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Mikaela Iacobelli is an Associate Professor in the Department of Mathematics at ETH Zürich. During the 2024-25 academic year, she was a von Neumann Fellow at the Institute for Advanced Study in Princeton. Previously, she held faculty positions at Durham University and a research fellowship at the University of Cambridge. Her educational background includes: PhD in Mathematics from Sapienza University of Rome and École Polytechnique in Paris (2015) Master's degree from Sapienza University of Rome (2012) Bachelor's degree from Sapienza University of Rome (2009) Mikaela's research lies at the interface of analysis, kinetic theory, and statistical mechanics. She studies partial differential equations that model the collective behavior of many-particle systems, with a focus on Vlasov-type plasmas and gravitational dynamics. Her current projects range from quasineutral and singular-limit problems for Vlasov-type systems to quantization of measures, ultrafast diffusion, and gradient-flow structures that link microscopic particle models to macroscopic fluid descriptions. She makes extensive use of PDEs techniques, optimal transport, probability, calculus of variations, and Riemannian geometry in her work. Her recent publications demonstrate a strong focus on Vlasov-type equations, particularly examining quasineutral limits, stability properties, and connections to other physical systems like Euler equations and magnetohydrodynamics. She has made significant contributions to understanding Landau damping, quantization problems on manifolds, and the mathematical foundations of plasma physics. Her notable scientific awards include: SNSF Starting Grant (Swiss ERC) Challenges and Breakthroughs in the Mathematics of Plasmas (2025-2030) von Neumann Fellow at the Institute for Advanced Study, Princeton (2024-2025) Invited speaker at the International Congress of Mathematical Physics (2021) CO-PI of the Germaine de Staël Funding Program for French-Swiss cooperation (2021-2023) L'Oréal prize for Women in Science (2015) Mikaela actively mentors postdocs, PhD, Master's, and Bachelor's students. Her current mentees include postdocs Dennis Chemnitz, Rishabh Gvalani, Stefano Rossi, and Simon Becker, as well as PhD students Thérèse Moerschell, Ata Deniz Aydin, and Antoine Gagnebin. She has served as PI for the Starting Research Grant from the University of Rome Sapienza and is currently the PI for the SNSF Starting Grant. She co-organizes several academic seminars including the Zurich Colloquium in Mathematics, the PDE and Mathematical Physics seminar at ETH Zürich and UZH, and the Analysis Seminar. She also serves on various committees including as Chair of the European Mathematical Society Committee for Women in Mathematics.

• Analysis
• Kinetic theory
• Statistical mechanics
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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
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Michele Dolce is a Lecturer and Scientist at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences (SB) and the Chair of Mathematical Analysis, Calculus of Variations and PDEs (AMCV). He previously held positions as a Postdoc at EPFL and a Research Associate at Imperial College London, where he worked under Prof. Michele Coti Zelati. His academic journey includes a PhD from the Gran Sasso Science Institute. Current affiliation: EPFL, School of Basic Sciences (SB), Department of Mathematics (MATH), AMCV Past affiliation: Imperial College London His research focuses on the mathematical analysis of Partial Differential Equations (PDEs) in fluid dynamics and kinetic theory. Key areas include hydrodynamic stability, long-time behavior of viscous vortex systems, and time-decay properties of kinetic models like the Boltzmann and Wave Kinetic Equations. Recent work explores vortex merging phenomena and Taylor dispersion in rotationally symmetric flows. Scientific activities include organizing workshops such as "Long time dynamics in random and deterministic systems" (2025) and co-organizing events like the "Deterministic and random features of fluids" summer school (2023) and the "Enjoying Probability and Fluids in Lausanne" workshop (2023). His publications span journals including Communications in Mathematical Physics, Archive for Rational Mechanics and Analysis, and Journal of Mathematical Fluid Mechanics. Supported by Swiss National Science Foundation (SNF Ambizione grant PZ00P2_223294) Partially funded by GNAMPA (INdAM group)

• Partial Differential Equations
• Fluid Dynamics
• Kinetic Theory
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Boris Buffoni is a Senior Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences, Institute of Mathematics, specifically within the Chair of Partial Differential Equations. He maintains his office at MA C2 605 (MA Building), Station 8, 1015 Lausanne, Switzerland, and can be contacted at boris.buffoni@epfl.ch or +41 21 693 49 87. His academic role spans both teaching responsibilities across multiple mathematics programs and active research in theoretical and applied mathematics. Dr. Buffoni's research program centers on the calculus of variations applied to Lagrangian and Hamiltonian systems, with significant contributions to optimal transportation in Lagrangian dynamics and hydrodynamics. His work explores semi-global minimization methods for quasi-linear elliptic variational problems and the variational approach to capillary-gravity water waves and their energetic stability. Additional research foci include local bifurcation and center-manifold theory for elliptic PDEs, the configurations of infinite elastic cylinders under compression or traction, and the analytic theory of global bifurcation with applications to gravity waves and their secondary bifurcations. The trajectory of his recent publications reveals a deepening focus on three-dimensional water wave phenomena, particularly steady rotational flows, gravity-capillary solitary waves, and advanced mathematical techniques for analyzing these complex systems. His 2025 publications demonstrate continued innovation in applying Kato's approach to locally coercive problems and developing the theoretical foundations of global bifurcation. The consistent application of variational methods and bifurcation theory across his work represents a unifying theme in addressing challenging problems in fluid dynamics and nonlinear partial differential equations. Dr. Buffoni has received research support including an EPSRC grant (GR/L41059) for work on 'Multibump localised solutions for spatially homogeneous partial differential equations,' reflecting the significance of his contributions to the field. His teaching portfolio at EPFL includes foundational courses such as Analysis II, Functional Analysis I, and Partial Differential Equations of Evolution, where he imparts knowledge of differential and integral calculus of real functions of several variables, linear functional analysis, and fundamental techniques for solving evolution equations.

• Calculus of Variations
• Lagrangian and Hamiltonian systems
• Optimal transportation
• +7 more

Ana Cannas da Silva is a Lecturer in the Department of Mathematics at ETH Zurich (Switzerland). She specializes in Symplectic Geometry , Geometric Topology , and Geometric Analysis . Her academic work includes research on symplectic toric manifolds, folded symplectic structures, and geometric quantization, with notable publications in journals like Pure and Applied Mathematics Quarterly and Mathematical Research Letters . Research Interests Symplectic Geometry Geometric Topology Geometric Analysis Hamiltonian Group Actions Toric Manifolds Recent Academic Activities Co-organized Symplectic Geometry Seminar (2021-2023) Supervised student theses on topics like contact toric manifolds, Hamiltonian actions, and symplectic linear algebra Authored research on Dedekind sums via Atiyah-Bott-Lefschetz theory (2023) and symplectic origami (2011) Teaching Lecturer for Mathematics I (2024), covering differential calculus and linear algebra Lecturer for Mathematics II (2024), focusing on multivariable calculus and partial differential equations Co-taught seminars on symplectic/contact geometry with Bahar Acu Academic Contributions Advised 20+ MSc/BSc theses at ETH Zurich since 2012 Co-organized conferences like D-Days (2013) and LP-60 (2023) Authored outreach book: Step by Step Symmetry (2016)

• Symplectic Geometry
• Geometric Topology
• Geometric Analysis
• +3 more

Full Professor in Analysis at the Department of Mathematics and Computer Science of the University of Basel. Born in Lecco, Italy in 1981, Dr. Crippa received his PhD in Mathematics in 2008 from the Scuola Normale Superiore di Pisa and the University of Zurich. His office is located at Spiegelgasse 1, 5th floor, Basel, Switzerland. Dr. Crippa's primary research focuses on mathematical analysis of fluid dynamics, with particular expertise in partial differential equations, geometric measure theory, and transport phenomena. His work bridges theoretical mathematics with applications in fluid mechanics and turbulence theory. He leads multiple significant research projects including an ERC Starting Grant and several national and international collaborations. His recent publications explore the interplay between transport, dissipation, and mixing in incompressible flows, contributing to fundamental understanding of fluid behavior. Dr. Crippa's research demonstrates strong connections between pure mathematical analysis and physical fluid phenomena, with implications for both theoretical understanding and practical applications. Notable Awards and Grants: ERC Starting Grant FLIRT (Fluid Flows and Irregular Transport) SNF Project FLUTURA (Fluids, Turbulence, Advection) DFG SPP 2410 CoScaRa (Hyperbolic Balance Laws in Fluid Mechanics) DFG-SNF Project with Emil Wiedemann on inhomogeneous and compressible fluids Dr. Crippa actively supervises research and is currently recruiting for PostDoc positions starting in Spring or Fall 2025. He encourages excellent candidates to apply for Swiss Government Excellence Scholarships under his supervision. His research group participates in multiple collaborative projects between Swiss and German research institutions. He is involved in several academic seminars including the Analysis Seminar, BZ Seminar in Analysis (Basel, Zurich), and Perlen Kolloquium, contributing to the vibrant mathematical community at the University of Basel and beyond.

• Partial Differential Equations
• Geometric Measure Theory
• Transport and Flows with Irregular Velocity Fields
• +5 more

Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .

• Causality
• Causal Representation Learning
• Causal Discovery
• +4 more

Ralf Hiptmair is a Full Professor at ETH Zürich, serving as Head of the Seminar for Applied Mathematics and Deputy Head of the Department of Mathematics. He also holds the position of Director of Studies for ETH BSc and MSc in Computational Sciences and Engineering (CSE). His research spans computational mathematics, numerical analysis, finite element methods, boundary element methods, computational electromagnetism, multigrid methods, discrete differential forms, shape optimization, wave propagation, and kinetic equations. Hiptmair's work on auxiliary space methods was recognized as a breakthrough in computational science in the 2008 DOE Report on recent significant advancements in computational science. His research focuses on developing and analyzing numerical methods for partial differential equations, with particular emphasis on structure-preserving discretizations, computational electromagnetism, and boundary integral equations. His work has significant applications in engineering, physics, and computational science. Hiptmair's publications demonstrate a strong focus on advancing numerical techniques for electromagnetic problems, wave propagation, and shape optimization. His recent work shows increasing interest in computational topology, geometric numerical integration, and interdisciplinary applications of numerical methods. Featured as breakthrough in computational science in the 2008 DOE Report on recent significant advancements in computational science (for Auxiliary space methods) Hiptmair has supervised numerous doctoral, master's, and bachelor's students across mathematics, computational science and engineering, and related fields. His research group has received funding for developing advanced numerical methods with applications in electromagnetism, fluid dynamics, and computational physics. He is actively involved in teaching numerical methods courses at both undergraduate and graduate levels. Hiptmair leads research efforts in the Seminar for Applied Mathematics, collaborating with industry partners like ABB Corporate Research and Siemens on practical applications of computational methods. His work bridges theoretical numerical analysis with real-world engineering challenges.

• Computational Mathematics
• Numerical Analysis
• Finite Element Methods
• +7 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

Prof. Dr. Marcus J. Grote is the Head of the Department of Mathematics and Computer Science at the University of Basel, affiliated with the Faculty of Science. His work spans numerical analysis, computational mathematics, and applications to parametric PDEs and uncertainty quantification. His research focuses on advanced numerical methods, including generalized polynomial chaos expansions and least squares techniques, with applications in computational uncertainty quantification for elliptic/parabolic PDEs. He contributes to academic leadership as department head. Contact: marcus.grote@unibas.ch , +41 61 207 39 96.

• Numerical Analysis
• Partial Differential Equations
• Computational Mathematics
• +3 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