جستجوی استاد راهنمای دانشگاهی

• Dynamical systems and gradient flows
• Analysis of partial differential equations
• Stochastic analysis
• +۲ مورد دیگر

Arnulf Jentzen is a distinguished mathematician holding dual positions as Presidential Chair Professor at the School of Data Science and Shenzhen Research Institute of Big Data at The Chinese University of Hong Kong, Shenzhen, and as Full Professor at the Faculty of Mathematics and Computer Science at the University of Münster, Germany. His research spans multiple institutions with significant contributions across mathematical disciplines. His primary research interests include dynamical systems and gradient flows (particularly geometric properties, domains of attractions, blow-up phenomena), analysis of partial differential equations, stochastic analysis (including stochastic calculus and well-posedness analysis), machine learning (with focus on mathematics for deep learning and stochastic gradient descent methods), and numerical analysis (particularly computational stochastics and computational finance). His work demonstrates a strong interdisciplinary approach bridging pure mathematics with practical computational applications. Jentzen's publication record shows a clear trend toward machine learning applications in solving complex mathematical problems, particularly in overcoming the curse of dimensionality in high-dimensional PDEs through deep neural networks. His research group actively publishes on optimization methods like Adam, convergence analysis, and applications of deep learning to partial differential equations and optimal control problems. ICBS Frontier of Science Award in Mathematics (2024) Fellow, Lamarr Institute (2023) ERC Consolidator Grant (2022) Joseph F. Traub Prize for Achievement in Information-Based Complexity (2022) Felix Klein Prize, European Mathematical Society (EMS) (2020) Professor Jentzen advises numerous PhD students across both institutions and serves on multiple editorial boards including SIAM Journal on Numerical Analysis, Journal of Complexity, and Communications in Computational Physics. His research group at Münster and CUHK-Shenzhen focuses on developing mathematical foundations for machine learning with applications to scientific computing problems. He has received significant research funding including an ERC Consolidator Grant, supporting his interdisciplinary work at the intersection of mathematics and artificial intelligence.

Jonas Schmittمشاهده پروفایل

مدرس

• Agricultural Economics
• Risk Management
• Crop Diversification
• +۲ مورد دیگر

Dr. Jonas Schmitt is a Lecturer at ETH Zurich's Department of Environmental Systems Science, specializing in Agricultural Economics and Policy. His office is located at SOL C 10.1, Sonneggstrasse 33, Zürich. Education PhD in Agricultural Economics, ETH Zurich (2021-2024) M.Sc. in Agricultural Economics, Georg August University of Göttingen (2016-2018) B.Sc. in Agricultural Economics, Georg August University of Göttingen (2012-2015) Research Focus Dr. Schmitt's research investigates climate risk management strategies in agriculture, focusing on: On-farm interventions like crop diversification Development of weather-indexed insurance products Economic impacts of extreme weather events Policy analysis of agricultural subsidy systems Statistical modeling of climate vulnerabilities Professional Experience Researcher, Thuenen Institut of Farm Economics (2020-2024) Policy Analyst, Federal Ministry of Agriculture, Berlin (2023) Data Analyst at Lavazza (2019-2020)

Axel Franzenمشاهده پروفایل

استاد

• Empirical Social Research Methods
• Game Theory
• Environmental Sociology
• +۴ مورد دیگر

Professor Axel Franzen serves as Professor of Empirical Social Research and Managing Director at the Institute of Sociology, University of Bern. His academic career spans appointments at the University of Aachen (Associate Professor) and University of Cologne (Full Professor) before returning to Bern in 2009 as Institute Director. He holds a PhD (1997) and Habilitation (2003) from the University of Bern following studies at Mannheim University and Indiana University. His research centers on empirical social research methods, game theory, environmental sociology, and social capital/networks . Franzen has published approximately 80 journal articles in outlets including Global Environmental Change , Experimental Economics , and Social Networks , alongside books on environmental behavior and social capital. Current teaching includes courses on structural equation modeling, survey research methods, and empirical research internships. Analysis of his recent publications reveals consistent focus on Swiss environmental behavior (electromobility, recycling), social capital dynamics (clubs, associations), and methodological innovations in experimental design. His work frequently employs longitudinal data and survey analysis, bridging sociological theory with practical policy applications in environmental governance and social organization. Franzen maintains active engagement with public discourse through numerous media appearances including SRF Tagesschau (migration integration), neue Energie magazine (renewable energy acceptance), and Kurier (game theory applications). His press coverage spans environmental governance ('Governing the Commons' farm outlets study), quiz show analytics, and climate change perceptions. As Institute Director, he oversees research operations at Bern's sociology hub while maintaining active supervision of empirical projects. His administrative leadership complements his scholarly contributions to understanding collective action, environmental decision-making, and social network effects in contemporary societies.

• Applied Analysis
• Numerical Analysis
• Data Science
• +۶ مورد دیگر

Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.

• MRI
• Brain Imaging (EEG, MRI)
• Neuroscience
• +۴ مورد دیگر

Franceschiello Benedetta is an Associate Professor at HES-SO Valais-Wallis School of Engineering, specializing in Technical and IT disciplines. She holds a PhD in Mathematical Neuroscience from Université Pierre et Marie Curie (Paris). Her work bridges applied mathematics, computational neuroscience, and neuroimaging, with a focus on visual perception modeling, MRI techniques, and neural dynamics. Teaching: Linear Algebra courses across multiple engineering bachelor programs Expertise: Combines mathematical modeling with neuroscientific applications to study optical illusions, brain connectivity, and ophthalmic diagnostics Key Affiliations: ISMRM, Organization for Human Brain Mapping (OHBM), Association for Research in Vision and Ophthalmology (ARVO) Research Interests: Computational modeling of visual cortex mechanisms underlying geometric optical illusions Development of MRI-based methods for eye structure segmentation and axial length estimation Analysis of brain network reliability through standardized MRI protocols Optimization techniques for medical imaging reconstruction (e.g., weighted LASSO problems) Recent Work Trends: Focus on integrating psychophysical experiments with computational models to elucidate perceptual mechanisms, emphasizing synergistic interactions between physical stimulus parameters. Active in advancing MRI applications for both clinical diagnostics and fundamental neuroscience research.

Michele Pellizzariمشاهده پروفایل

استاد

• Labour economics
• Economics of education
• Economics of networks
• +۴ مورد دیگر

Michele Pellizzari is a Full Professor at the Geneva School of Economics and Management (University of Geneva), with affiliations at CEPR (London), IZA (Bonn), and CREAM (UCL London). He previously served as Assistant Professor at Bocconi University and holds a PhD from the London School of Economics and a Doctorate from the University of Verona. Research Focus: Labour economics, Education economics, Network economics, Applied econometrics Scientific Contributions: Analysis of distance learning impacts, Social mobility correlations, Skill mismatch metrics, Wage compression in firms Key Collaborations: OECD, ILO, European Commission, Italian Ministry of Education

Michel C Milinkovitchمشاهده پروفایل

استاد

• Evolutionary Biology
• Developmental Biology
• Computational Biology
• +۵ مورد دیگر

Michel C Milinkovitch is a Full Professor in the Department of Genetics and Evolution at the University of Geneva, leading the Laboratory of Artificial & Natural Evolution (LANE) . His research merges biology, physics, and computational modeling to unravel the developmental and evolutionary mechanisms behind life's complexity, focusing on reptiles and mammals. Institution: University of Geneva Contact: Sc3 4024B | +41 22 379 33 38 Research Interests span evolutionary developmental biology, biomechanics of skin appendages, reaction-diffusion systems, mechanical instabilities in morphogenesis, and computational modeling. He investigates how physical constraints interact with genetic networks to generate patterns in scales, feathers, and pigmentation. Scientific Contributions include groundbreaking work on mechanical vs. chemical patterning in crocodile scales, snake scale organization via somitic cues, and the role of the sonic hedgehog pathway in avian feather development. His team employs advanced imaging, CRISPR-Cas9, and 3D simulations to bridge micro- and macro-scale biological phenomena. Collaborators & Alumni include Senior Lecturer Athanasia Tzika and researchers like Pierre-Yves Helleboid and Gabriel N. Santos-Durán. The lab maintains strong ties with interdisciplinary institutions and contributes to open-access protocols in reptilian genome assembly.

• Astrophysics
• Solar Physics
• Radiative Transfer
• +۳ مورد دیگر

Simone Riva is a Postdoctoral researcher affiliated with the Faculty of Informatics at the Università della Svizzera italiana . He is also associated with the Euler Institute and the Istituto ricerche solari Aldo e Cele Daccò (IRSOL) . His contact information includes simone.riva@usi.ch and offices located at East Campus, Sector D, Office D5.13 (Lugano) and Via Patocchi 57 (Locarno). His research focuses on astrophysics , particularly in the modeling of solar radiation transfer and polarization phenomena . Key areas include computational methods for solving radiative transfer equations, partial frequency redistribution effects, and high-performance computing techniques applied to stellar atmospheres. He has contributed to studies involving the Ca I 4227 Å line and the development of scalable numerical solvers for 3D transfer of polarized radiation. Recent work emphasizes the application of parallel preconditioned Krylov solvers and HPC techniques to address challenges in radiative transfer with angle-dependent partial redistribution. His research bridges computational physics with observational astrophysics, aiming to advance our understanding of solar magnetic fields and chromospheric dynamics. While no scientific awards are explicitly listed, his contributions to funded projects and collaborations with institutions like IRSOL underscore his active role in advancing solar and stellar physics research. He is affiliated with labs including the Euler Institute and IRSOL, which provide critical computational and observational infrastructure for his work.

Outi Supponenمشاهده پروفایل

استادیار

• Multiphase Fluid Dynamics
• Biomedical Engineering
• Material Science
• +۳ مورد دیگر

Outi Supponen is an Assistant Professor of Multiphase Fluid Dynamics at ETH Zurich's Department of Mechanical and Process Engineering, leading the Institute of Fluid Dynamics since 2020. She holds a MEng in Aeronautical Engineering from Imperial College London (2013) and a DSc in Mechanics from EPFL (2017). Prior to ETH, she was a Postdoctoral Fellow at the University of Colorado (2018–2019) and an Assistant Professor at McGill University (2019). Education: MEng, Aeronautical Engineering, Imperial College London (2013) DSc, Mechanics, Ecole Polytechnique Fédérale de Lausanne (2017) Her research focuses on experimental investigations of high-speed multiphase fluid phenomena, with applications in biomedical engineering , material science , and hydraulic machinery . Key areas include cavitation bubble dynamics, ultrasound-driven microbubble behavior, and fluid-structure interactions in medical and industrial contexts. Her work bridges fundamental fluid dynamics with practical applications, such as targeted drug delivery via microbubble jetting and kidney stone fragmentation mechanisms. She collaborates extensively with biomedical and engineering communities, leveraging advanced imaging techniques like X-ray phase-contrast and high-speed visualization. Grants & Advising: Supervises research on multiphase systems and collaborates on EU-funded projects on medical fluid dynamics. Labs/Teams: Leads the Fluid Dynamics Group at ETH, specializing in advanced experimental setups for high-speed fluid phenomena.

• Seismology
• Earthquake Engineering
• Ground Motion Prediction
• +۳ مورد دیگر

Dr. Jaleena Sunny is a Research Fellow at the Swiss Seismological Service (SED) based at ETH Zürich. Her work focuses on advancing ground motion prediction methodologies, induced seismicity analysis, and uncertainty quantification in seismic hazard assessment. She specializes in developing stochastic models for earthquake ground motion and validating their performance through novel metrics. Her research interests include seismology, earthquake engineering, and regional seismic risk analysis with applications to both natural and induced seismic events. Dr. Sunny's recent work emphasizes improving ground motion prediction equations for unconventional seismic sources like hydraulic fracturing-induced tremors and optimizing computational algorithms for model validation. Her publications (2018-2025) demonstrate contributions to stochastic ground motion modeling, induced seismicity characterization, and the development of metrics for comparing seismic models. Notable projects include extending the ERM-CH23 model for Swiss shallow induced seismicity and creating soil amplification maps for spectral acceleration periods. Awards: None explicitly mentioned Grants/Advising: No grants or advisees listed She is affiliated with the SED lab at ETH Zürich, contributing to Switzerland's earthquake monitoring infrastructure and hazard mitigation strategies.

Yasser Safaمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Multiphysics Modeling
• Solid Mechanics
• +۷ مورد دیگر

Dr. Yasser Safa is a Researcher at the Institute of Computational Physics (ICP) within the School of Engineering at Zurich University of Applied Sciences (ZHAW), leading the Multiphysics Modeling and Imaging research group. His work focuses on advanced computational techniques for solving complex engineering problems across energy systems, materials science, and mechanical design. His educational background includes a PhD from École polytechnique fédérale de Lausanne (EPFL) in 2005, where his thesis addressed numerical simulations of thermo-magneto-hydrodynamic phenomena in aluminum reduction cells. This foundation in coupled physics has driven his subsequent research in multiphysics modeling. Dr. Safa's research spans computational mechanics, solid mechanics (particularly nonlinear elasticity and buckling), corrosion engineering, additive manufacturing, metamaterials, and energy systems including fuel cells and wind power. His methodology emphasizes developing validated numerical models for real-world engineering challenges, often involving thin-film mechanics, material degradation, and structural integrity under extreme conditions. Recent work demonstrates increasing focus on renewable energy systems and advanced manufacturing processes. Analysis of his 15 most recent publications reveals strong thematic continuity in computational mechanics applied to energy and materials, with growing emphasis on wind energy systems and corrosion phenomena. His work consistently bridges theoretical mechanics with industrial applications, particularly in micro-fabrication and energy conversion technologies. He has secured and led multiple research projects including ongoing work on corrosion of multiphasic alloys and completed projects on airborne wind power systems, metamaterial wave guides, and additive manufacturing. These projects demonstrate his ability to translate computational expertise into practical engineering solutions across diverse domains. As part of ZHAW's ICP research group, Dr. Safa contributes to developing advanced computational tools for imaging and modeling physical systems, maintaining strong industry connections particularly in energy technology sectors. His current work continues to address critical challenges in material durability and energy system design through sophisticated numerical approaches.

Jürgen Schumacherمشاهده پروفایل

استاد

• Electrochemical energy systems
• Redox flow batteries
• Proton exchange membrane fuel cells (PEMFC)
• +۳ مورد دیگر

Prof. Dr. Jürgen Schumacher is Professor at the ZHAW School of Engineering and heads the research focus Electrochemical Cells & Energy Systems . Stationed in Winterthur, Switzerland, he spearheads numerous national and European projects aimed at advancing electrochemical energy conversion and storage technologies. Research Interests His work integrates multiscale modeling with experimental validation to address critical challenges in: Redox flow batteries – organic and hydrogen–bromine chemistries, membrane optimization, and system-level performance models. Proton exchange membrane fuel cells (PEMFC) – two-phase transport, water management, durability under heavy-duty cycles, and degradation coupling. Photoelectrochemical devices – band-structure engineering, optical and carrier-transport modeling for solar water splitting and dye-sensitized solar cells. Porous electrode theory – upscaling from pore-scale to macroscopic descriptions, Monte-Carlo and continuum approaches. Publication Trends Since 2014, his peer-reviewed output has concentrated on physics-based modeling frameworks that bridge electrochemical kinetics, transport phenomena, and material microstructure. Journal of Power Sources and Electrochimica Acta host the majority of his recent articles, reflecting a clear focus on flow batteries and PEMFC durability . A noticeable trend is the coupling of performance and degradation models , enabling predictive lifetime assessment. Ongoing Projects High-throughput screening & synthesis of active materials for flow batteries – Project leader. Robust PEMFC MEAs for heavy-duty applications – Project leader. Doctoral network on micro-process engineering for electrosynthesis – Project leader. Labs & Teams At ZHAW, Prof. Schumacher directs an interdisciplinary team combining electrochemical engineers , numerical modelers , and material scientists . The group operates state-of-the-art facilities for in-situ diagnostics , micro-computed tomography , and high-performance computing clusters dedicated to large-scale simulations of complete cells and stacks.

• Palliative Care
• Geriatrics
• Patient-Centred Outcome Measures
• +۵ مورد دیگر

Christina Ramsenthaler is a Professor of Research Methods and Statistics at the ZHAW School of Health Sciences , where she leads the (e)SENIORS projects. Her research focuses on patient-centred outcome measures (PCOMs) across palliative care, geriatrics, and community care, emphasizing their role in fostering patient-centred care for professionals and informal caregivers. Education: PhD in Palliative Care (King's College London, 2017) MSc in Palliative Care (King's College London, 2012) Diploma in Psychology (University of Bremen, 2009) She develops PCOMs for use in dementia care, acute care, and population-level healthcare monitoring, integrating psychometric methods (e.g., Rasch theory) and big data analysis. Her work addresses social inequality impacts on healthcare systems through quantitative frameworks and hybrid implementation trials. Recent publications center on IPOS-Dem validation, co-production in dementia research, and meta-analytic evaluations of palliative care service models. Network Memberships: German Guideline Program in Oncology Wolfson Palliative Care Research Centre POS and IPOS groups (Cicely Saunders Institute) European Association for Palliative Care

• 3D Computer Vision
• Geometric Deep Learning
• Topological Deep Learning
• +۱۰ مورد دیگر

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

Pierre Beckمشاهده پروفایل

پژوهشگر

• Nonlinear Dynamics
• Machine Learning in Physics
• Computational Modeling
• +۳ مورد دیگر

Pierre Beck is a Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering’s Institute of Mechanical Engineering (IGM) and the Emergent Complexity in Physical Systems (ECPS) group. He is also a PhD student in Mechanics at EPFL, currently pursuing his doctoral degree since 2022. BA (Hons) Mathematics, University of Cambridge (2016–2019) MMath, University of Cambridge (2019–2020) PhD Mechanics, EPFL (2022–present) Beck’s research focuses on leveraging data-driven methods to analyze and predict nonlinear dynamical systems, particularly chaotic attractors and periodic orbits. His work bridges machine learning with theoretical physics, aiming to uncover hidden structures in complex physical phenomena through computational modeling. The articles demonstrate expertise in applying deep learning to chaotic systems, topological analysis of attractors, and DSGE economic models. Key interdisciplinary themes include algorithmic innovation, pattern recognition, and mathematical physics, with a strong emphasis on numerical methods and non-existing solutions in dynamical systems. Beck is based in the ECPS group at EPFL, where he contributes to advancing understanding of emergent complexity in physical systems. His office is located in MED 2 2924, and he can be contacted at pierre.beck@epfl.ch.