Kasimir Forth is a Researcher at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, affiliated with the Professorship for Circular Engineering for Architecture. He is completing his PhD at Technical University of Munich (TUM) on BIM-based semantic enrichment using Large Language Models under Prof. André Borrmann. His research focuses on automating environmental assessments for sustainable buildings using digital methods like BIM and LLMs, particularly in early design stages. Currently, he contributes to the SWIRCULAR project, developing automated Digital Product Passports via BIM. Education: B.Sc. in Engineering Science (TUM) M.Sc. in Energy-Efficient and Sustainable Building (TUM, Distinction) Research Interests: BIM integration in sustainability analysis AI-driven semantic enrichment for environmental metrics Disassembly potential and circular economy applications Automated material passport generation Professional Experience: Former Managing Director of TUM’s Leonhard Obermeyer Center (Digital Methods for Built Environment) Consultant at Drees & Sommer (Energy Design/Sustainable Building) Key Projects: SWIRCULAR (automated Digital Product Passports), BIM4EarlyLCA (uncertain LCA visualization). Labs/Teams: Active in ETH Zurich’s Circular Engineering group and TUM’s Computational Modeling & Simulation lab.
Andreas Marfurt is a Lecturer at Lucerne University of Applied Sciences and Arts (HSLU) in the School of Computer Science and Information Technology, where he has been teaching and conducting research since September 2022. His academic background includes a PhD from EPFL focused on Interpretable Representation Learning for Abstractive Summarization , a postdoc at Idiap Research Institute, and BSc/MSc degrees in Computer Science from ETH Zurich. Research Focus: Marfurt specializes in Natural Language Processing (NLP) and Machine Learning Operations (MLOps), with particular interests in: Developing reliable NLP systems for document summarization and question answering Minimizing hallucinations in language models Practical applications of retrieval-augmented generation (RAG) Multimodal learning systems Interpretable AI and model evaluation He contributes to several research projects including: Music DNA - Personalised Event Matching P2Sr Profila Privacy Simplified reloaded ARGUMENT REPRESENTATION LEARNING (ARL) IAC (Intelligent Automation Components) As part of HSLU's NLP research group, Marfurt develops specialized systems for legal question answering, political argument analysis, and document summarization that emphasize reliability and interpretability.
Kenza Louisa Amara is an ETH AI Center Doctoral Fellow in the Department of Computer Science at ETH Zurich. Her research develops explainability methods for AI systems, including multimodal models and graph neural networks. She applies these techniques to healthcare, climate science, and social networks, with publications on LLM interpretability and power grid datasets. Amara collaborates with the IVIA Lab and Social Networks Lab at ETH. She received an ETH AI Center Doctoral Fellowship and has interned at Microsoft Research and Meta AI.
Eneko Agirre is a Professor in the Department of Computer Science and Artificial Intelligence at the Faculty of Informatics, University of the Basque Country. He is a leading researcher in Natural Language Processing with a strong focus on multilingual systems, particularly for the Basque language, and has published extensively in top NLP conferences including ACL, EMNLP, and NAACL. His research spans multiple areas of computational linguistics including word sense disambiguation, machine translation, cross-lingual learning, and information extraction. Agirre has pioneered work on zero-shot learning approaches, data contamination issues in LLM evaluation, and low-resource language processing. His recent work includes developing the Latxa family of Basque language models and creating novel methods like WiCkeD for more challenging benchmarks and GUIDEX for zero-shot information extraction. Analysis of his recent publications reveals a strong trend toward addressing fundamental challenges in large language model adaptation, evaluation reliability, and cross-lingual transfer learning. His work often combines theoretical insights with practical applications, particularly for under-resourced languages like Basque. His research demonstrates how linguistic typology impacts cross-lingual performance and how to overcome data limitations through innovative methodology. Agirre has mentored numerous researchers who have become active contributors to the NLP field, including Oscar Sainz, Jon Ander Campos, and Iker García-Ferrero. His collaborative work spans institutions across Spain and internationally, reflecting his standing in the global NLP community.
Joyce Chai is a prominent academic researcher in computational linguistics and AI with extensive contributions to grounded language learning, embodied agents, and human-machine collaboration. Her work spans vision-language models, theory of mind implementation, and task guidance systems. Key research themes: Language grounding in physical/social contexts Embodied AI and situated reasoning Interactive learning frameworks Zero-shot and continual concept acquisition Recent publications demonstrate her leadership in: Developing TRAVER for coding tutoring agents Creating W2W grounded language model Advancing theory of mind evaluation in LLMs Establishing HAR reasoning strategies for coherent physical reasoning She has mentored numerous students including Ziqiao Ma, Shane Storks, and Yuwei Bao. Her work appears in top venues like ACL, EMNLP, and NAACL with focus on practical applications like cake-making guidance systems (WTaG) and autonomous driving dialogue (DOROTHIE). Technical contributions include: MetaReVision retrieval-enhanced meta-learning EpiCA network for compositional concept recognition Neuro-symbolic DANLI agent architecture Pragmatic Rational Speaker framework
Teresa Schneider is a Research Associate at the Institute for Social Work and Law within the Lucerne University of Applied Sciences and Arts . She holds a PhD in Psychology (2023) from the University of Marburg and Maastricht, alongside a Master’s in Legal Psychology from the Psychologische Hochschule Berlin. Education: PhD in Psychology (2023), Philipps-Universität Marburg & Universität Maastricht M.Sc. in Legal Psychology (2023), Psychologische Hochschule Berlin M.Sc. in Psychology and Law (2015), Universität Maastricht B.Sc. in Psychology (2014), Eberhard-Karls-Universität Tübingen Her research focuses on psychology and law , particularly in improving children's questioning processes in legal contexts. She co-develops interactive interrogation training (invetra) and contributes to the Virtual Kids project, which uses virtual characters to enhance child interview quality. Recent publications explore the application of large language models (LLMs) in simulating investigative interviews with children. She also co-founded the European Registry of Exonerations (EUREX) , an online database for European wrongful convictions.
Reto Gubelmann is a postdoctoral researcher at the Text Technologies department within the University of Zurich's Digital Society Initiative (DSI). His work bridges computational linguistics , legal informatics , and philosophical analysis , focusing on large language models (LLMs) in normative contexts. His research explores pragmatic reasoning , argumentation theory , and the philosophical limitations of LLMs , particularly in understanding legal and ethical frameworks. Recent publications analyze LLMs' handling of negation , speech acts , and natural language inference through empirical and theoretical lenses. Key trends in his work include the dialectical shift in computational argumentation , symbol grounding in AI, and the epistemological foundations of neural language models . His interdisciplinary approach connects machine learning with philosophy of language and legal reasoning .
Dr. Steffen Eger is a professor at the Technical University of Darmstadt, focusing on Natural Language Processing, Machine Translation, and Computational Linguistics. His work spans both theoretical and applied aspects of NLP, including adversarial robustness, evaluation metrics, and creative text generation. Key Contributions: • Pioneering research in LLM-based evaluation metrics • Development of efficient translation quality frameworks • Analysis of social solidarity in historical discourse • Exploration of poetic creativity through neural models His recent publications demonstrate expertise in: - Human-Aware Translation Evaluation - Summarization of Historical Texts - Adversarial Defense Mechanisms - Explainable AI for Generation Metrics While no explicit awards or student supervision details are provided, his collaborative work appears across major NLP venues like ACL, EMNLP, and COLING. His research frequently intersects with human-centric NLP tasks, emphasizing reproducibility and robustness in system design.
Dongyang Fan is a Researcher at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Institute of Computer Science. She works in the Machine Learning and Optimization Laboratory (MLO) under EPFL's Doctoral Program in Computer and Communication Sciences, conducting cutting-edge research in artificial intelligence and machine learning. Her research spans Machine Learning, Deep Learning, Natural Language Processing, Optimization, Federated Learning, Mixture of Experts architectures, On-device Learning, and Ethics in AI. She focuses on developing efficient, ethical, and personalized AI systems with practical applications in edge computing and collaborative environments, addressing challenges in model training, data valuation, and real-world deployment constraints. Analysis of her recent publications reveals a concentrated effort on large language models (LLMs), with significant contributions to on-device collaborative learning frameworks, Mixture of Experts optimization, and ethical implications of web data usage. Her work bridges theoretical advancements in model architecture with practical implementations for resource-constrained devices, emphasizing privacy-preserving techniques and fairness-aware design principles. She is an integral member of EPFL's Machine Learning and Optimization Laboratory, contributing to research teams focused on next-generation AI algorithms and their optimization for real-world applications in distributed systems and edge computing environments.
Dr. Guang Lu is a Lecturer at the Lucerne School of Business, affiliated with the Institute of Communication and Marketing (IKM) and its CC Communication & Marketing Technologies division. He holds a Ph.D. in Energy Science and Engineering from ETH Zurich (2016), an M.Sc. in Mechatronics from Southeast University (2010), and a B.Sc. in Mechanical Design from Southeast University (2007). His work bridges computational mechanics, marketing technologies, and AI-driven solutions for societal challenges. Dr. Lu’s research focuses on AI applications in elder care (e.g., emotion-aware chatbots), NLP for corporate culture analysis, and sustainability in e-commerce. He has pioneered studies on sharing economy dynamics, algorithmic nudging for eco-friendly consumer behavior, and greenwashing detection via ESG report analysis. His technical expertise spans computational fluid dynamics, granular mechanics (e.g., rockfall simulations), and multimodal emotion recognition systems. His career includes roles as a Computational Mechanics Engineer at WSL SLF (2017–2019), Researcher/Teaching Assistant at ETH Zurich (2010–2016), and Engineer at Southeast University’s Robotics Center (2007–2010). He has published over 39 peer-reviewed articles, with recent work emphasizing AI ethics in elder care technology and data-driven marketing strategies. Dr. Lu collaborates with industry partners like GPTW Switzerland AG and has conducted projects on corporate culture assessment, chatbot localization for German-speaking elderly populations, and algorithmic solutions for sustainable consumer choices. His research often integrates interdisciplinary methods, merging natural language processing with engineering systems to address modern business and societal challenges.
Tingyu Yu is a Research Fellow currently based in Zürich, Switzerland. Their work focuses on applying artificial intelligence methodologies to climate science and environmental policy challenges. Key areas of research include automated fact-checking of climate-related claims, integration of AI with expert knowledge in global environmental assessments, and development of conversational AI tools for climate education. Yu's contributions emphasize the ethical application of large language models (LLMs) to address disinformation and democratize access to sustainability data. They have collaborated extensively with interdisciplinary teams on projects such as CHATREPORT and ChatClimate, which aim to bridge gaps between cutting-edge AI and real-world environmental decision-making. Recent publications highlight Yu's expertise in leveraging machine learning for climate communication, environmental policy analysis, and sustainable reporting mechanisms. Their research frequently intersects machine learning, natural language processing, and climate science to create actionable tools for stakeholders in academia, policy, and industry.
Meysam Alizadeh is a Research Fellow at the Department of Political Science, University of Zurich. His work focuses on digital media governance, social network analysis, and the application of artificial intelligence in political and social contexts. He investigates issues such as platform governance, content moderation, fake news propagation, and the impact of social media on political discourse. Alizadeh has collaborated extensively on projects involving large language models (LLMs) for text annotation, comparing their performance to human workers, and developing methods to detect information operations and hate speech. His research bridges computer science, political science, and data science, addressing both theoretical and applied challenges in digital society. Key areas of investigation include analyzing cryptocurrency market dynamics through social media data, exploring the relationship between Russian information campaigns and hate crimes, and examining conspiracy theory proliferation during the pandemic. He has contributed to methodological advancements in creating national random samples of Twitter users and optimizing LLM-based tools for academic and practical applications. Alizadeh's publications span journals like Political Communication , Scientific Reports , and Proceedings of the National Academy of Sciences , reflecting interdisciplinary collaboration with institutions worldwide. His research often emphasizes the ethical and policy implications of emerging technologies in public communication and governance.
Andrea Raballo is a Full Professor at the University of Lugano (USI) within the Faculty of Biomedical Sciences. His research focuses on prevention of mental disorders, psychopathology, and youth mental health, particularly in schizophrenia spectrum disorders and clinical high-risk (CHR-P) populations. He leads projects addressing early intervention strategies, diagnostic accuracy, and the neurodevelopmental underpinnings of psychopathology. His work integrates AI-driven methodologies (e.g., LLMs, adaptive RAG systems) for mental health screening and psychometric analysis. Research Interests Psychosis spectrum disorders and their early detection Neurodevelopmental models of self-disorders Transdiagnostic frameworks for youth mental health Evidence-based interventions in clinical high-risk populations Psychiatric diagnosis beyond traditional heuristics His recent publications emphasize challenges in CHR-P management, including pharmacological transparency and baseline treatment effects. He advocates for methodological rigor in prognostic precision and early intervention services to bridge gaps between child and adult mental health systems. Advising & Grants No formal advisees listed. His research is supported by funded projects focusing on translational psychiatry and clinical staging frameworks. Labs/Teams Part of the Parma Early Psychosis Program and contributes to international initiatives like ENIGMA and the EPA Summer School on Research.
Lonneke van der Plas is an Associate Professor at the Institute of Argumentation, Linguistics and Semiotics within the Faculty of Communication, Culture and Society at Università della Svizzera italiana (USI), and an Adjunct Professor at the Faculty of Informatics, USI, since October 2024. She also serves as the group leader of the Computation, Cognition & Language research group at the Idiap Research Institute in Martigny, a position she has held since February 2021. Her academic background includes: PhD in Humanities Computing, University of Groningen M.Phil in Computer Speech and Language Processing, University of Cambridge Postdoctoral research at the University of Geneva (CLASSiC project) Junior Professor at the University of Stuttgart (IMS, SFB 732) Associate Professor at the University of Malta (2014–2020) Her research interests span Natural Language Processing , Computational Linguistics , Distributional Semantics , Multilingual NLP , Computational Creativity , and Low-Resource Languages . She integrates insights from cognitive science, linguistics, and computer science to model language as a tool for creative thinking and reasoning. Her work includes semantic role labeling, cross-lingual transfer, medical question answering, and lexical innovation. The 15 most recent publications reflect a strong trend in interdisciplinary NLP research, combining linguistic theory with machine learning. Topics include lexical innovation, multilingual financial NLP, skill extraction, multi-modal fact checking, and cognitive modeling. The articles demonstrate expertise in both theoretical and applied NLP, with applications in healthcare, finance, education, and AI ethics. Key subfields include semantic role labeling, cross-lingual transfer, bootstrapping for low-resource languages, and structured knowledge integration. Scientific recognitions include: DSI Fellow, University of Zurich (2019–2020) Erasmus Mundus LCT Visiting Scholar at Shanghai Jiao Tong University and University of Melbourne (2016) Visiting Academic at Macquarie University, Sydney (2007) She has advised multiple PhD students including Stefan Müller, Patrick Ziering, Molly Petersen, Mete Ismayilzada, and Diego Rossini. She currently leads several major funded projects: NCCR Evolving Language (SNSF, PI), C-LING (SNSF, PI), SEM24 (Innosuisse, PI), and FactCheck (Hasler Foundation, co-PI). These grants support postdoctoral researchers, developers, and PhD students, and involve collaborations with institutions like EPFL, EHL, and ARCA24. Her research bridges academia and industry, with applications in HR, finance, and healthcare. Lonneke leads the Computation, Cognition & Language group at Idiap, which conducts highly interdisciplinary research involving collaborations with social scientists, cognitive scientists, linguists, and professionals in health, finance, and business. The group focuses on modeling language as a cognitive and creative tool, using computational methods to explore lexical innovation, diachronic change, and reasoning. Open PhD positions are available in areas such as NLP for cognitive modeling, multilingual NLP, and mental health applications.
Serina Chang is an Assistant Professor at the University of California, Berkeley , jointly appointed in Electrical Engineering and Computer Sciences (EECS) and Computational Precision Health , with affiliations to the Berkeley AI Research (BAIR) Lab . Her research addresses AI for public health, focusing on human behavior modeling, network inference, and policy decision support. PhD in Computer Science from Stanford University (2024) Postdoctoral research in Microsoft Research's Computational Social Science group Her work spans three thematic areas: inferring human behaviors from novel data sources (search logs, mobility data), simulating behaviors with LLMs for public health applications, and AI-driven policy support through models for vaccine distribution, epidemic forecasting, and equity-focused reopening strategies. She has pioneered AI models using anonymized mobility data to analyze socioeconomic disparities in pandemic impacts. Recent publications highlight cross-disciplinary innovations: Nature (2025) perspective on AI for infectious disease modeling ACL 2025 work on human-AI interaction evaluation ICWSM 2025 paper demonstrating LLM capabilities in network generation Scientific recognition includes: Google Research Scholar Award (2025) NSF Graduate Fellowship (2024) KDD Dissertation Award (2025) Meta PhD Fellowship (2023) She advises PhD students in EECS and Computational Precision Health , emphasizing skills in ML research , interdisciplinary collaboration , and real-world impact . Her lab integrates computational methods with public health practice through partnerships with WHO , Chan Zuckerberg Biohub , and United Nations Development Programme . Current teaching includes CS 294-286: Machine Learning & Human Behavior . She explicitly invites students with interests in AI , human behavior , network science , and public health applications to apply for PhD advising.
