Random Structures Professors and Supervisors in Switzerland

Paolo TisoView profile

Lecturer

ETH ZurichSwitzerland

Dr. Paolo Tiso is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, affiliated with George Haller's nonlinear dynamics research group. His work focuses on model order reduction for nonlinear structural mechanics, including light-weight and thermo-elastic systems, flexible multibody dynamics, and contact/friction mechanics. He contributes to advancing methodologies like spectral submanifolds and hyper-reduction techniques for efficient simulation of complex systems. His research emphasizes practical applications such as bolted joint analysis, aeroelastic systems, and structural health monitoring. Notable contributions include benchmark challenges for nonlinear dynamics predictions and advancements in component mode synthesis. Current projects explore geometrically nonlinear systems and their experimental validation. Dr. Tiso's recent publications highlight trends in reduced-order modeling for nonlinear systems, stochastic parameter analysis, and hyper-reduction strategies. He actively engages with industrial challenges, such as optimizing morphing airborne wind energy systems and addressing acoustic fatigue in aerostructures. Collaborations include the ETH Zürich nonlinear dynamics group and international conferences like IMAC. His work bridges computational mechanics and experimental validation, driving innovation in structural dynamics and system identification.

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Andreas LichtenbergerView profile

Lecturer

ETH ZurichSwitzerland

Dr. Andreas Lichtenberger is a Lecturer at the Department of Physics at ETH Zürich. His research focuses on advancing physics education through innovative technologies like augmented reality and exploring effective teaching methodologies. His work emphasizes conceptual understanding in electromagnetism, kinematics, and vector fields, with a particular interest in formative assessment strategies and the role of multiple external representations (MER) in learning. Key research areas include: Technology-enhanced learning (AR/VR in physics education) Cognitive aspects of physics concept acquisition Gender differences in representational competence Experimental validation of educational interventions His publications highlight contributions to: Designing AR tools for Lorentz force visualization Concreteness fading pedagogy in secondary physics Eye-tracking analysis of student problem-solving Development of competency assessment inventories No scientific awards are explicitly listed. He collaborates widely with educational researchers and physicists, contributing to both theoretical and applied aspects of STEM education.

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Ola SvenssonView profile

Associate Professor

Swiss Federal Institute of Technology in LausanneSwitzerland

Ola Svensson is an Associate Professor at the School of Computer and Communication Sciences, EPFL. His research focuses on approximation algorithms, combinatorial optimization, computational complexity, and scheduling. He has been supported by grants including the ERC Starting Grant "OptApprox" (2014-2019), SNF grants, and the ERC Consolidator Grant "POTCO" (2023-). He teaches courses such as Advanced Algorithms and Approximation Algorithms and Hardness of Approximation. Education: PhD from IDSIA - Universita della Svizzera italiana (2009) and Master's from Uppsala University (2005). Research Interests: Design and analysis of approximation algorithms for NP-hard problems, scheduling, and computational complexity. He explores limitations of approximation techniques through hardness results and contributes to theoretical computer science. Publications span clustering, scheduling, and graph problems like the Traveling Salesman Problem. Recent work includes learning-augmented algorithms and robust optimization. Awards: I&C teaching award and best paper awards at FOCS (2017) and STOC (2018). Over a dozen PhD students advised, many entering postdocs or industry roles. Labs/Teams: Part of the theory group at EPFL, collaborating on academic projects and course development.

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Daniele ZambonView profile

Researcher

University of Italian SwitzerlandSwitzerland

Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.

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Abraham Lewis LevitanView profile

Research Fellow

Paul Scherrer InstituteSwitzerland

Abraham Lewis Levitan is a Postdoctoral Fellow (PSI-FELLOW-III-3i) at the Paul Scherrer Institute (PSI) in Switzerland, where he is a member of the Computational X-ray Imaging group within the Laboratory for Synchrotron Radiation and Femtochemistry (LSF). His research focuses on the development of ptychography and coherent lensless imaging methodologies for X-ray microscopy, with particular emphasis on single-shot reconstructions and the mathematical underpinnings of phase retrieval. Education: Ph.D. from the Massachusetts Institute of Technology (MIT), Photon Scattering Lab Research Interests: Levitan's work spans computational imaging, X-ray microscopy, ptychography, coherent lensless imaging, phase retrieval, and optics. He develops advanced algorithms for reconstructing high-resolution images from diffraction patterns, with a focus on single-shot and randomized illumination techniques. His research addresses fundamental challenges in wave field reconstruction, particularly for partially coherent light, and integrates deep learning for probe calibration and image reconstruction. Publication Trends: His recent publications (2020-2025) in leading optics journals (Optics Letters, Optics Express) consistently advance computational methods for X-ray imaging. Key contributions include single-shot ptychography as structured illumination, error metrics for partially coherent fields, deep learning-based probe imaging, maskless Fourier transform holography, and single-frame diffractive imaging with randomized illumination. These works demonstrate a trajectory toward robust, real-time imaging solutions for dynamic samples. Grants: PSI-FELLOW-III-3i fellowship Laboratory and Team: Levitan is an active contributor to the Computational X-ray Imaging group at PSI, which pioneers novel imaging techniques using synchrotron radiation and free-electron lasers. The group's work enables unprecedented resolution in materials science, biology, and chemistry by overcoming the limitations of traditional lens-based microscopy.

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Dina PomeranzView profile

Assistant Professor

University of ZurichSwitzerland

Prof. Dina Pomeranz is an UBS Foundation Associate Professor of Applied Microeconomics at the University of Zurich's Department of Economics. Her research focuses on public policies in developing countries, particularly taxation, public procurement, firm development, and environmental sustainability. She previously held positions at Harvard Business School and MIT's Poverty Action Lab. Pomeranz's work bridges academia and practice, collaborating with governments, NGOs, and corporations to design evidence-based policies. Her notable projects include studies on tax evasion in Chile and Ecuador, solar energy access in Kenya, and the role of firm networks in tax compliance. She has received prestigious grants like the ERC (2017) and holds affiliations with J-PAL, CEPR, and the International Growth Centre. Pomeranz emphasizes global progress in reducing poverty and advocates for policies that balance economic development with environmental stewardship. Education: PhD in Economics, Harvard University Postdoctoral Fellowship, MIT's Poverty Action Lab Research Interests: Her work addresses taxation challenges in low-income countries, leveraging randomized evaluations to assess policy impacts. She explores how improved tax systems and energy access combat poverty while promoting sustainable development. Awards & Affiliations: ERC Starting Grant (2017) Affiliate, Abdul Latif Jameel Poverty Action Lab (J-PAL) Member, International Institute of Public Finance (IIPF) Policy Engagement: Serves on boards of organizations like Helvetas and Evidence Action, advising Swiss parliament on international cooperation strategies. Her work highlights pragmatic solutions to global inequality through collaborative research.

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Melanie MortenView profile

Researcher

University of ZurichSwitzerland

Melanie Morten is a Research Associate at Stanford University's Department of Economics within the School of Humanities and Sciences. Her work bridges development economics and spatial economics, focusing on migration, risk sharing, labor markets, and trade dynamics in low- and middle-income countries. Current Position: Research Associate, Stanford University Research Domains: Development Economics, Spatial Economics, Migration Collaborative Projects: Network-based hiring, border wall impacts, seasonal migration subsidies in Bangladesh Her research explores how spatial frictions like migration costs and infrastructure affect economic outcomes, including productivity, trade, and risk sharing. She employs randomized controlled trials, structural models, and field experiments to analyze migration's role in informal insurance, labor market integration, and urban development. Recent work examines gentrification's inequality effects, transportation's influence on migration patterns, and network hiring's dual impact on firms in developing economies. The evolving focus spans from localized labor dynamics to global migration policy implications. Morten has contributed to leading journals and working paper series, with media coverage from The Economist , The Washington Post , and VoxEU . She co-leads the Stanford Guestworker Migration Initiative, applying her findings to real-world policy challenges. Key Collaborators: Gharad Bryan, Ahmed Mushfiq Mobarak, Arun Chandrasekhar Policy Relevance: Labor mobility, regional inequality, development strategies Current Projects: Spatial economics frameworks for urbanizing economies

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Laurent DonzéView profile

Professor

University of FribourgSwitzerland

Laurent Donzé is a Professor of Applied Statistics and Modelling at the Department of Informatics, Faculty of Economics and Social Sciences, University of Fribourg. He is also a Research Professor at KOF ETH Zurich and a Professor of Econometrics at the University of Neuchâtel. He leads the ASAM research group and has extensive experience in teaching mathematics, econometrics, and statistics. His educational background includes a Ph.D. in Econometrics from the University of Fribourg, followed by research roles at IRE and KOF ETH Zurich. His academic journey reflects deep engagement with statistical methodology and applied economic research. Donzé's primary research interests lie in applied statistics, particularly survey methodology, fuzzy statistics, imputation, causal inference, matching techniques, and wage discrimination analysis . He has made significant contributions to the development and application of fuzzy statistical tools, especially in defuzzification and fuzzy regression. His recent publications (2019–2025) demonstrate a consistent focus on fuzzy confidence intervals, fuzzy p-values, fuzzy ANOVA, and fuzzy regression models , often applied to real-world datasets like SHARE and Swiss SILC. These works emphasize robust statistical inference under uncertainty and contribute to both theoretical and applied advancements in fuzzy statistics. Among his scientific recognitions is the Best Student Paper Award at IJJCI 2020 . He has also edited special issues and contributed to leading journals and conferences in computational intelligence and fuzzy systems. Donzé has been involved in numerous research and teaching initiatives, including grants from the Swiss National Science Foundation, mentoring at the Swiss Study Foundation, and leadership in statistical societies. He has supervised research projects and collaborated with institutions such as Nestlé, the Swiss Federal Statistical Office, and pharmaSuisse. He is actively involved in academic and professional communities, serving as president of the Education and Research section of the Swiss Statistical Society and contributing to the development of statistical infrastructure in social sciences.

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Alessio RanalloView profile

Research Fellow

University of GenevaSwitzerland

Alessio Ranallo serves as a Postdoctoral Scholar at the University of Geneva, actively contributing to Antti Knowles' research group since December 2023. His work bridges mathematical physics and probability theory, with a current focus on spectral properties of random graphs and quantum lattice systems. He earned his PhD in Mathematics under Roberto Longo, specializing in Algebraic Quantum Field Theory (AQFT). His doctoral research pioneered applications of Tomita-Takesaki modular theory to quantum information theory and complexity metrics. Ranallo's interdisciplinary research spans: Mathematical Physics Probability Theory Quantum Information Theory Operator Algebras Random Graphs Quantum Lattice Systems Quantum Complexity His current investigations target spectral analysis of random graph structures and stability phenomena in quantum many-body systems. No scientific awards were documented in the available records. Information regarding student supervision or research funding remains undisclosed in public sources. He operates within Antti Knowles' collaborative research environment, which emphasizes rigorous mathematical approaches to quantum and probabilistic systems.

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JennyView profile

Professor

ETH ZurichSwitzerland

Professor Jenny leads the Jenny Research Group at ETH Zürich, specializing in turbulent reactive flows, rarefied gas kinetics, and biomedical fluid dynamics. Her work bridges fundamental research with industrial applications in energy systems and fluid mechanics. Develops advanced turbulence models (TDDM, hybrid LES/RANS) for multi-scale flows Pioneers data assimilation frameworks for RANS simulations using adjoint methods Advances particle-based stochastic algorithms for fractured porous media transport Her recent publications emphasize adaptive time integration techniques, probabilistic modeling of non-linear transport phenomena, and optimized simulation tools for hydrogen storage systems. The group's methodological innovations focus on reducing computational costs while maintaining physical accuracy through novel regularization strategies. Key applications include combustion device optimization, high-pressure tank filling analysis, and fractured reservoir simulations. Current projects integrate machine learning with traditional CFD methods to address challenges in droplet clustering, flame surface density propagation, and supersonic spray dynamics. The research framework spans from direct numerical simulations of fundamental flow physics to industrial-scale hybrid modeling implementations.

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