Desi R. Ivanova is a research fellow at the University of Oxford's Department of Statistics under the Florence Nightingale Bicentennial Fellowship. Her work bridges probabilistic machine learning, Bayesian experimental design, and LLM evaluation frameworks. She holds a DPhil in Statistics from Oxford's StatML CDT program (2020-2024) and an MMORSE in Mathematics from University of Warwick (2011-2016) with Erasmus exchange at LMU Munich. Research spans causal machine learning and uncertainty quantification Developed CO-BED and Step-DAD frameworks Focus on LLM evaluation methodology and calibration Expert in real-time adaptive experimental systems Her publications demonstrate expertise in Bayesian self-consistency methods, neural data compression, and privacy-preserving dataset merging. Key contributions include improving amortized inference efficiency and developing gradient-based causal experimental designs. Current work emphasizes rigorous statistical evaluation of language models, advocating for appropriate uncertainty quantification when analyzing performance across small datasets. She critiques CLT-based methods for LLM evaluation and proposes more robust frequentist and Bayesian alternatives.
Melih Kandemir is an Associate Professor of Machine Learning at the University of Southern Denmark, Department of Mathematics and Computer Science. He earned his PhD in 2013 from Aalto University under Prof. Samuel Kaski, followed by postdoctoral work at Heidelberg University (with Prof. Fred Hamprecht) and an assistant professorship at Ozyegin University, Turkey. Prior to joining SDU, he led research at Bosch Center for Artificial Intelligence. Education: PhD (Aalto University, 2013), Postdoc (Heidelberg University). Previous Roles: Assistant Professor (Ozyegin University), Research Group Leader (Bosch CAI). His research focuses on Bayesian inference, stochastic process modeling with deep neural networks, and applications to reinforcement learning and continual learning. He leads the SDU Adaptive Intelligence (ADIN) Lab and is an ELLIS Member, reflecting his standing as a top European AI researcher. His work addresses critical challenges in uncertainty quantification, exploration strategies, and theoretically grounded algorithms for decision-making systems. Recent publications highlight expertise in model-based reinforcement learning (e.g., MOMBO for offline RL), PAC-Bayesian bandits, evidential learning for robust classification, and neural stochastic differential equations. His scientific awards include a Best Paper Award (2017) and ELLIS Membership. He also explores interdisciplinary applications of natural sciences to sustainable technology development.
Jocelyn Chanussot is a Professor at Grenoble Institute of Technology, holding the AXA Chair of Remote Sensing. He is affiliated with GIPSA-Lab (Laboratoire de traitement du Signal et des Images) at Ense3 (École Nationale Supérieure de l'Énergie, l'Eau et l'Environnement) and maintains a connection with the Chinese Academy of Sciences through his AXA Chair position. His research focuses on advancing remote sensing technologies, particularly in hyperspectral imaging and artificial intelligence applications for environmental monitoring. Professor Chanussot specializes in hyperspectral imaging, which captures information across several hundred wavelengths, allowing for detailed characterization of physical properties in observed scenes. His work develops algorithms to extract meaningful information from complex remote sensing data, with applications spanning natural disaster monitoring, environmental observation, biodiversity assessment, and urban planning. He has pioneered approaches that leverage artificial intelligence, particularly deep learning techniques, to process and analyze large-scale remote sensing datasets. His scholarly output shows a clear trend toward integrating advanced AI techniques with remote sensing data. Recent publications demonstrate growing emphasis on transformer architectures, contrastive learning, generative models, and foundation models specifically adapted for hyperspectral data. His work increasingly addresses multimodal data fusion, anomaly detection, and real-time processing for applications in natural disaster response. Ranked among the 157 most cited French researchers in 2019 by Clarivate Analytics Professor Chanussot actively serves on numerous conference program committees, particularly for SPIE's Image and Signal Processing for Remote Sensing conferences. His research is supported by the AXA Research Fund through his AXA Chair in Remote Sensing, which focuses on developing algorithms for natural disaster monitoring and emergency response. He collaborates extensively with international institutions including UCLA and Stanford University on applications ranging from toxic gas detection to tropical forest biodiversity assessment. He leads research at GIPSA-Lab, focusing on developing advanced tools and algorithms for extracting information from complex remote sensing data. His team works on processing heterogeneous data including aerial photos, multispectral and hyperspectral images, and other environmental measurements to improve natural disaster prediction and response capabilities.
Emmanuel Dupoux is a Professor at École des Hautes Études en Sciences Sociales (EHESS), affiliated with the School of Advanced Studies in Social Sciences and the Laboratory of Cognitive Science and Psycholinguistics (LSCP). His work bridges cognitive science, computational linguistics, and machine learning to study infant language acquisition and social cognition. Co-creator and director of EHESS Cognitive Science Master program Former LSCP laboratory director (1998-2009) Current research focuses on textless speech modeling and ecological audio analysis Research areas include: Modeling early language acquisition mechanisms using Bayesian models and HMM Investigating phonological 'deafness' and critical age plasticity Studying social cognition development through infant-toddler experiments His technical contributions span speech processing toolkits like Shennong, emergent communication frameworks (STOP dataset), and innovative approaches to self-supervised speech modeling. Recent publications demonstrate breakthroughs in: Topography-inspired CNN designs for better memory efficiency Prosody-aware generative spoken language models (pGSLM) Textless emotion conversion systems He actively explores how machine learning can reverse-engineer infant language learning processes from ecological audio data, with applications in neurodegenerative disease diagnostics. Key collaborations include: INRIA's Cognitive Machine Learning (CoML) team Facebook AI Research (FAIR) partnerships VoxPopuli multilingual speech corpus development
Grégoire Montavon is a Professor at Charité – Universitätsmedizin Berlin and Research Group Lead at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His appointment commenced on April 1, 2025, as part of BIFOLD's institutional partnership with Charité. He holds a Master's in Communication Systems from École Polytechnique Fédérale de Lausanne (2009) and a Ph.D. in Machine Learning from Technische Universität Berlin (2013). Master's: Communication Systems, EPFL (2009) Ph.D.: Machine Learning, TU Berlin (2013) Montavon pioneers Explainable AI (XAI) for medical applications, developing methods like Layer-Wise Relevance Propagation (LRP) to verify deep learning models in diagnostics. His work bridges machine learning theory with clinical practice, focusing on model transparency for tumor classification, bias mitigation, and regression strategy analysis. Recent research emphasizes counterfactual explainers and spectral analysis for explanation quality assessment. His 2025 publications reveal a cohesive trajectory: advancing explainability frameworks for distance-based classifiers, diffusion models in oncology, and visual counterfactual systems. Key themes include robustness against dataset shifts, metadata integration for fairness, and formal desiderata for explainer design—consistently targeting real-world medical AI deployment. Award highlights: 2013 Dimitris N. Chorafas Award 2020 Pattern Recognition Best Paper Award 2022 Digital Signal Processing Best Paper Award 2025 XAI Conference Best Paper Award (for XpertAI) As BIFOLD Research Group Lead for Explainable Machine Learning in Medicine, Montavon directs projects funded through Charité-BIFOLD partnerships, including the agility project on deep generative model transparency. His team collaborates with Klaus-Robert Müller and others on NIH/DFG grants for AI-driven cancer treatment personalization and Clever-Hans strategy pruning. He leads the BIFOLD research unit Explainable Machine Learning in Medicine, focusing on clinical AI integration. Current initiatives include ICLR 2025 contributions on foundation model reliability and MedI diffusion frameworks for tumor classification bias reduction.
Jacopo Staiano is a Senior Assistant Professor (RTDb) at the Department of Economics & Management, University of Trento (Italy). His academic journey includes previous positions as Head of Research at reciTAL.ai, research affiliate at Data-Pop Alliance, senior data scientist at Fortia Financial Solutions, and post-doctoral researcher at LIP6, UPMC - Sorbonne Universités and Fondazione Bruno Kessler. Staiano received his BSc in Computer Engineering from the University of Pisa (2003), an MA in Sonic Arts from Queen's University of Belfast (2005), and an MSc in Human Language Technologies and Interfaces from the University of Trento (2010). He completed his PhD under Prof. Nicu Sebe at the Department of Information Engineering and Computer Science, University of Trento. His academic visits include the Intelligent Systems Lab at University of Amsterdam, Ambient Intelligence Research Lab at Stanford University, Human Dynamics Lab at MIT Media Lab, and Telefonica I+D. Staiano's research spans multiple domains with a strong focus on Natural Language Processing and Human-Computer Interaction. His work ranges from modeling human behavior when interacting with technology to analyzing social network structures and virality dynamics. His recent publications demonstrate a significant shift toward large language models, with applications in sustainability reporting, medical diagnostics, financial analysis, and bias detection. His work shows sophisticated integration of technical AI capabilities with social science perspectives. Staiano has received several prestigious awards throughout his career, including an Honorable Mention at ACM DIS 2012, Best Paper Award at ACM UbiComp 2014, the UMUAI James Chen Award 2016, and most recently the Ten Year Technical Impact Award from ACM ICMI 2024. These awards reflect the sustained impact and quality of his research across multiple domains. His research has been supported by collaborations with major institutions including MIT Media Lab, Stanford University, and Telefonica I+D, as well as industry partnerships. His work on projects like DepecheMood for emotion analysis and SALSA for multimodal group behavior analysis has established him as a significant contributor to affective computing and social signal processing. Staiano maintains active connections with multiple research communities, particularly in the areas of computational social science, natural language processing, and affective computing. His work bridges technical AI development with real-world applications in finance, healthcare, and social good initiatives.
Cornelia Kienle is a scientist at the Ecotox Centre, affiliated with Eawag and EPFL. She has been working in aquatic ecotoxicology since 2008, focusing on in vitro and in vivo bioassays for monitoring aquatic ecosystem health. University: Eawag Expertise: Aquatic ecotoxicology, bioassay development, wastewater treatment impact assessment Her research spans aquatic toxicology , environmental risk assessment , and biomonitoring . She specializes in evaluating chemical micropollutants, endocrine disruptors, and stressor interactions in freshwater systems using advanced bioassay batteries and biomarker analysis. Recent publications highlight her work on micropollutant elimination via ozonation, biological early warning systems , and molecular biomarker applications . She contributes to Swiss and EU water quality frameworks through test system design and multi-assay evaluation. Key project areas include: Effect-based water quality monitoring Post-treatment wastewater analysis Ecotoxicity of facade runoff Integrated stream water quality management
Alexandra Tanner is Junior Professor for Historical Building Research and Monument Preservation at the Technical University of Berlin's Department of Building Archaeology and Heritage Conservation since 2025. Previously, she held positions as Postdoc at the University of Zurich (2019-2023) and Scientific Assistant at ETH Zurich (2011-2014). Her academic journey includes a Diploma in Architecture from ETH Zurich, a Master's in Monument Preservation from the University of Bamberg, and a PhD in Classical Archaeology from the University of Zurich. Her research focuses on Classical Mediterranean architecture, particularly Hellenistic religious structures, urban development, and conservation methodologies. Tanner specializes in the analysis of naiskoi (small temple-like structures), agora organization, and geometric principles in ancient design. Her fieldwork encompasses major sites including Aigeira, Monte Iato, Aegina Kolonna, and Eretria, where she conducts architectural documentation and conservation. Publication analysis reveals Tanner consistently explores Hellenistic architectural evolution, spatial organization principles, and site conservation techniques. Her works demonstrate recurring themes of multi-functional sacred spaces, Greek-Roman architectural transitions, and geometric proportionality in urban design. The chronological progression shows increasing focus on Sicilian urban complexes in recent publications. She leads significant field projects including: Excavation and analysis of Hellenistic naiskoi at Aigeira North Hall Complex research at Monte Iato Restoration of prehistoric suburbs at Aegina Kolonna Swiss National Science Foundation project 'Becoming Roman' on urban transformation in Sicily As scientific member of the Swiss Archaeological School in Greece (ESAG) and Koldewey-Gesellschaft, Tanner collaborates internationally on architectural heritage projects across Mediterranean sites.
Selina-Lara Fricke is a Researcher at the Department of Biosystems Science and Engineering (BSSE) of ETH Zurich, based at the Basel campus within the Professorship for Computational Biology. Her office is located at Klingelbergstrasse 48 (BSS G 15.1), 4056 Basel, Switzerland, with contact details including work phone +41 61 387 34 52 and email selina.fricke@bsse.ethz.ch. Her research centers on computational biology and bioinformatics, applying advanced computational modeling to analyze complex biological systems. This work aligns with BSSE's interdisciplinary mission integrating engineering, computer science, and life sciences to address challenges in biosystems. Key focus areas include algorithm development for biological data analysis and mathematical modeling of cellular processes. As part of ETH Zurich's Computational Biology group, Dr. Fricke contributes to a collaborative research environment that bridges theoretical and applied biosciences. The team emphasizes translational approaches to convert computational insights into practical biological understanding, leveraging Basel's ecosystem of pharmaceutical and biotech partners for real-world impact.
Dr. Arthur F. Petusseau is a biomedical optics researcher specializing in fluorescence-guided surgery, hypoxia imaging, and radiation therapy monitoring. His work focuses on developing advanced optical imaging techniques for tumor detection, oxygen quantification, and surgical navigation. Key affiliations: Collaborator with experts like Brian Pogue, Petr Bruza, and Marien Ochoa Institutional affiliation not explicitly stated in provided text Research Interests: Dr. Petusseau's work spans biomedical optics, surgical imaging, and tumor oxygenation analysis. He develops technologies leveraging porphyrin-based fluorescence, single-photon sensors, and real-time oxygen mapping to improve cancer treatment outcomes. Recent Publications: His research includes time-of-flight fluorescence imaging for tumor depth assessment, delayed fluorescence signal processing for hypoxia quantification, and Cherenkov imaging for radiation therapy monitoring. Keywords across his work include Photodynamic Therapy , Radiotherapy , SPAD Sensors , and Biological Imaging . Technical Innovations: Notable contributions include the PRESTO non-invasive skin lesion detection tool and pressure-enhanced tissue oxygen sensing. His 2025 work on deep learning-enhanced fluorescence reconstruction demonstrates cutting-edge computational applications in surgical imaging.
Prof. Dr. Timothy Griffin serves as Head of the FHNW Institute of Bioenergy and Resource Efficiency within the School of Engineering and Environment at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW). His leadership role positions him at the critical intersection of technological innovation and environmental sustainability, directing research initiatives that address global energy and resource challenges. His research expertise spans: Bioenergy conversion technologies Industrial resource efficiency optimization Sustainable engineering systems design Environmental impact assessment methodologies Circular economy implementation As institute head, Griffin oversees applied research projects focusing on practical solutions for energy transition and sustainable resource management. His work emphasizes real-world implementation through industry partnerships and interdisciplinary collaboration, directly supporting FHNW's mission of bridging technological advancement with environmental stewardship. While specific publication details aren't provided in available materials, his research trajectory demonstrates consistent focus on scalable sustainable technologies for industrial applications. Griffin actively leads research teams developing bioenergy solutions and resource-efficient processes, though specific grant details and student supervision information aren't documented in current sources. His institute serves as a regional hub for sustainable technology development, working closely with industrial partners to translate research into operational practices.
Yi Zhao is a Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the SCI-STI-FM group (IPESE) at EPFL Valais Wallis. Their work bridges energy systems analysis, industrial decarbonization, and refrigeration engineering. Research Interests Energy storage technologies (e.g., solid oxide electrolysis) Refinery and heavy industry decarbonization strategies Application of genetic algorithms and active learning in energy optimization Low-GWP refrigerant systems for automotive use Development of databases for industrial sustainability (AIDRES project) Modeling of thermodynamic and fluid dynamics in refrigeration systems Scientific Contributions Leading the AIDRES database project for European industrial decarbonization Innovating cost estimation frameworks for hydrogen storage systems Advancing AI-driven optimization in energy system design Investigating CO2 electrolysis safety and efficiency Contact: yi.zhao@epfl.ch
Adrienne Grêt-Regamey serves as Full Professor at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, where she heads the Institute for Spatial and Landscape Development. Her academic career spans over 15 years at one of Europe's leading technical universities, with significant leadership roles including Head of the Engagement Platform for Future Cities Lab Global since 2021. Her research focuses on the complex interactions between human activities and landscape evolution across temporal and spatial scales. Specializing in landscape planning and environmental science, she investigates land-use decision models through forecasting and backcasting approaches. A key innovation in her work involves developing state-of-the-art 3D visualizations and auralizations within her laboratory to understand public perception of landscape changes and create decision support tools for participatory planning processes. Her methodology emphasizes iterative design-science collaboration to develop place-specific solutions that balance human well-being with environmental sustainability. Analysis of her recent publications reveals a strong trend toward interdisciplinary research integrating spatial analysis, ecosystem services assessment, and participatory decision-making. Her work increasingly addresses urgent global challenges including renewable energy transitions, climate adaptation strategies, and urban-rural dynamics, often employing advanced computational methods like machine learning and immersive virtual reality environments. Dandelion Entrepreneurship Award (2023) Most Influential Female Award in Land System Science (2022) ERC Starting Grant for GLOBESCAPE project (2017) Swiss National Science Foundation Transdisciplinary Award (2013) ETH Silver Medals for both Master and PhD theses Grêt-Regamey actively contributes to scientific governance as Member of the Swiss Science Council (since 2024) and Advisory Committee of the Wyss Academy for Nature. She serves as Associate Editor for Landscape and Urban Planning and Editor for disP - The Planning Review. Her research program has secured significant funding including an ERC Starting Grant and multiple Swiss National Science Foundation projects, with current work focusing on integrating design and land system science to foster place-making in peri-urban landscapes. Her laboratory at ETH Zurich specializes in developing immersive 3D environments for landscape assessment and planning, with recent work extending into therapeutic applications of virtual reality. She leads the Future Cities Lab Global engagement platform, facilitating knowledge exchange between researchers, practitioners, and policymakers on sustainable urban development challenges worldwide.
Seyed Armin Tajalli is a Visiting Professor at the École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI), Institute of Electrical Engineering (IEM), and Integrated Neurotechnologies Laboratory (INL). He has extensive experience in ultra-low power integrated circuit design and high-speed serial communication systems. Dr. Tajalli received his B.S.E.E. and M.S.E.E. degrees (with honors) from Sharif University of Technology, Tehran, and Tehran Polytechnic University in 1997 and 1999, respectively. He earned his Ph.D.E.E. on low-power integrated circuit design techniques in 2010 from EPFL. Prior to his academic career, he worked with Emad Semicon from 1998-2006 as a senior analog/RF design engineer and technical manager. His research focuses on ultra-low power circuit design, including subthreshold source-coupled logic, analog/mixed-signal circuits, data converters, and serial data transceivers. His work addresses critical challenges in low-power design, high-speed communication, and energy-efficient circuit implementations. Recent publications demonstrate his expertise in spectrum shaping techniques, crosstalk reduction, and high-speed serial links for memory interfaces and multi-drop communication systems. Dr. Tajalli's publications span high-impact journals and conferences including IEEE Journal of Solid-State Circuits, IEEE Transactions on Circuits and Systems, and major conferences like ISSCC, ISCAS, and VLSI Symposium. His research demonstrates consistent innovation in low-power circuit techniques with practical applications in high-speed communication systems. Kharazmi Award on Research and Development, 2000 Outstanding Design Engineer, Emad Semicon, 2001 Presidential Award of the best Iranian researchers, 2003 ACM/CICC Award, 2009 EPFL Prime Special Award, 2009 Dr. Tajalli has advised PhD students including Kiarash Gharibdoust, whose thesis focused on Hybrid NRZ/Multi-Tone Signaling for High-Speed Low-Power Wireline Transceivers. His collaborative work with the ALGO team has resulted in significant contributions to high-speed serial data transceiver architectures. His research has been supported by various academic and industrial partnerships focused on advancing integrated circuit design techniques. His work is primarily conducted within EPFL's Integrated Neurotechnologies Laboratory (INL) and previously with the Microelectronic Systems Laboratory (LSM). These labs provide state-of-the-art facilities for mixed-signal IC design, characterization, and testing, supporting his research in ultra-low power circuits and high-speed communication systems.
Janna Hastings is Assistant Professor for Medical Knowledge and Decision Support at the Institute for Implementation Science in Health Care (Faculty of Medicine) at the University of Zurich since August 2022, while also serving as Vice-Director of the School of Medicine at the University of St. Gallen. Her research focuses on digitalization in clinical contexts, examining how AI-driven knowledge systems reshape clinical practice, professional identity, and doctor-patient relationships. Education: PhD in Computational Biology (University of Cambridge, 2019), part-time MSc in Computer Science (University of South Africa, 2011), MSc in Philosophy (Open University, 2012) Prior Roles: Group Coordinator at European Bioinformatics Institute (2006-2015), Postdoctoral Researcher at Otto-von-Guericke University Magdeburg (2019-2022), Co-Leader of Human Behaviour-Change Project at University College London (2017-2022) Her research spans ontology development for biomedical domains (ChEBI, Human Behaviour Ontology), AI applications in healthcare decision-making, and behavior change interventions. Key projects include: Building ChEBI molecular ontology Developing Human Behaviour-Change Project knowledge system Ontology-driven mental health frameworks LLM applications in radiation oncology Time-series modeling of metabolism in ageing She explores the capabilities and limitations of clinical AI systems, with publications in JMIR and Lancet Digital Health , covering topics like bias prevention in generative AI and proteomic biomarker discovery. Her work bridges biomedical research with implementation science and digital ethics.
