Maksym Andriushchenko is an incoming faculty member at the ELLIS Institute Tübingen and Max Planck Institute for Intelligent Systems , where he will lead the AI Safety and Alignment group as a principal investigator. Currently, he is a Doctoral Assistant at the Theory of Machine Learning Laboratory (TML) within the Department of Computer Science (IINFCOM) at Swiss Federal Institute of Technology in Lausanne (EPFL) . His work focuses on AI safety, adversarial robustness, and alignment of large language models (LLMs) with societal values.
Gustavo Alonso is Full Professor at the Department of Computer Science (D-INFK) of ETH Zurich and Head of the Institute for Computing Platforms . He has been at ETH since 1995, first as a post-doc, then as Assistant Professor from April 1998, and promoted to Full Professor in October 2001. Within the Systems Group he leads the Information and Communication Systems Research Group . Education: 1989 – Telecommunications Engineering (undergraduate), Madrid Technical University (UPM-ETSIT), Spain 1992 – M.S. Computer Science, University of California, Santa Barbara (UCSB) 1994 – Ph.D. Computer Science, University of California, Santa Barbara (UCSB) Research Interests: His work spans databases, distributed systems, cloud-computing architecture, FPGAs, hardware acceleration for data science, parallel and reconfigurable computing . The group investigates how modern heterogeneous hardware—from GPUs to SmartNICs—can be integrated into data-processing systems to achieve orders-of-magnitude performance gains, energy savings, and new functionality such as in-network computation and serverless acceleration. Scientific Awards & Honors: Fellow of the ACM (Association for Computing Machinery) Fellow of the IEEE (Institute of Electrical and Electronics Engineers) Distinguished Alumnus, Department of Computer Science, UC Santa Barbara Four Test-of-Time / Most Influential Paper Awards across databases, programming languages, cloud computing, and software engineering Labs & Projects: He directs the Information and Communication Systems Research Group within the Systems Group ( systems.ethz.ch ). The lab develops open-source platforms such as Coyote v2 for FPGA abstractions, Shuhai for HBM benchmarking, and MicroRec for micro-second recommendation serving, while collaborating with industry on SmartNICs, serverless analytics, and cloud-scale data analytics.
Professor Martin Schrimpf is a Tenure Track Assistant Professor at EPFL, holding dual appointments in the School of Life Sciences (SV) and the School of Computer and Communication Sciences (IC). His research bridges computational neuroscience, deep learning, and cognitive science to model human natural intelligence in vision and language. He leads the NeuroAI Lab, focusing on aligning artificial neural networks with brain mechanisms and human behavior. Education: PhD in Brain and Cognitive Sciences from MIT (2017–2022), MSc in Software Engineering from TUM/LMU/UNA (2014–2017), and BSc in Information Systems from TUM (2011–2014). His work has been recognized with awards including the Neuro-Irv Open Science Prize, McGovern Fellowship, and Takeda AI+Health Fellowship. He co-founded Integreat, a social impact startup recognized with Google.org’s Impact Challenge and TUM’s Social Impact Award. Research interests include neuroAI, brain-like models, and clinical translation (e.g., visual prosthetics). He has published in top venues like Neuron, Nature Human Behavior, NeurIPS, and ICLR. Current projects involve developing topographic language models (TopoLM) and investigating causal language network interactions using LLMs. Teaching: Courses include Neuroscience Foundations for Engineers and Brain-like Computation and Intelligence . Supervised over 26 students, including PhD candidates Badr Alkhamissi, Ben Lönnqvist, and Yingtian Tang. Active in grants from SNSF, NeuroX, and EPFL’s AI Center. Labs/Teams: NeuroAI Lab at EPFL Neuro-X Institute. Future directions include advancing brain-inspired models for clinical applications and expanding interdisciplinary collaborations between neuroscience and AI.
Sebastian Raisch is a Full Professor of Strategic Management at the Geneva School of Economics and Management (GSEM), University of Geneva, where he also serves as Director of the Executive MBA program. He is affiliated with the Institute of Management and holds a Ph.D. from the University of Geneva. His academic work is deeply rooted in understanding how organizations balance stability and change, particularly in the face of digital transformation. Research Interests: His primary areas of expertise include strategic management, organizational ambidexterity, organizational paradox, artificial intelligence in organizations, and digital transformation. His research investigates the dynamics of corporate growth and decline, strategic renewal, and the integration of human and artificial intelligence in organizational decision-making. He is especially interested in how firms navigate tensions between competing demands such as exploration and exploitation, automation and augmentation, and stability and innovation. Publications and Trends: His recent publications (2023–2025) show a strong shift toward the role of artificial intelligence in management, with a focus on hybrid human-AI systems, ethical AI, and the strategic implications of AI adoption. Earlier works center on ambidexterity, paradox theory, and corporate turnaround, establishing him as a leading scholar in these domains. Scientific Awards: Strategic Management Society’s Best PhD Paper Award Emerald Publishing Group’s Citation of Excellence Award Journal of Management’s Scholarly Impact Award Editorial and Professional Service: Sebastian Raisch serves as Associate Editor at the Academy of Management Review and sits on the editorial boards of the Academy of Management Journal and the Strategic Management Journal . He has advised numerous doctoral students and supervised research projects on organizational innovation and digital strategy, though specific names are not listed in the provided materials. He has not received explicit mention of research grants, but his extensive publication record suggests active research funding. Labs and Research Teams: While no formal lab is mentioned, Raisch leads or contributes to research initiatives within the Institute of Management at GSEM, particularly in strategic management and digital innovation. He collaborates with scholars across Europe and the U.S., including notable co-authors like J. Schad, S. Krakowski, and M. Tushman.
Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.
Prof. Dr. Otmar Hilliges is a Full Professor at the Department of Computer Science at ETH Zurich. He leads the AIT lab and serves as the head of the Institute of Intelligent Interactive Systems. His research focuses on spatio-temporal understanding of human movement and interaction, leveraging algorithms and representations from videos, images, and sensor data for applications in Augmented Reality (AR), Virtual Reality (VR), and Human-Robot Interaction. Education: Diplom (MSc) in Computer Science, Technical University of Munich (TUM), Germany PhD in Computer Science, Ludwig Maximilian University of Munich (LMU), Germany (2009) Research Interests: Hilliges' work spans computer vision, robotics, and human-computer interaction. He develops methods for 3D human pose estimation, generative models for realistic avatar creation, and physically plausible simulation of human-object interactions. His research emphasizes practical applications in AR/VR and assistive robotics, aiming to bridge the gap between perception and action. Grants & Contributions: ERC Consolidator Grant (2022-2027): 'AI-Perceive: Robust Human-Centric Computer Vision for Advanced AI-Agents' Google Research Agreement (2020-2025): 'Generative Modelling of Humans' Microsoft Research Grants: Focus on human-centric robotics and interactive technologies Labs & Teams: Leads the AIT Lab at ETH Zurich, which pioneers research in intelligent interactive systems, emphasizing human-centric AI and robotics. The lab collaborates on projects ranging from drone cinematography to haptic feedback systems.
Marc Pollefeys is a Full Professor at the Department of Computer Science, ETH Zurich, and Director of the Microsoft Mixed Reality and AI Lab. His work focuses on advanced perception systems for HoloLens, 3D computer vision, robotics, and machine learning. Key contributions include automated 3D modeling from video, real-time reconstruction pipelines, and vision-based autonomous systems. Education: PhD from KU Leuven (1999) Previous Affiliation: Professor at UNC Chapel Hill Research interests span 3D reconstruction , computer vision , robotics , SLAM , augmented reality , and privacy-preserving mapping . His work often integrates geometric modeling , feature matching , and deep learning . Recent projects emphasize implicit 3D representations , open-vocabulary scene understanding , and robust estimation using neural-guided algorithms. Recent publications highlight advancements in neural implicit fields , line-based correspondence , and vision-language integration . Trends include hybrid point-line methods, differentiable RANSAC, and privacy-aware localization frameworks. Scientific recognition includes: IEEE Fellow (2012) David Marr Prize (ICCV 1998) DAGM Best Paper Award (1999) Advisees include current and alumni PhD students such as Yagız Aksoy, Federico Camposeco, and Sudipta Sinha. Collaborations span institutions like UNC Chapel Hill, ETH Zurich, and Microsoft Zurich. Research sponsors include Microsoft, Google, and European research initiatives.
Stefan Bechtold is a Full Professor of Intellectual Property at ETH Zurich since 2013 and Associate Vice President IP Policy (2023–2026). He is affiliated with the ETH AI Center and serves on global advisory boards including the Academic Advisory Board of the German Federal Ministry for Economic Affairs and Climate Action. His interdisciplinary research bridges law, economics, and computer science.
Elliott Ash is an Associate Professor of Law, Economics, and Data Science at ETH Zurich's Center for Law & Economics. He holds a Ph.D. in Economics and J.D. from Columbia University, a B.A. in Economics, Government, and Philosophy from the University of Texas at Austin, and an LL.M. in International Criminal Law from the University of Amsterdam. His research focuses on empirical legal studies using econometrics, NLP, and ML, examining topics like judicial behavior, legislative impact, and AI-driven governance. He has been funded by the ERC, Swiss NSF, and others. Research Interests: Elliott explores automation of legal decisions, text-as-data analysis in law, and the intersection of AI with legal systems. He develops tools like BallotBot and LePaRD to enhance legal transparency and public understanding. His work bridges law, economics, and computer science, with publications in top journals like the American Economic Journal and Review of Economics and Statistics . Teaching: Courses include Building a Robot Judge , Natural Language Processing for Law , and Big Data for Public Policy . He co-organizes the Zurich Workshop in AI+Economics and Monash-Warwick-Zurich Text-as-Data Workshops. Awards: European Research Council Starting Grant, Swiss National Science Foundation Grant, and multiple grants from U.S. and Swiss institutions. His work has been featured in NPR , VoxEU , and Georgetown Law Journal . Labs/Teams: Leads the Swiss AI Initiative's Human-AI Alignment team, collaborates with the CEPR on Political Economy research, and serves as an Economic Journal Associate Editor.
Olga Fink is a Tenure Track Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Department of Intelligent Maintenance and Operations Systems (IMOS) within the School of Architecture, Civil and Environmental Engineering (ENAC). She also holds roles in PhD program committees for Civil and Environmental Engineering (EDCE) and Robotics, Control, and Intelligent Systems (EDRS). Her research focuses on machine learning for infrastructure monitoring, predictive maintenance, and physics-informed AI models. She teaches courses on machine learning, data science for infrastructure, and advanced deep learning topics. Fink advises multiple PhD students and is involved in interdisciplinary projects such as ThermoNeRF (multimodal 3D thermal modeling) and physics-informed neural networks for fault diagnostics. Her work bridges AI and engineering with applications in smart infrastructure, energy systems, and industrial IoT. Education: PhD in Engineering (inferred from role) Affiliations: IMOS Lab, ENAC-SGC, EPFL PhD Committees (EDCE, EDRS) Key Research Themes: Explainable AI, Digital Twins, Structural Health Monitoring, Domain Adaptation Her publications (2023–2025) emphasize robust AI for industrial systems, including fault detection in high-voltage equipment, multimodal data fusion, and physics-consistent models. She collaborates on EU and industry-funded projects, focusing on real-world applications like predictive maintenance and energy efficiency.
Devis Tuia serves as Associate Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), holding appointments in the Institute of Environmental Engineering (IIE) within the School of Architecture, Civil and Environmental Engineering (ENAC). He leads the Environmental Computational Science and Earth Observation Laboratory (ECEO) since 2020 and contributes to EPFL's Doctoral Program in Civil and Environmental Engineering. His academic journey began in Lausanne with studies at UNIL and EPFL, culminating in a PhD in remote sensing from UNIL. Postdoctoral research followed at institutions in Valencia, Boulder, and EPFL, focusing on machine learning model adaptation. He progressed from Research Assistant Professor at University of Zurich to Associate and Full Professor at Wageningen University before joining EPFL. Tuia's research bridges Earth observation with artificial intelligence, specializing in interpretable deep learning for environmental applications. His lab develops algorithms for making remote sensing accessible, with particular emphasis on digital wildlife conservation through automated censuses using drone and satellite imagery. Current projects tackle the 'black box' problem in environmental modeling while advancing spatial intelligence for sustainable urban development. His 2023-2025 publication portfolio reveals three dominant trends: (1) species distribution modeling using incomplete observations, (2) multimodal fusion of satellite/drone data with textual descriptions, and (3) interpretable AI frameworks for environmental decision-making. This work consistently addresses real-world challenges like wildfire forecasting and biodiversity monitoring. As an educator, Tuia supervises 12 current PhD students and has graduated 4 former EPFL doctoral candidates. His teaching portfolio includes Frontiers of Deep Learning for Engineers , Sensing and Spatial Modeling for Earth Observation , and Image Processing for Earth Observation courses. The ECEO laboratory maintains active collaborations with ESA-NASA initiatives and conservation organizations globally.
Andrea Cavallaro is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) and Director of the Idiap Research Institute. He holds dual appointments in the School of Engineering (STI) within the Institute of Electrical Engineering and Measurements (IEM) and the School of Engineering's Education Unit (SEL-ENS). His research focuses on machine learning for multimodal perception, privacy-preserving AI, and autonomous systems. Cavallaro earned his PhD in Electrical Engineering from EPFL in 2002 and has held leadership roles including Director of Research at Queen Mary University of London and Turing Fellow at The Alan Turing Institute. Education: PhD in Electrical Engineering (EPFL, 2002) Leadership: Idiap Director, Affiliate at ELLIS Society Editorial Roles: Editor-in-Chief of Signal Processing: Image Communication (2020–2023), Senior Area Editor for IEEE Transactions on Image Processing Research Interests: Machine learning for audio-visual sensing, privacy in AI, autonomous systems perception, and ethical AI frameworks. Key projects include AlignAI (trustworthy AI alignment) and CORSMAL (multimodal object manipulation). Recent articles explore privacy-aware AI models, adversarial attacks, and multimodal perception systems. His work bridges theoretical advancements with practical applications in robotics, healthcare, and education. Awards include the Royal Academy of Engineering Teaching Prize and IAPR Fellowship. Teaching: Leads courses on deep learning ethics and multimodal AI at EPFL. Advising: Supervises 11 PhD students in areas like privacy-preserving algorithms and autonomous systems. Labs/Teams: Coordinates Idiap’s Audiovisual Intelligence and Learning Lab (LIDIAP) and collaborates on projects like GraphNEx (explainable AI via graph neural networks).
Mennatallah El-Assady serves as Assistant Professor at ETH Zurich's Department of Computer Science, where she leads the Interactive Visualization and Intelligence Augmentation Lab (IVIA). Her academic trajectory includes research fellowships at ETH's AI Center and doctoral work at University of Konstanz and OntarioTech University, establishing her expertise at the intersection of visualization and artificial intelligence. Her research focuses on advancing responsible data-driven decision-making through human-centered analytics, with particular emphasis on explainable machine learning systems. Dr. El-Assady combines data mining techniques with visual interfaces to create transparent AI workflows, specializing in text data analysis. Her work bridges computational linguistics, digital humanities, and information visualization to develop tools that make complex AI processes interpretable for end users. Recent publications demonstrate growing focus on generative AI's impact on visualization practices and sophisticated frameworks for interactive machine learning. Her research consistently addresses the challenge of maintaining human agency in increasingly automated systems, with applications spanning political debate analysis, musicology, and healthcare data interpretation. The trend shows increasing sophistication in evaluation methodologies for visual analytics systems. Best Paper Award: Honorable Mention for 'Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework' Dr. El-Assady actively shapes her field through workshop leadership including ArgVis (Argument Visualization), Vis4DH (Visualization for Digital Humanities), and VISxAI (Visualization for AI Explainability). Her teaching includes 'Interactive Machine Learning- Visualization and Explainability' at ETH Zurich, training next-generation researchers in human-centered AI development. She maintains strong industry connections with coverage in Forbes and ETH News regarding human-AI collaboration frameworks. The Interactive Visualization and Intelligence Augmentation Lab (IVIA) develops cutting-edge tools including explAIner for transparent machine learning, LingVis for linguistic analysis, VisArgue for debate structure visualization, and VALIDA for political deliberation analysis. These projects share a common thread of enhancing human understanding through carefully designed visual interfaces that expose AI decision processes.
Gerold Schneider is an Associate Professor at the University of Zurich , affiliated with the Department of Computational Linguistics under the Faculty of Arts and Social Sciences and Faculty of Business, Economics and Informatics . He leads the Text Crunching Center (TCC) , focusing on interdisciplinary research at the intersection of NLP, Digital Humanities, and Health Data Science. Research Interests His work spans Text Analytics , Digital Humanities , Corpus Linguistics , and Health Data Science , with applications in: Biomedical NLP (e.g., Alzheimer’s detection, clinical trials) Digital Humanities projects (e.g., analyzing Charles Dickens, UN archives) Migration discourse framing across languages Adversarial data collection for hate speech detection Interdisciplinary methodologies for digital unstructured data Recent Publications 2025–2024 research highlights include annotated corpora for preclinical and neurological studies, AI-driven analysis of historical linguistic variation, and innovative tools for language learners. His NLP applications address health diagnostics, ethical AI, and cross-lingual political discourse. Labs & Teams As TCC leader, he spearheads collaborative projects within the Digital Society Initiative (DSI) communities (AI & Law, Health, Ethics, etc.), integrating computational methods with humanities and health research.
Elaine M. Huang is an Associate Professor of Human-Computer Interaction at the Department of Informatics, University of Zurich, where she has served since 2010. She also leads the People and Computing Lab, focusing on the dynamic interplay between human practices and technological advancements. Her academic background includes: PhD in Computer Science, Georgia Institute of Technology (2006) Dr. Huang's research centers on human-computer interaction, examining the bidirectional relationship between technology and human practices. She is particularly known for her work on gender equality in technology, challenging assumptions about innate gender differences and investigating how AI systems may perpetuate biases. Her research also spans sustainable interaction design, mental health technologies, and chronic disease management, always emphasizing empirical data over intuition. Analysis of her recent publications (2023-2025) reveals a strong trajectory toward socially impactful HCI, with significant focus on health technologies (diabetes management, mental health), cultural sensitivity in design, and the ethical challenges of AI. Her methodology often involves field studies to understand real-world technology use, countering the industry's reliance on intuition. As head of the People and Computing Lab, Dr. Huang oversees a research group dedicated to designing and evaluating technologies that address complex human needs. The lab's work frequently involves co-design with diverse user communities to ensure relevance and inclusivity in technological solutions.
