Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.
Angelo Ranaldo is a Full Professor of Finance and Financial Economics at the University of Basel, Faculty of Business and Economics, and holds a Senior Chair at the Swiss Finance Institute (SFI) since 2024. He was appointed by the University Council on October 24, 2023, effective August 1, 2024. Additionally, he serves as a Member of the Bank Council of the Swiss National Bank, elected in April 2023. He collaborates with major institutions including the Bank of England, the Bank for International Settlements (BIS), and the European Central Bank (ECB). His research centers on financial market liquidity, foreign exchange markets, safe assets, cryptocurrency markets, and funding mechanisms. Key areas include FX swap liquidity, collateral cycles, high-frequency trading, blockchain applications in finance, and central banking operations. He frequently collaborates with leading scholars such as Peteris Kloks, Wenqian Huang, Loriano Mancini, and Jan Wrampelmeyer. The trends in his recent publications (2022–2025) reflect a deep engagement with both traditional and emerging financial markets. His work spans central banking operations, market microstructure, and digital finance, with a strong emphasis on empirical analysis and policy relevance. Articles cover topics such as liquidity risk, currency demand, blockchain currency markets, and pension fund liquidity, published in top journals like the Journal of Finance , Journal of Financial Economics , and Review of Financial Studies . Notable scientific recognition includes the 2018 Duisenberg Fellowship awarded by the European Central Bank. This fellowship highlights his contributions to central banking research and policy-relevant financial economics. Ranaldo actively advises and collaborates with central banks and international financial institutions. His work is supported through institutional affiliations and research grants, particularly via the Swiss Finance Institute and collaborations with the BIS, ECB, and SNB. There is no public list of his advisees or students. He leads or participates in research teams focused on financial stability, market liquidity, and monetary policy, though specific lab affiliations are not detailed. He is involved in ongoing research on safe assets, currency markets, and the impact of regulation on financial intermediation, with several forthcoming publications in leading journals. His work continues to shape discourse in financial economics and central banking.
Spartaco Greppi is a Professor of Social Security and Welfare Systems at the Department of Business Economics, Health and Social Care (DEASS) of the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), where he has served as a full professor since 2013 and previously as a lecturer-researcher from 2001-2012. He is affiliated with the Labor, Welfare and Society Center (CLWS) and has held significant roles including membership on the Board of Directors of the Swiss Society of Social Work since 2013 and the MOSAICH 2.0 Scientific Commission since 2017. Greppi holds a Master's degree in Political Economy and a PhD in Economic Analysis from the University of Fribourg, Switzerland. His academic journey includes serving as a scientific collaborator at the Federal Statistical Office (1995-2001) and as a lecturer for the Master's Degree in Social Work at HES-SO since 2009. His research focuses on the critical intersection of welfare state transformations, social security systems, and evolving labor market dynamics. Greppi examines how contemporary work arrangements impact social integration, with particular attention to marginalized populations including single mothers, people with addiction issues, and those in the complementary labor market. His work bridges theoretical analysis with practical policy evaluation, consistently addressing the tension between social support and work requirements. Greppi's publication record reveals a sustained scholarly trajectory examining how media representations shape discourse around social assistance, with particular focus on the evolving narrative from moralization of 'deviant' behavior (1960s-1980s) to work incentives (1980s-1990s) and contemporary individualized performance expectations. His research increasingly addresses emerging phenomena like 'free work' (unpaid or low-paid labor) and platform economy impacts on social protection systems. Member of Board of Directors, Swiss Society of Social Work (2013-present) Expert for Social Security module in FIAS exams (2012-present) Member of MOSAICH 2.0 Scientific Commission (2017-present) Greppi leads multiple research initiatives including the Platform Labour in Urban Spaces (PLUS) project analyzing digital platforms' impact across seven European cities, and studies on secondary labor markets focused on developing comprehensive evaluation models that measure both employment outcomes and multidimensional well-being. His work consistently emphasizes the need for welfare systems to adapt to contemporary labor market transformations while maintaining social cohesion.
Barbara Solenthaler is a Lecturer at the Department of Computer Science, ETH Zurich. Her research focuses on physics-based simulations, facial animation, and machine learning applications in computer graphics.
Professor Wing-Keung Wong is a distinguished academic at the Department of Finance, Asia University . With over 187 scholarly papers and 638 citations, his work spans critical areas in financial economics and quantitative finance. Current affiliation: Asia University, Department of Finance Past affiliations: National University of Singapore, Chinese University of Hong Kong, Erasmus University Rotterdam Research Themes include: Portfolio optimization and stochastic dominance theory Market efficiency analysis across diverse financial instruments Behavioral finance and investor decision-making models Risk measurement with VAR and CVaR frameworks International financial market integration studies Quantitative trading system development Key Article Trends reveal consistent focus on empirical finance, mathematical modeling, and decision science applications in portfolio management and market anomalies.
Prof. Dr. Robert Stelter is a full Professor of Cliometrics at the University of Basel's Faculty of Business and Economics (WWZ), specializing in quantitative economic history. His work focuses on historical demographic patterns, the development of universities in medieval/early modern Europe, and elite formation processes. He holds a position within the Cliometrics Professorship department and is based at Peter Merian-Weg 6 in Basel, Switzerland. His research combines historical data analysis with modern quantitative methods, particularly using genealogical records and AI-driven heritage book analysis. Key areas include the demographic transition, academic market dynamics in pre-industrial Europe, and the interplay between pronatalist policies and authoritarian governance. Recent projects examine scholars' networks at universities like Ingolstadt, Leipzig, and Leiden, exploring how intellectual elites shaped socio-economic structures. Stelter's publications span cliometric methodologies, historical demography, and institutional economics, with a strong focus on Central and Western Europe. While no specific grants or awards are noted, his work contributes to debates on long-term economic development and historical population dynamics. He can be contacted directly at robert.stelter@unibas.ch.
Jérôme Goudet serves as Associate Professor in the Department of Ecology and Evolution at the University of Lausanne's Faculty of Biology and Medicine, where he has held academic positions continuously since 1995. He currently leads the master program Behavior, Evolution, Conservation (BEC) and previously served as Vice Head of the Department managing finance and human resources from 2001-2004. His educational background includes a Baccalaureat in Biology (1983), DEUG in Biology from University Paris VII (1985), Agronomic Engineer degree from Institut National Agronomique Paris-Grignon (1989), and PhD from University of Wales, Bangor (1993) focusing on The genetics of geographically structured populations . Professor Goudet's research centers on population genetics with emphasis on spatial genetic structure, evolutionary dynamics, and conservation applications. His work bridges theoretical modeling with empirical studies in natural populations, examining how ecological forces shape genetic variation across landscapes. He leads the Goudet research group exploring these themes through interdisciplinary approaches integrating fieldwork, laboratory analysis, and computational methods. He teaches core courses including Statistics for second-year biologists, Population Genetics for undergraduate and graduate students, and specialized R programming workshops. His administrative leadership extends beyond the BEC program to departmental strategic planning and resource management. Outside academia, Professor Goudet maintains active engagement with mountain environments through ski touring, telemarking, snowboarding, and hiking in the Alps and Jura ranges, reflecting his deep connection to the ecological systems he studies.
Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL. His educational background includes: Ph.D. in Electrical Engineering at EPFL (2012-2017) M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011) M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009) École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012) Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs). His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis. His scientific achievements have been recognized with: Best Paper Award at the MIDL Conference (2019) EPFL Best Doctorate Award (2018) Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017) Education Award from the Life Science Department, EPFL (2013) Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018). As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.
Tolga Birdal is an Assistant Professor (Lecturer) and UKRI Future Leaders Fellow in the Department of Computing at Imperial College London. As the Principal Investigator (PI) of the CIRCLE group , his research focuses on topological deep learning, geometric machine learning, and 3D computer vision, with theoretical interests in non-Euclidean inference and deep learning principles. Education: PhD and MSc in Computer Vision from Technical University of Munich (2018), BSc in Computer Science from Sabancı University (2008). Projects: PI for UKRI-EPSRC's UNTOLD (Topological Deep Learning), Royal Society's drug discovery initiative, and EPSRC's GNOMON (Generative Models in non-Euclidean Spaces). Leadership: Area Chair for CVPR, ICCV, and 3DV 2025 Publication Chair. His work bridges differential geometry, algebraic topology, and deep neural networks, with applications in quantum computer vision, 3D/4D generative priors, and medical imaging. Key contributions include novel frameworks for rotation forecasting, graph generation, and topological generalization bounds. Scientific Awards: UKRI Future Leaders Fellowship EMVA Young Professional Award
Bernhard Egger is a junior professor (adidas Stiftungsprofessur) at the Chair of Visual Computing, Cognitive Computer Vision Lab at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) . His research bridges human/machine perception of faces and shapes with synthetic data generation. Formerly, he was a postdoc at MIT's Computational Cognitive Science Lab and Computer Science & AI Lab, following a PhD in facial image annotation at the University of Basel. Research Focus: 3D Morphable Models, Statistical Shape Modeling, Inverse Rendering, AI in Mental Health, Medical Imaging. Education: PhD (University of Basel, 2017), MSc/BSc in Computer Science (University of Basel), Teaching Diploma (University of Applied Sciences Northwestern Switzerland). Notable Awards: Best Poster Award (2024, Cognitive Computational Neuroscience) Best Paper Award (CVPR Workshop 2019) Honk Award (SIGBOVIK 2020) Publications Trends: Recent work spans implicit surface modeling (ICLR 2025), 3D scene decomposition (3DV 2025), medical shape models (BVM 2025), and multimodal AI (npj Mental Health 2024). Labs: FAU's Cognitive Computer Vision Lab (advisor to 8+ students/researchers).
Dr. Zebang Shen serves as a Lecturer in the Department of Computer Science at ETH Zurich, affiliated with the Institute for Machine Learning (Institut für Maschinelles Lernen). His research activities are centered at Andreasstrasse 5, 8092 Zürich, Switzerland, with teaching responsibilities confirmed for the Autumn Semester 2025. His primary research domains include Optimization, Machine Learning, and Data Science, with specialized focus on Federated Learning, Stochastic Optimization, and Reinforcement Learning. Shen develops algorithmic solutions for projection-free optimization, minimax problems, and diffusion model applications, emphasizing theoretical guarantees alongside practical implementations in distributed learning environments. Analysis of his 2021-2025 publications reveals consistent innovation in optimization frameworks for machine learning, particularly in federated settings where privacy-utility tradeoffs and straggler resilience are addressed. His work bridges mathematical rigor (e.g., Poincaré inequalities, McKean-Vlasov equations) with scalable algorithms for real-world data science challenges. No scientific awards were documented in the available sources. While the sources confirm his faculty role and publication record, specific details regarding student advising, grant funding, or laboratory leadership were not provided. His current teaching activities indicate ongoing academic engagement at ETH Zurich. Shen operates within ETH Zurich's Institute for Machine Learning, contributing to the Department of Computer Science's research ecosystem focused on advancing machine learning theory and applications.
Torben Peters is a Lecturer in the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on 3D computer vision, deep learning, and generative models applied to geospatial analysis and photogrammetry. Research Focus: Peters develops computational tools for processing LiDAR point clouds, aerial imagery, and satellite data. His work enables automated environmental monitoring (e.g., forest inventories and avalanche mapping) and urban modeling through advanced segmentation and 3D reconstruction techniques. Generative models like TetraDiffusion expand capabilities in geometric deep learning. Publication Trends: Recent articles emphasize scalable geospatial AI, including war damage assessment in Ukraine, global biomass datasets, and self-supervised shape completion. Methodological innovations center on reducing annotation dependencies and improving geometric accuracy. Teaching: Leads courses on image-based mapping and geodetic data processing at ETH Zürich.
Jean-Marc Odobez is a Senior Scientist at the IDIAP Research Institute and Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he is affiliated with the School of Engineering and serves on the Electrical Engineering Doctoral committee (EDEE). He leads the Perception & Activity Understanding Group at Idiap and has extensive teaching responsibilities across multiple departments. Dr. Odobez received his PhD in Computer Science from Rennes University in 1994. His research focuses on multimodal perception systems combining computer vision, statistical machine learning, and deep learning for activity recognition, behavior understanding, and human-robot interaction. His work spans diverse application domains including human health assessment, social robotics, and media content analysis. His recent research shows strong trends in gaze estimation, human activity recognition, and multimodal processing. His team has developed innovative solutions for gaze tracking, head pose estimation, and activity recognition using depth sensors and neural networks. His work increasingly bridges computer vision with digital humanities, particularly in the analysis of ancient Maya glyphs. IEEE member Associate Editor of Machine Vision and Applications journal Dr. Odobez has supervised numerous PhD students and serves as a committee member for the Electrical Engineering Doctoral program. He has been principal investigator for over 16 European and Swiss research projects and has worked on 10 technology transfer projects with SMEs. He co-founded Klewel SA and Eyeware SA, focusing on eye tracking and attention modeling technologies. His research group actively collaborates with industry partners and maintains strong connections with the computer vision and human-computer interaction research communities.
Felix Holzmeister is a Professor at the University of Innsbruck’s Department of Economics , Austria, whose research agenda spans experimental and behavioral finance, meta-science, risk perception, and the reproducibility of empirical research. His work is characterised by large-scale collaborative projects—often involving dozens of co-authors—that combine laboratory experiments, field interventions, and meta-analytical methods to understand how individuals, particularly finance professionals, form expectations, perceive risk, and make decisions under uncertainty. Research Interests: Experimental and behavioural finance, with special attention to replicability of asset-market findings Risk preferences and risk perception among finance professionals and retail investors Meta-science topics such as non-standard errors, reviewer reliability, and computational reproducibility Policy-oriented behavioural interventions (nudging) in household finance and debt collection Across more than fifteen recent papers, Holzmeister’s research exhibits a clear focus on methodological rigour and transparency . His 2024 Journal of Finance article on non-standard errors—co-authored with over 150 colleagues—has already garnered thousands of downloads and citations, illustrating the broad interest in improving statistical inference in finance. A parallel strand of work uses preregistered experiments to test the robustness of classic asset-market findings, while additional studies explore how cognitive skills and personality traits shape fund-manager performance and how choice architecture influences portfolio allocation. Scientific Impact & Recognition: Top-3 000 SSRN author by total downloads (>24 000) Top-10 500 SSRN author by total citations (>100) Lead or co-author on large multi-institutional studies featured in top finance journals Collaboration & Funding: Holzmeister routinely leads interdisciplinary teams involving institutions such as the Stockholm School of Economics, VU Amsterdam, HEC Paris, the University of Gothenburg, and Copenhagen Business School. Funding acknowledgements in his papers imply support from national science foundations and European research councils, although specific grant numbers are not detailed in the present text. Laboratory & Research Environment: He conducts experiments within the University of Innsbruck’s experimental-economics laboratory infrastructure and is affiliated with cross-university consortia such as the “Non-Standard Errors Project” and the “Researcher Variation in Economics” consortium, which bring together dozens of scholars to tackle methodological challenges in economics and finance.
Christophe Pérignon is a Professor and Associate Dean for Research at HEC Paris, a leading business school in France. He is affiliated with the Finance Department and has established himself as a prominent scholar in financial risk management, systemic risk, and computational reproducibility in finance. His work bridges academic research with practical applications in banking regulation and risk management. His research spans several critical areas in modern finance: Advanced risk management methodologies, particularly Value-at-Risk (VaR) modeling and validation Systemic risk measurement and financial regulation frameworks Computational reproducibility in financial research through multi-analyst studies Machine learning applications in banking and credit scoring Ethical considerations in algorithmic finance Professor Pérignon's recent work demonstrates a significant shift toward examining the reproducibility crisis in financial research and the integration of machine learning techniques into traditional risk management frameworks. His influential paper "Nonstandard Errors" in the Journal of Finance (2024) examines how different research teams approach the same financial questions, highlighting methodological challenges in the field. He has also pioneered tools like "The Risk Map" for validating risk models, which has practical applications for financial institutions. His scholarly impact is substantial, with over 38 published papers that have collectively received more than 50,000 downloads and 499 citations on SSRN, placing him among the top researchers in finance. His collaborative work spans multiple international institutions, reflecting the global nature of financial research and regulation. As Associate Dean for Research at HEC Paris, Professor Pérignon plays a key leadership role in shaping the research agenda of one of Europe's premier business schools. His work continues to influence both academic discourse and practical risk management approaches in the financial industry worldwide.
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