جست‌وجوی استاد و پژوهشگر

• Augmented Reality
• Conceptual Modeling
• Enterprise Modeling
• +۳ مورد دیگر

Dr. Fabian Muff is a Senior Researcher at the Department of Informatics, University of Fribourg, within the Interfaculty Informatics unit. He holds a position as Oberassistent (Senior Research Assistant) specializing in Digitalization and Information Systems. His research focuses on the intersection of conceptual modeling with augmented reality (AR) and virtual reality (VR), emphasizing workflow modeling languages, AR application frameworks, and AI integration in modeling tools. He teaches courses on Requirements Engineering, Information Systems Modeling, and Project Development. Dr. Muff's work addresses challenges in hybrid modeling environments, contextual AR applications, and the limitations of large language models in conceptual modeling. His recent projects include developing the M2AR web-based modeling tool and exploring the use of ChatGPT in schema generation. He actively collaborates with academic and industry partners, publishing widely in venues like ER Conference and AAAI workshops. His contributions span technical innovations in AR modeling languages (e.g., ARWFML) and methodological advancements in combining conceptual modeling with emerging technologies. Current research explores AI-augmented visualization, context-aware AR systems, and the ethical implications of digital-physical integration in enterprise systems.

• Digital Linguistics
• Writing Technology and Document Engineering
• Linguistic Analysis and Modeling of Writing Processes
• +۴ مورد دیگر

Cerstin Mahlow is a Professor of Digital Linguistics and Writing Research at the ZHAW School of Applied Linguistics , where she leads initiatives like the CAS Texten: effektiv schreiben und Generative KI effizient nutzen . Her work bridges linguistic theory with computational methods, focusing on writing process analysis, NLP-based authoring tools, and socio-cultural impacts of digital transformation in education. PhD in Computational Linguistics (University of Zurich, 2011) M.A. in Computational Linguistics, Spanish Linguistics, and Political Science (Erlangen-Nürnberg, 2008) Continuing Education in E-Learning (ETH Zurich/University of Zurich, 2003) Her research explores linguistic modeling of writing processes using digital tools, with a focus on sentence-centric analysis and AI-driven writing support. She investigates how large language models (LLMs) and generative AI reshape academic writing practices, authoring workflows, and literacy requirements. Recent publications highlight trends in writing analytics , including sentence history modeling (2025), feedback revision strategies (2023), and cognitive writing aids (2022). She co-leads projects like Computational Competencies for Non-Tech Students and Potentials of Advanced Text Technologies , emphasizing machine learning applications. Prof. Mahlow serves on the steering committees of the ACM Symposium on Document Engineering and coordinates the SIG Writing special interest group under EARLI. She actively contributes to networks like Girls Can Code Switzerland and the Association for Computational Linguistics .

Hans-Georg Fillمشاهده پروفایل

استاد

• Conceptual Modeling
• Business Informatics
• Meta Modeling
• +۱۰ مورد دیگر

Hans-Georg Fill is a full professor in business informatics at the University of Fribourg, where he leads the Digitalization and Information Systems Group within the Faculty of Management, Economics and Social Sciences. He plays a central role in the Smart Living Lab, a joint initiative of EPFL, HEIA-FR, and the University of Fribourg, focusing on digital transformation and information systems. Prior to his current role, he held academic positions at the University of Bamberg and the University of Vienna and was a visiting scholar at Stanford University, Karlsruhe Institute of Technology, and École Nationale Supérieure des Mines. Research Interests: His primary research lies in conceptual modeling, particularly meta modeling, semantic information systems, and the application of blockchain and smart contracts in enterprise contexts. He also investigates the integration of virtual and augmented reality into enterprise modeling and explores deviceless interaction with information systems. A growing focus of his recent work is the role of large language models (LLMs) in enhancing modeling processes, knowledge representation, and automated enterprise model generation. Publication Trends: His recent publications (2023–2025) reflect a strong emphasis on the intersection of AI and conceptual modeling. Key themes include the application of LLMs in modeling, blockchain-based modeling languages, augmented reality for enterprise systems, and semantic technologies. He frequently publishes in top-tier venues such as ER, CAiSE, EMISAJ, and HICSS, often in collaboration with PhD students and international researchers. Scientific Awards and Recognition: Nominated for Best Paper Award at HICSS'51 (2018) Nominated for Best Paper Award at Modellierung 2016 and 2018 LexisNexis Top 10 Paper Award nomination (2016) Advising and Grants: He advises numerous PhD and Master’s students, including Charuta Pande, Benedikt Reitemeyer, Fabian Muff, and Simon Curty. His research is supported through institutional affiliations and collaborative projects such as the Smart Living Lab. He has also contributed to editorial leadership as co-department editor of BISE and supporting editor-in-chief of EMISAJ. Labs and Teams: He leads the Digitalization and Information Systems Group at the University of Fribourg and is a key contributor to the Smart Living Lab, a multidisciplinary research environment focused on digital innovation in living spaces and enterprise systems.

Anna [Anouk] Luhnمشاهده پروفایل

پژوهشگر ارشد

• Comparative Literature
• Digital Humanities
• Translation Studies
• +۳ مورد دیگر

Anna [Anouk] Luhn is a Research Fellow at Freie Universität Berlin, specializing in General and Comparative Literature. Her work explores the intersections of language, power structures, and media innovations, particularly through algorithmic text production and Large Language Models (LLMs). Education: PhD in General and Comparative Literature (2020), Freie Universität Berlin Her research investigates how literary communication and experimental translation practices in digital paradigms can critically engage with societal shifts in literacy and media. She co-founded the publication series con·stel·la·tions and contributes to transdisciplinary discussions on language's role in platformed societies. Contact: anna.luhn@fu-berlin.de

• Natural Language Processing
• Human Evaluation
• Reproducibility
• +۴ مورد دیگر

Anja Belz is a Professor at Dublin City University's School of Computing, specializing in Natural Language Processing research. She leads the Natural Language Processing Research Group and has established herself as a leading expert in human evaluation methodologies, reproducibility in NLP, and data-to-text generation systems. Her work bridges theoretical research and practical applications with significant contributions to medical text generation and evaluation standards. Her research interests center on creating robust evaluation frameworks for NLP systems, with particular focus on human evaluation methodologies, reproducibility assessment, and quality criteria standardization. She has pioneered work on the Human Evaluation Data Sheet (HEDS) and the QCET (Quality Criteria for Evaluation Taxonomy), addressing critical gaps in evaluation comparability across NLP research. Her work on reproducibility spans multiple shared tasks (ReproNLP, ReproGen) that have become benchmarks in the field, examining how different experimental conditions affect evaluation outcomes. Analysis of her recent publications reveals a clear research trajectory focused on making NLP evaluation more rigorous, transparent, and comparable. Her work increasingly incorporates large language models while maintaining critical scrutiny of their evaluation methodologies. She has made significant contributions to understanding when LLM-based evaluation correlates with human judgments, and has developed frameworks for assessing the reproducibility of NLP evaluation results in quantified terms. Professor Belz has organized numerous workshops and shared tasks focused on human evaluation and reproducibility in NLP, including multiple ReproNLP shared tasks that have attracted international participation. Her research has been consistently published in top-tier NLP conferences including ACL, EMNLP, and INLG, with a strong emphasis on methodological rigor and practical applicability to real-world NLP evaluation challenges. She has also contributed significantly to NLP research in under-resourced languages, particularly Irish, Welsh, Breton, and Maltese. Her work on medical text generation has practical implications for healthcare applications, particularly in automating clinical documentation and systematic reviews. She has developed methods for biomedical synthesis generation that could significantly reduce the time and cost of keeping medical practitioners updated with research. Her research on consultation checklists aims to standardize the human evaluation of medical note generation systems, addressing critical challenges in clinical safety and quality assessment.

Tom Hopeمشاهده پروفایل

استادیار

• Artificial Intelligence
• Scientific Knowledge Discovery
• Text Mining
• +۷ مورد دیگر

Tom Hope is an Assistant Professor (Senior Lecturer) at the Hebrew University of Jerusalem's School of Computer Science and Engineering, and a Research Scientist at The Allen Institute for AI (AI2). His work focuses on developing AI methods that augment and scale scientific knowledge discovery by harnessing vast repositories of scientific knowledge through literature, knowledge bases, and electronic medical records. His educational background includes a PhD with Dafna Shahaf, followed by postdoctoral research at AI2 and the University of Washington working with Daniel Weld and Eric Horvitz. Prior to his PhD, he led an applied AI research team at Intel that published award-winning work. Hope's research spans a full-stack spectrum from constructing new datasets and machine learning models to designing user-facing systems. His primary focus areas include text mining, knowledge graphs, information extraction, multimodal models, LLMs, and human-computer interaction for scientific discovery. He explores how computational approaches can transform the scientific process by helping researchers explore literature, generate hypotheses, and make informed decisions. His work has been featured in top venues including NAACL, EMNLP, ACL, CHI, AAAI, KDD, CACM, and PNAS, with coverage in Nature and Science. His recent publications reveal a strong emphasis on scientific idea generation systems, literature-based hypothesis formation, and tools for navigating scientific literature. His work often bridges AI techniques with real-world scientific challenges, particularly in biomedicine. 2022 Azrieli Early Career Faculty Fellowship (awarded to eight scientists across all fields) KDD 2017 Best Research Paper Award AKBC 2021 Outstanding paper award Selected for 2021 Global Young Scientists Summit Selected for 2019 Heidelberg Laureate Forum ELLIS Society member Member of KDD 2020 Best Paper Selection Committee Hope has advised numerous students from leading institutions including Carnegie Mellon, University of Washington, Georgia Tech, and others. His research is organized around five main thrusts: AI Inspiration systems for idea generation, Biomed Predictions for clinical applications, Knowledge Graph construction, Scientific Information Extraction, and Scientific Search engines for discovery. His work has significant implications for accelerating scientific discovery and improving clinical decision-making through evidence-based AI systems.

Alberto Bacchelliمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Software Quality
• Code Review
• +۳ مورد دیگر

Alberto Bacchelli is an Associate Professor of Empirical Software Engineering at the University of Zurich (UZH), leading the Zurich Empirical Software Engineering Team (ZEST). He joined UZH in 2017, having previously worked as an Assistant Professor at Delft University of Technology (Netherlands), where he earned tenure. His research focuses on improving software quality, developer effectiveness, and code review practices through empirical studies and tool development. Bacchelli holds a PhD from the University of Lugano, with internships at Microsoft Research. He has received prestigious awards, including the MSR Ric Holt Early Career Achievement Award (2020) and the 10-year Most Influential Paper Award from SANER. His work spans code review efficiency, software security, and developer tools. In his personal life, he balances family time with a passion for photography, particularly through his sister Chiara’s wedding photography work. Affiliations: University of Zurich (since 2017), Delft University of Technology (2013–2017), Microsoft Research (internships 2012–2013). Education: PhD in Computer Science (University of Lugano, 2013), Master/BS in Computer Science (University of Bologna), studies at Université Libre de Bruxelles. His research interests center on understanding software engineering challenges and designing tools/methods to enhance practices. Notable projects include studies on code review strategies, developer cognition, and security in collaborative environments. He emphasizes bridging theory and practice, aiming for real-world impact through tools like PyDriller and frameworks for mining software repositories. Awards highlight his contributions to code review and software engineering education. He actively engages in teaching, mentoring, and advancing open-source practices. His team, ZEST, explores topics such as code review dynamics, developer productivity, and empirical methods to improve software processes.

Pooja Raniمشاهده پروفایل

پژوهشگر ارشد

• Empirical Software Engineering
• Green Software Engineering
• Natural Language Processing
• +۴ مورد دیگر

Pooja Rani is a Senior Researcher at the Software Evolution and Architecture Lab (SEAL), Department of Informatics, University of Zurich. She holds a PhD from the University of Bern (2022) and an M.E. in Software Systems from BITS Pilani (2017). With industry experience at Samsung, VMware, and People Interactive Ltd., she bridges theoretical and practical software engineering. Her research focuses on: Empirical Software Engineering, particularly developer practices in code comprehension and maintenance. Green Software Engineering, including energy anti-patterns and sustainability tooling. NLP and machine learning applications in software evolution, documentation quality, and polyglot environments. AI-driven approaches for debugging, testing, and optimization. Her publications emphasize energy efficiency in software systems, AI-assisted development tools, code comment analysis, and metamorphic testing. Recent work explores LLMs for sustainable coding, object-centric debugging, and autonomous system validation. She actively advises students, with ongoing supervision of 15+ BSc/MSc theses on topics like comment quality, energy anti-patterns, and polyglot development. She collaborates internationally with institutions including TU Delft, Inria, and the University of Victoria.

Martin Jaggiمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Artificial Intelligence
• +۳ مورد دیگر

Martin Jaggi is an Associate Professor at EPFL, leading the Machine Learning and Optimization Laboratory (MLO). He holds academic positions within the School of Computer and Communication Sciences (IC), including roles in the SIN, SSC, and EDIC departments. His research focuses on machine learning optimization, federated learning, and large language models. He earned his PhD in Machine Learning and Optimization from ETH Zurich (2011) and a MSc in Mathematics from the same institution. Education: PhD in Machine Learning and Optimization, ETH Zurich (2011) MSc in Mathematics, ETH Zurich Research Interests: Dr. Jaggi's work integrates optimization theory with machine learning applications, emphasizing scalable algorithms and ethical AI. He explores topics like distributed learning systems, privacy-preserving techniques (e.g., federated learning), and model interpretability. His lab develops foundational methods for large language models (LLMs) and their ethical deployment. Teaching & Courses: Optimization for Machine Learning Topics in Machine Learning Systems Advising & Grants: He currently advises 9 PhD students and has supervised 11 past students. His work is supported by grants focusing on federated learning, optimization algorithms, and medical AI applications like Meditron-70b. Collaborations include projects on respiratory disease detection and mental health modeling. Labs & Teams: Academic Director of RCP-GE and oversees MLO lab, which develops open-source tools like FLamby for federated learning benchmarks and Meditron for clinical AI. The lab also advances techniques in distributed optimization and low-precision training.

Marco Hutterمشاهده پروفایل

استاد

• Robotics
• Intelligent Systems
• Autonomous Navigation
• +۴ مورد دیگر

Prof. Marco Hutter is a Full Professor at the Department of Mechanical and Process Engineering at ETH Zürich, and Deputy Head of the Institute for Robotics and Intelligent Systems. His research focuses on advanced robotics, including autonomous legged systems, robotic perception, and control strategies for challenging environments. He leads projects in areas such as forest inventory using legged robots, space exploration robotics, and mobile manipulation systems. His work emphasizes real-world deployment, with contributions to robotic navigation, sensor integration, and policy optimization. Prof. Hutter collaborates on large-scale datasets and frameworks like ROS-LLM for embodied AI, and explores applications in logistics, construction, and planetary exploration. His team develops both hardware (e.g., LEVA logistic vehicles) and software solutions (e.g., risk-guided diffusion models for safety-critical systems). Key projects include ETHcavation (construction environment understanding) and TartanGround (robot perception datasets). He pioneers work in agile locomotion policies, magnetic climbing robots, and multi-agent robotic teams for parcel delivery. His research bridges theoretical advancements with practical implementations, addressing challenges in terrain adaptation, energy efficiency, and robustness in autonomous systems.

• Natural Language Processing
• Computational Linguistics
• Dependency Parsing
• +۷ مورد دیگر

Carlos Gómez Rodríguez is a Full Professor (Catedrático de Universidad) in the Department of Computer Science at the University of A Coruña, Spain, where he leads the FASTPARSE Lab within the LyS Research Group at the CITIC Research Center. His work bridges theoretical and practical aspects of computational linguistics with significant applications in natural language processing. His primary research interests focus on natural language parsing algorithms, with particular emphasis on improving parsing speed for web-scale applications, handling non-projective dependency structures, analyzing morphologically-rich and low-resource languages, and exploring the cognitive aspects of syntax. He maintains a critical perspective on the field's current LLM-driven revolution, advocating for continued research into alternative approaches that prioritize efficiency, explainability, and scientific insight beyond user-facing applications. His recent publications reveal a strong trend toward examining the capabilities and limitations of large language models, particularly in comparison to human language processing, while continuing to advance traditional parsing techniques. His work spans theoretical foundations, practical implementations, and diverse applications including sentiment analysis, opinion mining, and creative writing evaluation. National Young Researcher Price "María Andresa Casamayor" in Mathematics and Information Technologies by Spain's Ministry of Science Honorary Member of the Royal Spanish Mathematical Society CAEPIA 2024 Best Paper Award for research on evaluating creative writing in LLMs Professor Gómez Rodríguez has successfully led significant research projects including the ERC Starting Grant project FASTPARSE, which focused on techniques to improve the speed of parsing algorithms. His work demonstrates consistent funding support for innovative research at the intersection of computational linguistics, cognitive science, and practical NLP applications. He actively collaborates with researchers across Spain and internationally, as evidenced by his extensive publication record with diverse co-authors. He leads the FASTPARSE Lab and is a core member of the LyS Research Group, focusing on advancing both theoretical understanding and practical implementations of language processing technologies. His team explores diverse aspects of natural language processing, from fundamental syntactic analysis to applied sentiment analysis and innovative applications of language technology in domains like healthcare.

Filip Ilievskiمشاهده پروفایل

استادیار

• Commonsense Reasoning
• Neurosymbolic AI
• Analogy and Abstraction
• +۱۱ مورد دیگر

Filip Ilievski serves as an Assistant Professor of Commonsense AI (Sr.) at Vrije Universiteit Amsterdam, where he leads the Learning and Reasoning Group. He holds additional affiliations as an Affiliated Scientist at USC Information Sciences Institute and Amsterdam Sustainability Institute, and serves as Scientific Coordinator of the Digital Sustainability Institute (DiSC). His academic journey includes a PhD from Vrije Universiteit Amsterdam (2015-2019), research positions at USC (2019-2023), and a research visit to Carnegie Mellon University (2017). Dr. Ilievski's research focuses on advancing human-centric AI with common sense for social good applications. His work spans three interconnected areas: commonsense reasoning (including situational awareness, numeracy, and modeling of other agents), analogy and abstraction (studying cognitive generalization mechanisms through narratives and lateral thinking puzzles), and AI for social good (interpreting complex online media like internet memes and developing knowledge-based solutions for sustainable policies). He employs neuro-symbolic methods including prototype-based networks, combining LLMs with deterministic engines, and reasoning with scene knowledge graphs to create robust, interpretable AI systems. His publication record shows a strong trend toward multimodal reasoning and practical applications of commonsense AI, particularly in understanding internet memes, developing sustainable AI solutions, and creating robust evaluation frameworks like the MARVEL benchmark. Recent work increasingly integrates cognitive theories with neural architectures to address limitations in current foundation models, with particular emphasis on explainability and cultural context awareness. Best Student Paper Award at K-CAP for 'Knowledge-enhanced Agents for Interactive Text Games' Best Paper and Best Presentation Award at Workshop on Multimodal Content Analysis for Social Good (MM4SG) Book 'Human-Centric AI with Common Sense' published in Springer Nature Synthesis series Dr. Ilievski actively supervises a growing team of PhD students and postdocs, with current projects focusing on visual commonsense reasoning, meme semantics, analogical abstraction, multimodal alignment, and architectures for human-centric AI. His Digital Sustainability Institute (DiSC) coordinates research on knowledge-driven AI for sustainable policies, while his Situated AI minor program develops next-generation AI education. He maintains active collaborations with institutions including USC, CMU, RPI, University of Lyon, UvA, University of Bielefeld, and industry partners like Bosch Research, NEC Labs, Merit Technologies, and Tencent.

Bingcong Liمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Signal Processing
• +۳ مورد دیگر

Bingcong Li is a postdoctoral researcher at ETH Zurich collaborating with Prof. Niao He and the ODI group. Previously, they completed doctoral studies at the University of Minnesota under Prof. Georgios B. Giannakis, followed by industry experience focused on large language models (LLMs). Education includes a PhD from the University of Minnesota under Prof. Georgios B. Giannakis. Research centers on making computation efficient, accessible, and affordable across heterogeneous resources—from GPU clusters to consumer hardware—through interdisciplinary approaches combining deep learning, optimization, and signal processing. Key research areas address foundational computing architectures, large-scale system sustainability, and personalized AI access. Their work develops theoretically grounded methods for explainable systems, with recent focus on LLM fine-tuning efficiency. Publication trends show consistent contributions to top conferences (NeurIPS, ICML, ICLR) with emphasis on optimization techniques for resource-constrained LLM deployment. Their advising and grant activities aren't explicitly detailed, though they actively participate in academic service through conference talks (EUROPT 2025, ICASSP 2025) and co-organizing events like the Efficient LLMs Fine-tuning Track at AI+X Summit. Lab affiliation centers on ETH Zurich's ODI group under Prof. Niao He, focusing on optimization-driven AI solutions.

• Machine Translation
• Natural Language Processing
• Computational Linguistics
• +۷ مورد دیگر

Pierrette Bouillon is affiliated with the University of Geneva as a researcher, focusing on machine translation, accessible communication, and language technology applications in healthcare. Her work bridges computational linguistics, speech processing, and inclusive design. Institution: University of Geneva Research Focus: Medical translation, sign language processing, text/pictograph translation Research Interests : Bouillon specializes in machine translation and computational linguistics , with particular emphasis on: Accessible communication for patients with language barriers Sign language and pictograph translation systems (e.g., BabelDr) Speech-to-speech translation in emergency settings Text simplification for intellectual disabilities Historical French normalization for NLP Multilingual speech recognition and post-editing Article Trends : Her recent publications highlight the integration of large language models with accessibility tools in healthcare, focusing on Swiss French sign language corpora , pictograph sequences , and spontaneous speech simplification . Work spans both NLP and human-computer interaction in medical contexts. Education and Teaching : While specific educational details are omitted, Bouillon contributes to translation pedagogy through tools like COPECO and MT3 , integrating speech technologies into post-editing workflows.

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۳ مورد دیگر

Dr. Don Tuggener is a Researcher at the ZHAW Zurich University of Applied Sciences' School of Engineering, affiliated with the Centre for Artificial Intelligence. His work focuses on Natural Language Processing (NLP), Computational Linguistics, and Machine Learning applications in professional training and AI evaluation. He leads or collaborates on projects such as Meaning@Work (stress management for healthcare professionals), Virtual Kids (child interrogation training), and Favi-Score (bias detection in AI evaluation). His research emphasizes dialogue systems, generative AI ethics, and legal text analysis. Key projects include developing frameworks for conversational AI evaluation (Spot The Bot), large-scale legal text classification (LEDGAR corpus), and innovative training tools using LLMs. He has contributed to over 30 peer-reviewed publications since 2011, spanning topics like compound splitting in German, coreference resolution, and generative AI applications in professional contexts. As an editor for SwissText conferences and reviewer for ACL/EMNLP, he actively participates in the NLP academic community. Project Leadership: Virtual Kids, Interscriber Deputy Leadership: AutoNews, PRISM, SCAI Core Research Themes: AI ethics, dialogue systems, legal NLP Recent work highlights AI's role in professional education (e.g., investigative interviews with children) and societal challenges (hate speech mitigation via social influencers).