Thijs Defraeye is a Senior Scientist at Empa (Swiss Federal Laboratories for Materials Science and Technology) and Adjunct Professor at Dalhousie University. He holds a PhD in Building Physics from KU Leuven (2011) and a Master's in Civil Engineering (2006). His work focuses on optimizing food supply chains through multiphysics simulations and digital twins, addressing challenges in refrigerated transport, postharvest quality preservation, and energy-efficient food processing. He leads the SimBioSys group, developing solutions for perishable goods logistics and electrohydrodynamic technologies. Research interests include: Biophysics of food systems Digital twin applications in agriculture Electrohydrodynamic drying Thermal management in cold chains Sustainable food technologies Recent work emphasizes reducing food loss through physics-based modeling of refrigerated containers, ventilated packaging optimization, and scalable evaporative cooling systems. His studies bridge engineering principles with biological processes, aiming to enhance global food security and environmental sustainability.
Jean-Louis Scartezzini is an Honorary Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Solar Energy and Building Physics Laboratory (LESO-PB). His research focuses on natural/artificial lighting, solar energy systems, and building technology, with a strong emphasis on energy efficiency and sustainability. Director of LESO-PB since 1994 Founded and led several institutes, including the Institute for Infrastructure, Resources, and Environment (2002–2009) Doctorat in Physics from EPFL (1986) Extensive international collaborations, including visiting roles at NUS (2009) and LBNL/UCLA (1988) Research interests include: - Daylighting and lighting control systems - Passive/active solar technologies - Urban microclimate and energy systems - Stochastic simulation and predictive control Recent work addresses climate change impacts on energy systems, urban sustainability, and machine learning applications in energy optimization. Key publications span lighting health impacts, renewable integration, and microclimate modeling Awards include the European Solar Prize (2001/2002) and Walsh-Weston Bronze Medal (1998) Mentored over 20 PhD students, many leading in academia and industry (e.g., Marilyne Andersen at EPFL, Flavio Foradini at E4Tech).
Georgia Fragkouli is a Researcher affiliated with ETH Zürich's School of Computer and Communication Sciences, working within the Institute of Computer Engineering and Communication Systems. Her role is part of the Professorship for Networked Systems, focusing on advanced networking and distributed systems research. She specializes in analyzing network performance, security, and transparency, with a particular emphasis on BGP convergence dynamics, anomaly detection, and decentralized computing architectures. Her research interests include network protocol validation, machine learning-based traffic analysis, and improving internet transparency through innovative measurement frameworks. She has contributed to projects like MorphIT for packet-level transparency and explored failure mitigation in globally distributed systems. Notable recent work includes studies on transient forwarding anomalies, iBGP convergence effects, and data-plane performance consistency. Her publications span both theoretical advancements and practical implementations, aiming to bridge gaps between networking theory and real-world deployment challenges.
Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.
Dr. Paolo Bergamo is a Senior Researcher at the Swiss Seismological Service (SED), ETH Zurich, since April 2016. He specializes in engineering seismology, focusing on earthquake site response models, ground-motion modeling, and seismic risk assessment. His work includes projects such as the Earthquake Risk Model Switzerland (ERM-CH23) and the SERA Horizon2020 initiative. He holds a PhD in Earth Sciences from Politecnico di Torino (2012) and advanced degrees in Environmental Engineering. Key research areas include soil amplification analysis, geophysical surveys, and the integration of empirical and computational methods for seismic hazard mitigation. His contributions span microzonation studies, site characterization using borehole and ambient vibration data, and the development of design-compatible waveforms for Swiss building codes. Education: PhD in Water and Territory Management Engineering, Politecnico di Torino (2012) MSc and BSc in Environmental Engineering, Politecnico di Torino (2008, 2005) Research Interests: Dr. Bergamo’s work emphasizes the collation of empirical ground-motion data with building codes, spatial modeling of soil amplification, and geophysical site characterization. He employs advanced techniques like surface-wave analysis, machine learning, and canonical correlation for seismic hazard assessment. Projects & Grants: ERM-CH23: Site response implementation and national seismic risk modeling SERA Project (Horizon2020): Site characterization indicators Swiss Federal Office for Environment-funded studies on microzonation and geophysical monitoring Labs & Teams: Active contributor to the Engineering Seismology group at SED, leading efforts in alpine valley seismic modeling and offshore site characterization in Lake Lucerne.
Dr. Alexander Breuss is part of the Sensory-Motor Systems Professorship at ETH Zürich, focusing on developing innovative robotic and sensor technologies for medical applications, particularly in sleep disorder treatment and home healthcare. His work integrates biomedical engineering, robotics, and machine learning to address challenges in sleep medicine and cardiovascular diagnostics. Key projects include the Somnomat Care robotic bed for vestibular stimulation and the Somnomat Casa system for nocturnal interventions. His research spans sensorized devices for sleep monitoring, clinical trials for rhythmic movement disorders, and cardiovascular disease prognosis using imaging and hemodynamic analysis. Dr. Breuss collaborates on interdisciplinary projects, combining engineering and clinical insights to advance healthcare technologies. His research interests include the design of medical devices for home environments, non-invasive monitoring systems, and closed-loop robotic systems for therapeutic applications. Notable contributions include lightweight wearable sensors for movement disorders and automated sleep position classification using neural networks. He has published extensively on topics such as pleural effusion in aortic stenosis and ECG-based cardiac prognosis, highlighting his cross-disciplinary approach to biomedical challenges. No scientific awards are explicitly mentioned for Dr. Breuss. His work is centered at the Sensory-Motor Systems Lab, where he contributes to advancing technologies that improve patient care and sleep quality through robotics and sensor innovation.
Dr. Christian Russ is a Senior Lecturer at the Institute of Business Information Technology , Zurich University of Applied Sciences (ZHAW), with expertise in digital transformation, IT leadership, and business-IT alignment. His work spans healthcare, education, and automotive sectors, focusing on agile IT governance and adaptive business models. Current roles: Program Director MAS IT-Leadership and TechManagement Key projects: ZHAW Digital Culture Assessment, Monitoring eCH Standards Research interests include digital transformation , agile IT , health informatics , and emerging technologies . Recent publications analyze post-pandemic organizational culture, medical AI regulation, and cloud infrastructure in healthcare. 2025: Organizational culture shifts during/after pandemic 2024: ML solutions in Swiss hospitals under MDR 2023: iPaaS cloud security in healthcare 2023: Agile transformation in non-profits Scientific recognitions: Best Paper Award at SMART 2018 "Educate to lead" award (Soroptimist International Europa, 2016) Active in the ZHAW Digital Health Lab, he leads projects on tech startup coaching, digital ecosystems, and IT governance. His teaching covers IT strategy, digital transformation, and enterprise service management at ZHAW.
Prof. Dan Olteanu is a full professor at the Department of Informatics, University of Zurich, leading the Data Systems and Theory (DaST) group. He holds visiting professorships at the University of Oxford and is an emeritus fellow of St Cross College. His academic journey includes a PhD from Ludwig Maximilian University (2005), postdoctoral roles at Saarland University and Cornell University, and prior faculty positions at Oxford (2007–2020). He has also worked in industry with companies like LogicBlox and RelationalAI, focusing on database systems and AI. Education: Bachelor’s in Computer Science, Politehnica University of Bucharest (2000) PhD in Computer Science, Ludwig Maximilian University (2005) Professional Roles: Full Professor, University of Zurich (since 2020) Visiting Professor, University of Oxford Emeritus Fellow, St Cross College Editorial Roles: ACM TODS, VLDBJ, SIGMOD Conference Chair: ICDT Council (since 2022) His research focuses on data systems theory, including query optimization, probabilistic databases, factorized databases, and in-database machine learning. He co-authored the seminal book Probabilistic Databases (2011) and has pioneered algorithms for efficient machine learning over relational data and incremental maintenance of analytical workloads. His work emphasizes scalable, theoretically grounded solutions for real-world data challenges. Awards: ICDT 2019 Best Paper Award ACM SIGMOD 2018 Distinguished PC Member Award ERC Consolidator Grant (2016) Oxford Outstanding Teaching Award (2009) Grants & Funding: Supported by Google, Microsoft Azure, Amazon AWS, EPSRC, and the European Commission. His research bridges academia and industry, with contributions to commercial systems like LogicBlox and RelationalAI. Labs & Teams: Heads the DaST group at Zurich, focusing on data systems theory and applications. Collaborates widely in the database and AI communities.
Thomas Renault is a researcher at the Centre d'Economie de la Sorbonne (CES), affiliated with Université Paris I Panthéon-Sorbonne in Paris, France. His scholarly work focuses on financial markets, monetary policy, and the intersection of social media with finance. With multiple publications in high-impact journals like the Journal of Finance, he has established himself as an active contributor to economic research. Dr. Renault's research interests span financial economics, behavioral finance, monetary policy, market microstructure, cryptocurrency markets, and textual analysis in finance. His work often employs innovative methodologies to analyze how information flows through financial markets, particularly examining the impact of social media and central bank communications on market behavior. He has developed novel measures for analyzing ECB communications and investigating investor attention patterns at intraday frequencies. His publication record demonstrates a consistent focus on market efficiency, information dissemination, and the behavioral aspects of financial decision-making. Recent work examines nonstandard errors in financial research, the dynamics of cryptocurrency markets, and methods to combat misinformation on social media platforms. His research frequently involves large-scale data analysis of social media content and high-frequency financial data. Dr. Renault has collaborated with researchers across numerous European institutions, indicating strong international connections within the academic finance community. His work has appeared in top finance journals and working paper series from prestigious institutions including the Bank of England and the Federal Reserve.
Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.
Dr. Alexander Artikis is an Associate Professor of Artificial Intelligence at the University of Piraeus and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos". He leads the Complex Event Recognition (CER) group , focusing on symbolic and probabilistic approaches to event recognition and forecasting. University of Piraeus (2025–present) NCSR Demokritos (2017–present) Complex Event Recognition Group (2017–present) His research spans Artificial Intelligence and Distributed Systems , with a focus on: Complex Event Recognition (CER) : Developing logic-based systems for detecting events in real-time data streams Event Calculus : Creating probabilistic and incremental versions for runtime reasoning Multi-Agent Systems : Modeling norm-governed interactions Maritime Informatics : Applying CER to vessel trajectory analysis and fleet management Key publications reveal trends in: Neuro-symbolic forecasting models combining deep learning and logic-based reasoning Symbolic automata with memory for pattern detection Online learning techniques for dynamic event rule generation Tensor-based formalizations for efficient temporal reasoning Handling uncertainty in real-time maritime data streams Optimizing memory usage for scalable stream processing He contributes to open-source tools like RTEC (Run-Time Event Calculus) and holds a European patent on complex event forecasting. His work addresses challenges in: Proactive decision-making systems Knowledge Graph consistency Hybrid human-machine discovery of movement patterns Big Data analytics for time-critical applications
Jakub Macina is a Doctoral Fellow at the ETH AI Center and a PhD Candidate at ETH Zürich . He works in the intersection of Natural Language Processing and Learning Sciences as part of the Language, Reasoning and Education Lab (led by Prof. Mrinmaya Sachan) and the Professorship for Learning Sciences and Higher Education (led by Prof. Manu Kapur). Forbes 30 Under 30 in Science & Education 2023 Recipient of ETH AI Center Fellowship ( Co-founder of a health-tech startup with seed investment Research Interests : Focus on generative large language models (LLMs), dialogue tutoring systems , pedagogical alignment of AI models, and mathematical reasoning . His work explores: Reinforcement learning for pedagogical steering LLM evaluation frameworks Socratic question generation Stepwise error detection and remediation Student-teacher interaction modeling Publications span top conferences like EMNLP , ACL , NeurIPS , and RecSys , with particular emphasis on educational applications of LLMs and dialogue-based learning systems . Scientific Awards : Forbes 30 Under 30 in Science and Education (2023) 2nd Place in ACM IT SPY Computer Science Master's Thesis Competition (2017) ETH AI Center Fellowship (2021) Leadership & Teaching includes: Managing team of 6 data scientists Teaching Assistant for Machine Learning and NLP courses at ETH Zurich Developing large-scale ML pipelines for recommender systems Open-source contributions to Discourse and Google Summer of Code projects
Prof. Donat Fäh is a faculty member at ETH Zurich's Institute of Geophysics, part of the Swiss Seismological Service (SED). His work focuses on advancing seismic hazard assessment and site response modeling in Switzerland and beyond. He leads interdisciplinary projects integrating geophysical surveys, machine learning, and empirical data to refine risk models for urban areas like Basel and Lucerne. Key contributions include developing the ERM-CH23 national earthquake risk framework and improving methodologies for nonlinear soil behavior analysis using KiK-net data from Japan. His research emphasizes high-resolution amplification mapping, subsurface characterization via ambient vibrations, and understanding glacial and subaqueous slope dynamics. Research interests span seismic site effects, soil mechanics, landslide stability monitoring, and the application of advanced geophysical inversion techniques. He collaborates internationally to enhance earthquake risk communication and building code compliance, particularly in low seismicity regions. Current efforts include refining 3D geophysical models for urban settings and exploring Bayesian methods for subsurface structure identification. His work bridges fundamental geophysical research with practical engineering solutions for infrastructure resilience. Advising and grants: No formal advisees or grant details are explicitly listed in the provided texts. His collaborative projects, however, suggest involvement in large-scale initiatives such as URBASIS and the Swiss strong-motion network modernization. Labs and teams: Prof. Fäh is affiliated with the Swiss Seismological Service (SED) and actively contributes to ETH Zurich’s seismic monitoring infrastructure. His team collaborates with institutions in Japan (KiK-net network) and applies cutting-edge geophysical techniques to study subglacial environments and lakebed geotechnics.
Lisa Larrimore Ouellette is the Deane F. Johnson Professor of Law at Stanford Law School, where she has established herself as a leading scholar in intellectual property law and innovation policy. Her work spans patent law, trademark law, pharmaceutical policy, and the intersection of artificial intelligence with legal frameworks. Professor Ouellette's research focuses on the intersection of law, economics, and innovation. Her scholarship examines how intellectual property systems influence technological development, with particular attention to pharmaceutical innovation, biomedical research, and emerging technologies. She has made significant contributions to understanding patent systems, trademark law, and the policy frameworks governing innovation. Her work often employs empirical methods to analyze real-world impacts of legal rules on innovation incentives and outcomes. Analysis of her recent publications reveals a strong focus on contemporary challenges in intellectual property law, including the impact of artificial intelligence on patent systems, equity in patent inventorship, pharmaceutical pricing mechanisms, and innovation policy responses to public health emergencies like the COVID-19 pandemic. Her work demonstrates a consistent pattern of addressing timely policy questions with rigorous empirical analysis and thoughtful legal reasoning. Professor Ouellette has collaborated extensively with leading scholars in law and economics, including Daniel J. Hemel, Jonathan Masur, Mark Lemley, and others. Her research has been supported by prestigious institutions including the National Bureau of Economic Research (NBER), and she has contributed to numerous policy discussions through amicus briefs and responses to government requests for comments. While specific advising relationships aren't detailed in the available information, her extensive publication record with co-authors suggests active mentorship of junior scholars and students.
Tarun Ramadorai is Professor of Financial Economics at Imperial College London, with a distinguished career spanning household finance, financial economics, behavioral economics, real estate, and international finance. He serves as Executive Editor of the Review of Financial Studies and holds prestigious fellowships including Research Fellow of the Centre for Economic Policy Research (CEPR), Senior Academic Fellow of the Asian Bureau of Finance and Economics Research (ABFER), and Nonresident Senior Fellow at the National Council of Applied Economic Research (NCAER). Education BA in Mathematics and Economics from Williams College MPhil in Economics from the University of Cambridge PhD in Business Economics from Harvard University Research Interests Professor Ramadorai's research spans household finance, financial economics, behavioral economics, real estate, and international finance. His work examines how households make financial decisions across different markets and countries, with particular focus on housing markets, investment behavior, and financial inclusion. He has established himself as a leading expert in international comparative household finance, having previously served as Principal Investigator on a transformational initiative financed by the Sloan Foundation to establish this sub-field of finance and economics. His recent research explores the intersection of technology and personal finance, housing market dynamics, and optimal tax policy. He has demonstrated how housing costs impact fertility decisions, how machine learning affects credit markets, and how privacy policies influence consumer data extraction. His work combines rigorous theoretical frameworks with innovative empirical approaches using large-scale datasets from diverse markets. Publication Trends Professor Ramadorai's recent publications reveal a strong focus on household decision-making in financial markets, with increasing attention to the digital transformation of finance. His work bridges theoretical insights with practical policy implications, particularly in emerging economies. There is a clear trend toward interdisciplinary research that combines finance, economics, and data science to address pressing questions about financial inclusion, housing affordability, and the impact of technology on traditional financial services. Scientific Awards Brattle prize for best paper in the Journal of Finance Jensen prize for the best paper in the Journal of Financial Economics Wharton School-WRDS Best Paper Award in Empirical Finance James A Lebenthal Excellence in Municipal Finance Research Prize FMA Napa Conference Best Paper Prize INQUIRE Europe third prize Viz Risk Management Best paper prize Policy Engagement and Advisory Roles Professor Ramadorai has made significant contributions to policy discussions worldwide. He served as Chairman of the Inter-Regulatory Committee on Household Finance constituted by the Reserve Bank of India, which produced the influential "Indian Household Finance" report. He has advised numerous institutions including the Economic Advisory Council to the Prime Minister of India, the European Securities and Markets Authority, and the Norwegian Sovereign Wealth Fund. Currently, he co-chairs the Fintech workstream of the India-UK Financial Partnership, helping to shape the future of financial technology across borders. Research Initiatives Professor Ramadorai previously led the Initiative on International Comparative Household Finance, funded by the Sloan Foundation, which established household finance as a distinct sub-field of research. He is in the process of setting up a new initiative at Imperial College Business School to further advance this area of study. His work has influenced both academic research and practical policy interventions in financial markets around the world.
