias Hagenمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Web Data Mining
• +۳ مورد دیگر

Matthias Hagen is Professor of Databases and Information Systems at Friedrich-Schiller-Universität Jena. His research focuses on information retrieval (query understanding, conversational search, comparative questions, known-item search, user simulation), natural language processing (clickbait, argumentation), and web data mining. He earned his Ph.D. from Friedrich-Schiller-Universität Jena with a thesis on algorithmic complexity, and previously led research groups at Bauhaus-Universität Weimar and Martin-Luther-Universität Halle-Wittenberg. His current work develops novel methods for retrieval-augmented generation evaluation, neural information retrieval efficiency, and user-centered search systems. Recent publications examine crowdsourcing for RAG evaluation, LLM-based relevance assessment, corpus subsampling techniques, and child-friendly web search evaluation frameworks. He contributes to open web search initiatives and develops tools like the TIREx Tracker for experimental reproducibility in IR research. Dr. Hagen serves on program committees for major conferences including SIGIR, ECIR, and ACL. His research group participates in competitive evaluations such as TREC, CLEF, and Touché. Recent projects explore axiomatic approaches to retrieval, argumentation systems, and the impact of search result quality on decision-making.

• Human-Computer Interaction
• Automated Vehicles
• User Interface Design
• +۷ مورد دیگر

Enrico Rukzio is a Professor at the University of Ulm, Germany, leading a prominent research group focused on human-computer interaction with particular emphasis on automotive user interfaces, mixed reality, and accessibility. His research spans multiple domains including automated vehicles, virtual reality, and sustainable interaction design, with consistent publication output in top-tier venues such as CHI, UIST, and AutomotiveUI. Professor Rukzio's research interests center around the intersection of human factors and emerging technologies. His work investigates how users interact with automated systems, particularly in transportation contexts, with significant contributions to external vehicle communication, in-vehicle interfaces for automated driving, and accessibility solutions for diverse user populations. His research group has pioneered methods for optimizing user interfaces through Bayesian optimization and has conducted extensive studies on user acceptance of automated vehicle technologies. His publication portfolio reveals a clear trajectory of research evolution from foundational HCI work to specialized applications in automated transportation systems. Recent work shows increasing focus on accessibility aspects of automated vehicles, particularly for users with visual impairments, as well as exploration of emerging domains like Urban Air Mobility. The research demonstrates strong methodological diversity, incorporating controlled experiments, field studies, and computational optimization techniques. Professor Rukzio has supervised numerous doctoral students who have become active researchers in the field, including Mark Colley, Pascal Jansen, and Luca-Maxim Meinhardt. His research group maintains strong international collaborations and has secured funding for multiple projects at the forefront of automotive user experience research. The group's work has practical implications for automotive manufacturers and technology developers creating next-generation transportation interfaces.

• Computer Architecture
• Parallel Processing
• FPGA Design
• +۳ مورد دیگر

Frank Hannig is a Professor at the University of Erlangen-Nuremberg, Germany, specializing in computer architecture and high-performance computing. His research focuses on FPGA acceleration, hardware-software co-design, neural network optimization, and embedded systems. He has collaborated extensively with co-authors such as Jürgen Teich and Oliver Reiche, producing over 200 publications since 2001. His work emphasizes domain-specific languages (DSLs) for image processing (e.g., Hipacc) and compiler optimizations for FPGAs. Key contributions include techniques for quantized neural networks on microcontrollers, CGRA toolchain evaluation, and efficient mapping of CNNs onto processor arrays. Hannig also explores energy-efficient architectures and reconfigurable computing for emerging applications like edge AI and automotive systems. Publications span conferences like ASAP, FPL, and ARC, reflecting his interdisciplinary approach to bridging algorithm design and hardware implementation. His research often addresses practical challenges in deploying machine learning models on resource-constrained devices while maintaining performance and energy efficiency.

Anne Lauscherمشاهده پروفایل

پژوهشگر

• Argumentation Mining
• Scientific Publication Mining
• Scitorics
• +۴ مورد دیگر

Dr. Anne Lauscher is a Researcher at the Data and Web Science Group, University of Mannheim, School of Business Informatics and Mathematics. Her work focuses on neural methods for natural language understanding, particularly in Argument Mining, Scientific Publication Mining (scitorics), and ethical considerations in NLP. She investigates language representations, bias detection in models, and fair AI. She has been involved in projects like the Linked Open Citation Database (LOC-DB) and contributes to conferences like ACL and EMNLP. She has taught courses on Text Analytics, Machine Learning, and Ethics in NLP, and supervised theses on topics like detecting unfairness in Arabic text representations and hyperpartisan news detection. Her research bridges computational linguistics with societal ethics, aiming to improve fairness and interpretability in AI systems. Her research interests span Argumentation Mining, Scientific Publication Mining, scitorics, Transfer Learning, Representation Learning, and Ethics in NLP. She explores how to inject knowledge into language models and analyze biases in text representations. Her work on 'scitorics' involves analyzing rhetorical aspects of scientific writing to understand argumentation structures. Publications highlight contributions to bias mitigation (e.g., DebIE platform), specialized language models, and multilingual datasets like Multi2WOZ. Her work appears in top venues such as EMNLP, ACL, and COLING. She actively contributes to the academic community through roles like Publication Co-Chair for EurNLP 2020 and PC member for multiple conferences. Her advising includes guiding students on thesis projects addressing fairness, argumentation, and NLP applications. She collaborates with institutions like Stuttgart Media University and the Data and Web Science Group to advance research in ethical AI and NLP.