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
Renato Renner is a Full Professor of Theoretical Physics and Head of the Institute for Theoretical Physics at ETH Zürich. He specializes in Quantum Information Science, Quantum Thermodynamics, and the Foundations of Quantum Physics. Born in Lucerne, he earned his physics degrees from EPF Lausanne and ETH Zurich, with a PhD focused on quantum cryptography. After postdoctoral work at the University of Cambridge, he joined ETH Zurich in 2007, progressing through academic ranks to Full Professor by 2015. His research group, the Research Group for Quantum Information Theory, explores cutting-edge topics like quantum key distribution, device-independent protocols, and the thermodynamic limits of quantum processes. His work bridges theoretical physics and applied cryptography, with contributions to quantum security, entropy accumulation, and foundational questions in quantum mechanics. Notable projects include the Space QUEST mission proposal to test quantum decoherence due to gravity and the development of rigorous security frameworks for quantum communication. Renner’s publications span foundational quantum theory, cryptographic protocols, and interdisciplinary applications of quantum information principles.
Dr. Yanwen Li is a Researcher affiliated with the Professorship for Food and Soft Materials Science at ETH Zürich's Institute of Food, Nutrition and Health. Her work focuses on advanced materials science, particularly magnetic fluids and their applications in damping, sealing, and energy harvesting systems. She explores bioinspired designs, smart materials, and multiphase fluid dynamics, with notable contributions to magnetic fluid shock absorbers and triboelectric nanogenerators. Her research bridges mechanical engineering, computational modeling, and industrial applications. Key research areas include magnetic fluid behavior under varying conditions, optimization of sealing systems, and development of adaptive damping technologies. She has pioneered lattice Boltzmann models for high-viscosity fluid flows and investigated bioinspired hexagonal structures for enhanced damping efficiency. Her work frequently integrates machine learning approaches, such as physics-informed neural networks for hydrodynamic lubrication analysis. Publications span 2018–2024, emphasizing energy conversion, vibration control, and material characterization. Notable trends include exploration of biomimetic principles, improvement of sealing technologies, and application of magnetic fluids in automotive and industrial systems. No scientific awards or student advisement records are explicitly mentioned in the provided data. Her contributions highlight interdisciplinary innovation in soft materials science and mechanical systems.
Marco Steenbergen is a Full Professor and Ordinarius in the Institute of Political Science at the University of Zurich’s Faculty of Philosophy. He teaches courses such as Advanced Methods and Statistics , Political Behavior , and Statistical Modeling , focusing on quantitative methodologies and political psychology. His research spans causal inference, electoral competition, political polarization, and identity formation, often employing statistical modeling to understand voter decision-making processes. His recent publications include work on causal inference with latent outcomes and multidimensional party polarization . He has supervised numerous PhD students in diverse topics ranging from climate politics to judicial inconsistency, reflecting his broad methodological expertise. Key Research Areas: Political methodology, statistical modeling, causal inference, electoral behavior, political polarization, and behavioral political science. Advising: Mentored 10+ PhD students across political science, international relations, and policy analysis. Publications: 15+ recent articles/books covering causal inference, populist attitudes, cleavage theory, and cross-national policy dynamics.
Horst Eidenmueller is a Statutory Professor for Commercial Law at the University of Oxford's Faculty of Law. He also serves as a Research Associate at the European Corporate Governance Institute (ECGI) in Brussels. His academic career spans multiple decades with significant contributions to corporate law, insolvency law, and the intersection of artificial intelligence with legal systems. Professor Eidenmueller's research interests focus on Corporate Law , Insolvency and Bankruptcy Law , European Company Law , and the emerging field of Artificial Intelligence and Law . His work examines how traditional legal frameworks adapt to technological advancements, particularly in dispute resolution, corporate governance, and restructuring processes. He has extensively analyzed regulatory competition in European company law and the implications of AI on legal concepts like agency, liability, and decision-making. His scholarly output reveals a clear trajectory toward increasingly technology-focused legal scholarship, with recent publications examining AI negotiators, digital dispute resolution systems, and the implications of autonomous systems on traditional legal concepts. The research spans both theoretical legal analysis and practical implications for policy-making, particularly in European contexts. While no specific scientific awards are mentioned in the provided text, Professor Eidenmueller's work has garnered significant academic attention with numerous downloads and citations across SSRN, indicating substantial scholarly impact. His academic collaborations span multiple institutions across Europe, reflecting the international nature of his research on European corporate governance and insolvency frameworks. He frequently works with colleagues from Oxford, Cambridge, and various German and European institutions, contributing to the development of comparative legal scholarship.
Katie Szilagyi is an Assistant Professor at the University of Manitoba , specializing in the intersection of law and technology . Her work critically examines legal frameworks for emerging technologies, including artificial intelligence, blockchain, and machine learning , with a focus on ethical implications and epistemological challenges. Co-author of groundbreaking research on generative AI (ChatGPT) in legal contexts (2025) Contributor to debates on lethal autonomous robots in warfare (2016, 2018) Key publications in property law theory and data sovereignty in agriculture (2018, 2023) Her recent work explores epistemological fragmentation in AI-driven legal systems (2024) and gender-equitable technology policy in East Africa (2023). While her articles span diverse subfields—from blockchain governance to vegetal rights in smart farming —they share a unifying theme of challenging technocratic assumptions through legal and philosophical lenses.
Amirreza Razmjoo Fard is a PhD student and Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) and the Robot Learning and Interaction (RLI) Group at the Idiap Research Institute. Supervised by Dr. Sylvain Calinon, he focuses on developing adaptive, efficient, and intelligent robotic control methods for contact-rich environments and constrained scenarios. Research Areas: Generative AI (diffusion models, flow matching), Model composition (product of experts), System dynamics, Control theory, Physics-based simulation (Isaac Sim) Key Goals: Bridging theory and real-world applications, enhancing robot autonomy, interaction, and physical intelligence His work spans publications at top robotics conferences like IROS, CoRL, RSS, and ICRA, with a Best Paper Finalist recognition at RSS 2024. Notable methods include CCDP for diffusion policy composition, CDF for differentiable robot geometry, and D-LGP for hybrid planning. He has also collaborated with Honda Research Institute Europe during a six-month internship.
Philipp Schütz is a Professor at the Lucerne School of Engineering and Architecture (HSLU), part of the Lucerne University of Applied Sciences and Arts. He holds dual appointments in the Institute of Mechanical Engineering and Energy Technology (IME) where he leads the CC Thermal Energy Storage research group, and the Institute of Natural and Social Sciences (ING). His office is located in Room E300/E311 at Technikumstrasse 21, 6048 Horw, Switzerland. Dr. Schütz earned his Physics degree from ETH Zürich with specialization in theoretical physics and optics. He completed his PhD in 2009 at the University of Zürich's Biochemical Institute, focusing on computer-aided modeling of spectroscopy experiments and pattern recognition in biochemical networks. From 2010-2014, he worked as a researcher at Empa in Dübendorf developing non-destructive testing methods before joining HSLU in September 2014 as a Physics lecturer. He completed a Certificate of Advanced Studies in Higher Education Didactics in 2015 and the 'Exzellenz in der Lehre' program in 2019. Professor Schütz's research spans Non-destructive Testing with emphasis on X-ray computed tomography , Energy System Modeling , and Computational Physics . His work on phase change materials and thermal energy storage has led to significant advancements in understanding calcium chloride hexahydrate solidification and salt hydrate behavior. He combines experimental work with sophisticated computational modeling, including Monte Carlo simulations and high-performance computing approaches. His expertise in algorithm development for large image datasets has applications across energy systems, materials science, and archaeological conservation. His publication record shows a clear evolution from fundamental physics toward applied engineering solutions, with recent work (2023-2025) increasingly focused on practical thermal energy storage applications for residential and district heating systems. The integration of X-ray computed tomography with energy system modeling represents his unique interdisciplinary approach. Professor Schütz actively leads numerous research initiatives including SWEET PATHFNDR, SWEET DeCarbCH TES, WindCoEconomy, and INTERSTORES. He teaches Mathematics & Physics for Engineering students and Time Series Analysis in the Master of Science in Applied Information and Data Science program. His research group operates advanced X-ray computed tomography facilities for studying material properties, energy storage systems, and conservation methods for archaeological materials, bridging theoretical physics with practical engineering applications in the energy sector.
Moreno Colombo is a postdoctoral researcher and doctoral assistant at the Department of Computer Science, University of Fribourg, affiliated with the Human-IST Institute. He holds a PhD in Phenotropic Interaction and is actively engaged in research and teaching within the Faculty of Mathematics, Natural Sciences and Medicine. His work bridges human-centered computing, smart cities, and sustainable technology design. His research interests focus on making human-technology interaction more natural and personalized. Key areas include Human-Computer Interaction (HCI), Human-Building Interaction, Smart Cities, Sustainability, Green Mobility, and the application of machine learning and fuzzy logic in perceptual computing. He specializes in Computing with Words and Phenotropic Interaction, aiming to reduce protocol dependency in interfaces. His recent publications (2020–2024) demonstrate a consistent focus on human-centered smart environments, including lighting systems, urban perception mapping, citizen engagement in smart cities, and semantic modeling for natural language understanding. These works reflect interdisciplinary collaboration and a strong commitment to user experience and environmental sustainability. PhD in Phenotropic Interaction, University of Fribourg He has supervised numerous Bachelor’s and Master’s theses on topics such as mobility visualization, smart city applications, and human-building interfaces. While no scientific awards are listed, his active publication record and involvement in international conferences indicate strong recognition in the research community. Moreno Colombo leads and contributes to projects involving crowdsourcing, machine learning, and fuzzy systems, often in collaboration with researchers across disciplines. His labs and research teams include the Human-IST Institute and collaborations within the Energy Informatics and Engineering departments.
Andreas Humm is a Researcher in the Department of Computer Science at the University of Fribourg, affiliated with the Faculty of Mathematics, Natural Sciences and Medicine. He is actively involved in research and can be reached at andreas.humm@unifr.ch or +41 26 300 9289. His office is located in PER 21, room B410, Bd de Pérolles 90, 1700 Fribourg, Switzerland. The research activities within the Department of Computer Science focus on modern computational challenges. Given his position, Andreas Humm's work likely spans areas such as artificial intelligence, data science, software engineering, and machine learning. These domains reflect the department's broader mission to advance both theoretical and applied computing. No publications or scientific awards are listed in the available information. There is no mention of student supervision, research grants, or leadership in specific labs or research teams. His current role emphasizes research contributions within the academic framework of the university.
Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at the Center for Artificial Intelligence and Data Science (CAIDAS), Julius-Maximilians-Universität Würzburg, where he holds the Chair of Computer Science XV. He has held prior academic positions at the University of Zürich, Bergische Universität Wuppertal, ETH Zürich, and KIT. He is actively involved in the academic community as founding co-chair of the Computational Social Science Section of the German Informatics Society (GI), associate editor of EPJ Data Science, and member of the European Lab for Learning and Intelligent Systems (ELLIS). He also serves as deputy spokesperson of CAIDAS. Research Interests: His research lies at the intersection of machine learning, network science, graph mining, and computational social science. He specializes in higher-order models of complex systems, temporal networks, and deep graph learning. His work aims to develop novel analytical frameworks for understanding dynamic, real-world systems through data-driven network modeling. The recent publications and preprints highlight a strong focus on advancing theoretical and practical aspects of temporal and graph-based machine learning. Key themes include the expressivity of temporal graph neural networks, the development of temporal graph isomorphism, and leveraging network science to improve deep learning on graphs. Scientific Awards: Junior-Fellowship from the German Informatics Society (2014) SNSF Professorship (CHF 1.5 million) from the Swiss National Science Foundation (2018) He leads the BMBF-funded research project COMFORT (Compression Methods for Robustness and Transferability), which received nearly €2 million in funding. His academic leadership includes organizing workshops, delivering invited talks (e.g., at NetSci, Dagstuhl, and university seminars), and mentoring through research supervision. His research group is engaged in foundational and applied work in AI and data science, with strong interdisciplinary links to physics and social sciences. He is involved in several research teams and labs, including the CAIDAS center and his own research group on machine learning for complex networks. His work is supported by national and international funding bodies, and he collaborates with researchers such as Prof. Petra Mutzel and Dr. Jonas Sauer.
Marcel Sébastien is a current researcher at the LIDIAP laboratory within the School of Engineering (STI) at EPFL. His work focuses on biometric systems, information forensics, and security applications. With over 200 scholarly works since 2000, he has contributed to journals like IEEE Transactions on Information Forensics and Security and conferences such as the International Conference on Biometrics. His research emphasizes secure biometric authentication, signal processing for fraud detection, and privacy-preserving technologies. Key areas of expertise include multi-modal biometric fusion, deep learning for template protection, and forensic analysis of biometric data. He has collaborated extensively with institutions like IEL, LTS5, and EDEE at EPFL. Despite no explicit mention of awards, his prolific publication record indicates significant contributions to the field. No formal student advisees or grants are listed in the provided text, though his involvement with LIDIAP suggests leadership in research teams focused on applied computer science and security.
Dr. Damien Barbier is a researcher specializing in theoretical physics, with a focus on statistical mechanics, condensed matter physics, and neural networks. His work bridges computational and theoretical approaches to understanding complex systems. Research Interests : Disordered systems, optimization algorithms, and quantum transport phenomena. Publications : Active in journals like SciPost Physics , with contributions to high-dimensional geometry and thermalization in harmonic models. Scientific Contributions : His recent articles explore anomalous transport in Anderson models, connected-solutions states in perceptrons, and generalized Gibbs ensembles. These works highlight interdisciplinary methods combining physics and machine learning.
Andreas Kunz is a Lecturer at the Department of Mechanical and Process Engineering, ETH Zurich, where he leads the Innovation Center for Virtual Reality (ICVR). His research focuses on developing user-oriented virtual and mixed reality systems tailored for industrial applications across product development processes and digital factories. Current Affiliation: ETH Zurich, Institute for Machine Tools Research Themes: Virtual Reality, Mixed Reality, Human-Computer Interaction, Digital Twin Systems Since joining ETH Zurich in 1994 and completing his habilitation in 2004 on "Interaction with the digital product model in virtual space," Kunz has pioneered VR systems for visualization, collaboration, and haptic interfaces in industrial contexts. His work combines academic research with practical implementation through industry collaborations. Recent research trends show increasing focus on real-world VR applications including: Multiuser redirected walking algorithms Attention guidance systems Industrial MTM motion transcription Smartphone-MR device integration Eye tracking for cognitive analysis Haptic interfaces for control panels The ICVR group under Kunz's leadership maintains strong industry partnerships while producing significant publications in IEEE VR, ISMAR, and ACM VRST conferences. Their work bridges theoretical VR research with practical implementations in manufacturing, assembly, and safety-critical environments.
Öykü Işık is a Professor at IMD Business School, where she leads research at the intersection of technology, business, and societal impact. Her work addresses critical challenges in digital transformation with direct implications for industrial sectors and human well-being. Her research spans three core domains: Digital Health and Mental Health, focusing on eliminating bias through improved healthcare data representation Circular Economy systems, particularly security vulnerabilities in second-hand electronic device ecosystems Ethical AI governance, advocating for technical experts' inclusion in ethics policymaking Publication trends reveal a strategic progression from foundational AI ethics (2023) to circular economy applications (2024) and digital health innovation (2025). Cross-cutting themes include data sovereignty, algorithmic accountability, and human-centered technology design, demonstrating her commitment to responsible innovation that prioritizes equity and sustainability.
