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Rima Alaifari is currently an Assistant Professor for Applied Mathematics at ETH Zürich , where she works on applied analysis, inverse problems, and scientific machine learning. Her research emphasizes stability analysis and regularization of inverse problems, applied harmonic analysis, phase retrieval, and operator learning. She is an associated member of the ETH AI Center and will transition to a full professorship at RWTH Aachen University in 2025 as Chair of Analysis and its Applications. Education : PhD in Mathematics (2010–2014, Vrije Universiteit Brussel); MSc in Applied and Industrial Mathematics (2005–2010, Johannes Kepler University) Research Focus : Stability estimates for inverse problems, phase retrieval in wavelet/Gabor transforms, operator learning with neural networks, and deep learning robustness. Article Trends : Her recent work bridges harmonic analysis with machine learning, focusing on phase retrieval stability, adversarial perturbations in imaging, and mathematically grounded neural operator frameworks like ReNO and CNO. Advising : She has supervised PhD students like Tandri Gauksson and Matthias Wellershoff. Former postdoctoral researchers include Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner).

• Applied Analysis
• Inverse Problems
• Scientific Machine Learning
• +7 more

Francesca Da Lio is a Professor at the Department of Mathematics, ETH Zurich, where she has held a titular professorship since 2014. Her research focuses on nonlinear elliptic and parabolic partial differential equations (PDEs), with applications in stochastic and deterministic optimal control, homogenization, front propagation, and geometric analysis. She has pioneered work on conformally invariant variational problems and nonlocal PDEs, including fractional harmonic maps and stability analysis for critical points. PhD in Mathematics (1998) and Summa Cum Laude Degree in Mathematics (1994) from University of Padova. Her research explores the interplay between nonlinearity and non-locality, particularly in problems arising from geometry, mathematical finance, and physics. She has led major Swiss National Fund (SNF) projects, including grants for geometric analysis and conformally invariant variational theory. Her work on 3-commutators, integrability by compensation, and Morse index stability has advanced the understanding of harmonic maps and elliptic systems. Francesca Da Lio has mentored numerous PhD, postdoctoral, and Master/Bachelor students, including Dominik Schlagenhauf, Jerome Wettstein, and Ali Hyder. She has served on hiring committees for full professorships at ETH Zurich and co-organized international conferences such as 'Recent Advances in Nonlocal and Nonlinear Analysis' and 'Topics in Sub-Elliptic PDEs.' Scientific Awards: Italian Scientific Qualification as Full Professor in Mathematical Analysis (2013). She contributes to editorial boards, including Advances in Calculus of Variations , and participates in academic services like refereeing for SNF projects and international journals.

• Partial Differential Equations
• Mathematical Analysis
• Geometric Analysis
• +2 more

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +5 more

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.

• Dynamical systems and gradient flows
• Analysis of partial differential equations
• Stochastic analysis
• +2 more

Georgios Moschidis is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Chair of Mathematical General Relativity (CMGR) and the Mathematics Section of the School of Basic Sciences (SB-SMA). He holds dual positions within the Department of Mathematics (MATH) and the SMA-ENS unit, focusing on advanced mathematical physics and geometric analysis. His research interests center on mathematical general relativity, differential geometry, and partial differential equations, with a strong emphasis on spacetime stability problems, trapped surface formation, and geometric analysis in curved spacetimes. He teaches advanced courses such as Differential Geometry IV (General Relativity) and Analysis IV, reflecting his expertise in theoretical physics and advanced mathematics. Moschidis' work includes groundbreaking studies on the instability of anti-de Sitter (AdS) spacetime, ergosphere instabilities, and scalar wave dynamics in black hole geometries. His publications span topics from Vlasov systems to geometric inequalities in Gauss spaces, demonstrating interdisciplinary rigor. He advises doctoral student Abraham Gabriel Dorsaz and maintains active research through the CMGR lab (https://www.epfl.ch/labs/cmgr/). His research is supported by EPFL's academic framework, and he contributes to both teaching and doctoral supervision within the School of Basic Sciences.

• General Relativity
• Differential Geometry
• Partial Differential Equations
• +3 more

Xue-Mei Li is a Professor of Mathematics at Imperial College London and École Polytechnique Fédérale de Lausanne (EPFL). She holds chairs in Probability and Stochastic Analysis at both institutions. Her research focuses on stochastic analysis, geometric stochastic processes, and multi-scale systems, with contributions to areas like Malliavin calculus, fractional dynamics, and coarse curvature. Li has held positions at the University of Warwick, University of Connecticut, and others, supported by fellowships from the Alexander von Humboldt Foundation, Royal Society, and MSRI. Her work addresses fundamental questions in stochastic differential equations, geometric analysis, and their applications to complex systems. Education and Career: PhD in Mathematics, University of Warwick EPSRC Research Associate Faculty positions at the University of Connecticut (tenured Associate Professor) Research Interests: Her research spans stochastic differential equations (SDEs), stochastic partial differential equations (SPDEs), geometric stochastic analysis, and fractional dynamics. Notable contributions include the BEL formula, strict local martingales, and solutions to longstanding problems in strong completeness on non-compact manifolds. She explores interactions between stochastic processes and geometric structures, including coarse Ricci curvature and homogenization theory. Awards and Grants: Supported by NSF, EPSRC/UKRI, and Swiss NSF grants Awarded fellowships from Alexander von Humboldt Foundation, Royal Society, and MSRI Advising and Teams: PhD students: Johann Gehringer, Rhys Steel, Julian Sieber, and others Leading working groups on stochastic analysis and geometric dynamics

• Stochastic Analysis
• Geometric Stochastic Analysis
• Malliavin Calculus
• +5 more

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.

• Applied Analysis
• Numerical Analysis
• Data Science
• +6 more

Tommaso Redaelli is a researcher at ETH Zürich, affiliated with the Professorship for Groundwater and Hydromechanics. His work integrates fluid mechanics with biological and active-matter systems, covering phenomena from biofilm deformation to plankton sensing and micro-swimmer propulsion. Research Interests Low-Reynolds-number hydrodynamics and micro-swimmers Biofilm rheology and mechanical response to flow Hydrodynamic signalling in planktonic organisms Stochastic models of turbulence and scalar transport Across his recent publications one observes a trajectory from theoretical turbulence studies toward biologically inspired fluid problems, combining analytical modeling with experimental relevance, notably in understanding how small organisms exploit fluid-borne signals. Contact Email: tredaelli@ethz.ch Postal: ETH Zürich, HIF D 22.2, Laura-Hezner-Weg 7, 8093 Zürich, Switzerland

• Fluid Mechanics
• Hydrodynamics
• Micro-swimmers
• +5 more

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +4 more

Robert Dalang is a Full Professor and Chair of Probabilities at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. He is affiliated with the School of Basic Sciences, Department of Mathematics, specifically the Probability Section. His office is located in building MA, room B2 493 at EPFL's Lausanne campus. He also serves as a Full Professor in the Section of Mathematics (SMA) and for the Doctoral Program in Mathematics (EDMA). Professor Dalang's research focuses on probability theory and stochastic analysis. His work primarily centers on: Stochastic partial differential equations (SPDEs), particularly stochastic heat and wave equations Brownian motion and Brownian sheet Lévy processes and Lévy white noise Hitting probabilities and polarity Fractal properties of random fields Stochastic optimization problems His recent publications demonstrate a continued focus on the theoretical properties of solutions to stochastic partial differential equations, including their regularity, hitting probabilities, and asymptotic behavior. He has made significant contributions to understanding the fine properties of solutions to stochastic heat and wave equations, particularly in critical dimensions. Professor Dalang has served on the elected committee for the Pension fund PUBLICA since Fall 2007, being reelected for 2009-2012. His work on this committee has focused on preserving employee interests during the transition from a defined benefits system to a defined contributions system. He has supervised numerous PhD students, including: Candil David Jean-Michel Chen Le Ciccarella Carlo Conus Daniel Dumas Frédéric Humeau Thomas Marie Jean-Baptiste Pu Fei Rossel Jean-Benoît Vinckenbosch Laura Professor Dalang teaches courses including Analysis III, Analysis IV, and Theory of Stochastic Calculus at EPFL.

• Probability Theory
• Stochastic Processes
• Stochastic Partial Differential Equations
• +3 more

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.

• Optimization
• Machine Learning
• Data Science
• +3 more

Lénaïc Chizat is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) within the School of Basic Sciences and Institute of Mathematics. He chairs the Dynamics of Learning Algorithms (DOLA) laboratory and teaches advanced courses in machine learning and computational optimal transport, focusing on mathematical analysis of neural networks and measure transportation theory. His research centers on optimal transport theory and its applications to deep learning, with emphasis on Wasserstein geometry, entropic regularization, and gradient flow dynamics. He investigates implicit regularization in neural networks, convergence properties of learning algorithms, and the infinite-width limits of deep architectures. His work bridges theoretical mathematics with practical machine learning challenges, particularly in computational aspects of modern supervised learning. Analysis of his 15 most recent publications (2023-2025) reveals a strong thematic focus on entropic optimal transport, where he has made fundamental contributions to Sinkhorn algorithm convergence in continuous settings and Wasserstein barycenter computation. His research consistently explores the mathematical foundations of deep learning, especially training dynamics, min-max optimization, and the role of initialization in neural network scaling. Chizat currently advises PhD student Wang Guillaume Yitian and leads the DOLA laboratory, which develops theoretical frameworks for understanding learning algorithm dynamics through the lens of optimal transport and measure-valued optimization.

• Optimal Transport
• Machine Learning
• Deep Learning
• +3 more

Florian Schweiger is a Postdoctoral Scholar in the Department of Mathematics at the University of Geneva, hosted by Professor Hugo Duminil-Copin. Previously, he was a postdoc at the Weizmann Institute of Science under Ofer Zeitouni. His research centers on probability theory and analysis, with a primary emphasis on stochastic models for interfaces. His educational background includes: BSc in Mathematics from the University of Bonn (2011-2014) MSc in Mathematics from the University of Toronto (2014-2015) MSc in Mathematics from the University of Bonn (2015-2016) PhD in Mathematics from the University of Bonn (2016-2020), supervised by Stefan Müller Dr. Schweiger's research interests encompass probability theory and mathematical analysis. He investigates stochastic interface models, log-correlated fields and their extremes, correlated percolation models, and regularity theory for discrete elliptic operators. His work often addresses the behavior of random surfaces and fields in various regimes. His recent publications demonstrate a strong focus on interface models and log-correlated Gaussian fields. Key contributions include the study of the maximum of log-correlated fields in random environments, pinning phenomena in membrane models, and finite range decompositions of Gaussian fields. These works highlight the interplay between probability and analysis in understanding complex random systems. He is an active member of Hugo Duminil-Copin's research group at the University of Geneva, contributing to the group's efforts in probability theory and statistical mechanics.

• Probability Theory
• Mathematical Analysis
• Stochastic Processes
• +4 more

Pawel Duch serves as a Lecturer and Scientist at École Polytechnique Fédérale de Lausanne (EPFL) within the School of Basic Sciences, Department of Mathematics. He is affiliated with the Chair of Probability and Partial Differential Equations (PROPDE) and the SMA-Teaching unit, maintaining his office at MA B2 487. Previously, he held postdoctoral positions at Adam Mickiewicz University in Poznań and the Max Planck Institute for Mathematics in the Sciences in Leipzig. He earned his PhD in 2017 from Jagiellonian University in Kraków with research on massless fields and the adiabatic limit in quantum field theory. Dr. Duch's research centers on the intersection of probability theory, partial differential equations, and Euclidean quantum field theory. He specializes in singular stochastic partial differential equations and renormalization techniques, with significant contributions to the flow equation approach for constructing quantum field theory models. His work bridges mathematical rigor with theoretical physics challenges, particularly in stochastic quantization and model construction. Recent publications reveal a strong trend toward applying stochastic analysis to quantum field theory, exemplified by constructions of the fractional Φ^4_3 model and Gross-Neveu model using flow equations. His research consistently addresses renormalization challenges in singular SPDEs across subcritical regimes. Scientific Awards: None. Supported by the Simons Foundation, Dr. Duch actively contributes to the PROPDE research group under Martin Hairer. He teaches mathematics courses at EPFL and has organized academic events including the 47th LQP workshop on Foundations and Constructive Aspects of QFT. His mentoring extends through lecture courses and summer school instruction. He is integral to the PROPDE group's mission, advancing mathematical methods for stochastic PDEs and quantum field theory through collaborative research and workshop organization.

• Probability Theory
• Partial Differential Equations
• Quantum Field Theory
• +4 more

Lyudmila Grigoryeva is a Professor at the University of St. Gallen. Her research focuses on reservoir computing, machine learning, and nonlinear dynamical systems. She has contributed to advancing theoretical frameworks for recurrent neural networks, including memory capacity analysis, approximation bounds, and time-series forecasting. Key collaborations include work with Juan-Pablo Ortega Lahuerta and Lukas Gonon on reservoir systems and stochastic processes. Research Themes: Infinite-dimensional reservoirs, geometric invariant learning, and parallel-in-time solvers. Applications: Macroeconomic forecasting, chaotic attractor learning, and PDE solving. Her recent work addresses foundational questions in reservoir computing, such as memory capacity evaluation and universal approximation properties. She has also explored interdisciplinary applications in econometrics and computational neuroscience.

• Reservoir Computing
• Machine Learning
• Neural Networks
• +4 more