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Benoît Mahault serves as a Group Leader and Researcher at the Max Planck Institute for Dynamics and Self-Organization (Göttingen, Germany) within the Department of Living Matter Physics, where he directs the Motile active matter research group. His work bridges theoretical physics and biological complexity through nonequilibrium statistical mechanics. His academic background includes a Ph.D. from Université Paris-Saclay (2018) under Hugues Chaté, followed by a postdoctoral position at the University of Tokyo in Prof. Masaki Sano's group. He joined the Max Planck Institute in 2019 as a postdoc and was promoted to Group Leader in 2021. Dr. Mahault's research centers on emergent self-organization in active matter systems , with focus areas including: Transition mechanisms to collective motion Bose-Einstein-like condensation via motility inhibition Topological defect dynamics in active nematics Navigation strategies for microswimmers in complex environments His theoretical framework reveals universal principles governing both synthetic and biological active systems. Analysis of his 15 most recent publications (2022–2025) shows a cohesive trajectory exploring nonreciprocal interactions , quorum sensing , and energy-accuracy tradeoffs in active matter. Key themes include phase separation in driven mixtures, defect-mediated pattern formation, and hydrodynamic optimization of microswimmer locomotion—demonstrating consistent innovation at the physics-biology interface. The Motile active matter group employs advanced theoretical modeling to dissect self-organization principles, contributing foundational insights through collaborations with experimental teams at the Max Planck Institute. Current projects investigate non-equilibrium steady states in confined active systems and topological constraints in collective navigation.

• Nonequilibrium Statistical Mechanics
• Active Matter
• Living Matter Physics
• +5 more

Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.

• Artificial Intelligence in Healthcare
• Biomedical Engineering
• Traditional Chinese Medicine Informatics
• +3 more

Giorgio Ferrari is a Full Professor for Mathematical Finance at the Institute for Mathematical Economics (IMW), Faculty of Economics, Bielefeld University. His research bridges stochastic control theory with applications in economics, finance, actuarial science, and epidemiology. Education: B.Sc. and M.Sc. in Physics and Mathematical Physics from the University of Rome La Sapienza, Ph.D. in Mathematics for Economic-Financial Applications (2012). Academic Appointments: Post-Doctoral Researcher (2012–2015), Substitute Full Professor (2015), Junior Professor (W1) (2016–2017), Associate Professor (2017–2023), and Full Professor (2023–present) at Bielefeld University. Research Interests focus on Singular Stochastic Control , Optimal Stopping , and Stochastic Games , with applications to economic policy, financial markets, and epidemic modeling. His work extends to Mean-Field Games for large-scale strategic interactions and Free-Boundary Problems for investment decision-making under uncertainty. Scientific Contributions include groundbreaking publications in Stochastic Processes and their Applications , Mathematical Finance , and SIAM Journal on Control and Optimization . His research projects, such as the DFG SFB 1283 subproject C4 and the Research Training Group 2865 , address uncertainty in dynamic economies through game-theoretic and stochastic frameworks. Notable Awards: AMASES Best Young Researcher Paper (2014), YITP Research Prize (2017), and multiple research fellowships from the University of Padova. Leadership: Director of the Bielefeld Graduate School in Theoretical Sciences (2023–present) and Principal Investigator in major DFG-funded initiatives.

• Singular Stochastic Optimal Control
• Optimal Stopping and Free-Boundary Problems
• Stochastic Games
• +6 more

Prof. Dr. Lubomir Banas is a full-time Professor at the Faculty of Mathematics , University of Bielefeld. His research focuses on numerical analysis of stochastic partial differential equations (SPDEs) , particularly in micromagnetism, phase field models, and stochastic games. He leads Subproject B03 in the SFB 1283 project 'Taming Uncertainty and Profiting from Randomness and Low Regularity in Analysis, Stochastics and Their Applications.' Research Interests: Numerical methods for SPDEs and singular-degenerate PDEs Adaptive finite element techniques and a posteriori estimates Phase field models (Cahn-Hilliard, obstacle potentials) Stochastic games with asymmetric information Computational micromagnetism and magnetostriction Self-organized criticality and nonlinear stochastic flows Recent work includes: 2025: Numerical approximation of biharmonic wave maps and stochastic games 2024: Sharp interface limits for stochastic Cahn-Hilliard equations 2023: Singular-degenerate SPDEs and a posteriori estimates 2022: Stochastic total variation flow and Hamilton-Jacobi-Bellman equations 2021: Nematic electrolytes and homogenization of two-phase flows He serves on examination boards for Bachelor's and Master's programs in Mathematics and Mathematical Physics, and supervises graduate students within the Bielefeld Graduate School in Theoretical Sciences . His publications (over 30) address convergence analysis, error estimation, and computational modeling in applied mathematics.

• Numerical Analysis
• Stochastic Partial Differential Equations
• Computational Physics
• +12 more

Tony Stillfjord is an Associate Professor at the Centre for Mathematical Sciences, Lund University, Sweden. He previously held postdoctoral positions at the Max Planck Institute for Dynamics of Complex Technical Systems and Chalmers/University of Gothenburg. Ph.D. and MSc in Numerical Analysis from Lund University Funded by WASP (Wallenberg AI, Autonomous Systems and Software Program) Research Interests : Numerical methods for partial differential equations, splitting schemes, stochastic optimization, and large-scale differential Riccati equations. His work focuses on developing low-rank approximations and robust optimization algorithms. Recent Publications highlight advancements in Lie/Strang splitting for operator-valued Riccati equations, stochastic descent methods, and GPU-accelerated splitting schemes. Software Contributions : Developed DREsplit (MATLAB package for differential Riccati equations) and BST20_CODE for stochastic optimization experiments. Contact : Office at MH:562E, Lund University. Email: tony.stillfjord@math.lth.se . URL: tonystillfjord.net

• Numerical Analysis
• Partial Differential Equations
• Stochastic Optimization
• +4 more

Paul GASSIAT is a Lecturer at CEREMADE (Paris Dauphine University-PSL) and DMA (École Normale Supérieure, Paris). His research focuses on stochastic analysis, particularly rough path theory, stochastic PDEs, and applications to financial mathematics. 2011 PhD in Mathematics, supervised by Huyên Pham 2019 Habilitation (Authorization to supervise research) Research interests include: Pathwise stochastic analysis and regularization by noise Stochastic Hamilton-Jacobi equations Rough volatility modeling Applications to financial mathematics and machine learning optimization Scientific roles: JUNIOR MEMBER OF THE UNIVERSITY INSTITUTE OF FRANCE (2024-2029) Co-head of the MASEF Master program (2023/2024) Member of the editorial board of EJP/ECP (2020-) Member of the RT Matrisk office (2024-)

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +5 more

Dr. Johannes Storn is affiliated with the Faculty of Mathematics at Universität Bielefeld. His research focuses on numerical analysis, partial differential equations, and stochastic processes, with a particular emphasis on stochastic non-Newtonian fluids and their regularity and numerical solutions. He is involved in the Collaborative Research Center (SFB) 1283 project, specifically the sub-project B7 addressing 'Stochastic Non-Newtonian Fluids: Regularity and Numerics.' His research interests span numerical methods for PDEs, finite element techniques, adaptive mesh refinement, and iterative solvers for nonlinear problems. Recent work includes studies on minimal residual methods, p-Laplacian equations, and interpolation operators in negative Sobolev spaces. Dr. Storn contributes to interdisciplinary projects at the intersection of mathematics and fluid dynamics, leveraging advanced numerical analysis to address complex physical phenomena. Key trends in his publications emphasize robust numerical schemes for challenging PDE systems, including those with large exponents or stochastic elements, and the development of efficient adaptive algorithms. His work often intersects with applied mathematics, targeting real-world applications in engineering and physics.

• Numerical Analysis
• Partial Differential Equations
• Fluid Dynamics
• +3 more

Marius Yamakou is a Researcher and Lecturer at the Department of Data Science (DDS) within the Faculty of Sciences at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) . He is also an Interim Professor in the same department (10/2024–03/2025) and a Habilitation Candidate in Applied Mathematics (2019–present). Education : Ph.D. in Mathematics (2014–2018), IMPRS Scholarship (2014–2018). Dr. Yamakou's research focuses on applied mathematics , theoretical physics , and machine learning to model and control complex dynamical systems in neuroscience and physical/biological systems. His work merges dynamical systems theory , non-equilibrium statistical physics , and data-driven methods to uncover governing equations and devise optimal control strategies . His recent publications analyze diversity-induced resonance , STDP-driven synchronization , and noise-optimized neural dynamics . Key areas include spiking neural networks , physics-informed neural networks , and self-organization in adaptive systems. Scientific Awards : DFG Research Grant (2021–2023, €200K), IMPRS Scholarship (2014–2018). Dr. Yamakou supervises master’s students on topics like noise-induced resonance optimization and physics-informed neural network stability , including industry collaborations. He organizes the Conference on Women in Data Science (2023–2025) and has reviewed for 15+ journals in nonlinear dynamics and computational neuroscience .

• Applied Mathematics
• Theoretical Physics
• Machine Learning
• +18 more

Jian Sun is a researcher affiliated with the Chinese Academy of Sciences' Institute of Computing Technology. His work spans interdisciplinary fields including machine learning, control systems, robotics, and signal processing. He collaborates with institutions globally to advance theoretical and applied research in computational methods and their real-world applications. Research interests focus on machine learning for healthcare diagnostics, optimization algorithms for complex systems, and sensor fusion technologies for autonomous systems. Recent work emphasizes applications in cognitive impairment detection, UAV-based communication systems, and crystal structure prediction. Publications highlight advancements in adaptive control methodologies, cybersecurity in control systems, and 3D reconstruction techniques. His contributions bridge theoretical foundations with practical implementations in robotics, aerospace, and environmental monitoring.

• Machine Learning
• Control Systems
• Computer Vision
• +3 more

Ulrich Horst is a Full Professor at the Department of Mathematics, Humboldt University Berlin. He holds a PhD in Mathematics from Humboldt-University Berlin (2000) and has held visiting positions at Princeton University, the University of British Columbia, and the National University of Singapore. His research focuses on stochastic control theory, mean-field games, and financial mathematics. He has served as Scientific Director of the Deutsche Bank Quantitative Products Laboratory and has been a co-spokesperson of the Collaborative Research Center (CRC) 388 on Rough Analysis. He is the Editor-in-Chief of *Mathematics and Financial Economics* since 2015 and has organized numerous workshops, including the *Berlin-Princeton-Singapore Workshop on Quantitative Finance* series. His research interests include stochastic processes, optimal trading strategies, and game-theoretic models. His recent work explores market microstructure, Hawkes processes, and rough volatility. He has contributed to over 70 publications, with a focus on mathematical finance and stochastic systems.

• Stochastic Control Theory
• Mean-Field Games
• Mathematical Finance
• +3 more

Professor Stefanie Petermichl holds the Chair of Mathematics II (Harmonic Analysis) at the Institute of Mathematics, University of Würzburg, where she leads the Harmonic Analysis research team. She was awarded the prestigious Alexander von Humboldt Professorship in 2018, one of Germany's most valuable research awards, and maintains an active research program through 2026 under this appointment. Her research focuses on harmonic analysis, particularly on topics including BMO spaces, matrix-weighted inequalities, dyadic analysis, and singular integral operators. Professor Petermichl's work bridges pure mathematics with probability theory, developing sharp estimates for operators and exploring connections between harmonic analysis and stochastic processes. Her research has significantly advanced understanding of weighted norm inequalities and the behavior of operators in various function spaces. Her publication record shows consistent output in top mathematics journals, with recent work emphasizing matrix weights, dyadic models, and connections between harmonic analysis and probability. The trend in her research demonstrates increasing sophistication in handling matrix-weighted settings and exploring connections between discrete and continuous models in harmonic analysis. 2024 ECM invited lecture 2022 Member of the Bavarian Academy of Sciences and Humanities 2018 Humboldt Chair 2016 European Research Council (ERC) Consolidator Grant 2012 Ernest Dechelle prize of the French academy of science 2006 Salem prize Professor Petermichl has secured substantial research funding throughout her career, including the Alexander von Humboldt Professorship (2019-2026), ERC Consolidator Grant (2017-2022), Institut Universitaire de France membership (2012-2017), and multiple grants from the Agence Nationale de la Recherche. Her collaborative work spans international boundaries with frequent co-authorship across European and American institutions. She leads the Harmonic Analysis research group at the University of Würzburg, which includes several researchers including Dr. Hollenberger Jonas, Dr. Kakaroumpas Spyridon, Dr. Lechner Theresa, Dr. Nguyen Hisha, and Dr. Vardakis Dimitris, fostering a vibrant research environment focused on cutting-edge problems in harmonic analysis and its applications.

• Harmonic Analysis
• Probability Theory
• Functional Analysis
• +1 more

Marion Leibold is a Private Lecturer at the Faculty of Electrical Engineering , Technical University of Munich (TUM) . She holds a Habilitation (2020) and a Dr.-Ing. in Control Engineering (2007) from TUM, preceded by a Diploma in Techno-Mathematics (2002) from TU Munich. Research interests include Nonlinear and Robust Control , Model Predictive Control (MPC) , Safety in Regulation , and applications to Bipedal Robots and Autonomous Driving . Her work focuses on stochastic MPC , trajectory planning , and uncertainty handling in dynamic environments. Recent publications address greenhouse climate control , autonomous driving safety , data-driven MPC , and dual-arm robotic manipulation . She collaborates frequently with researchers like Dirk Wollherr and Martin Buss , with affiliations to the Chair of Control Engineering at TUM.

• Nonlinear Control
• Robust Control
• Optimal Control
• +4 more

Dr. Henrik Zimmer is a researcher at RWTH Aachen University specializing in computer graphics and geometry processing. His work bridges theoretical computational geometry with practical applications in digital fabrication, medical visualization, and industrial manufacturing. Affiliated with the Department of Computer Science, he has made significant contributions to mesh processing, 3D modeling, and optimization algorithms for real-world applications. His research focuses on geometry optimization for fabrication constraints, including Zometool-based approximation of freeform surfaces and rationalization of point-folding structures. Key interests include Mesh processing and topology preservation Variational methods for planar polygonal meshing Mixed-integer optimization for quadrangulation Medical visualization techniques for diffusion fiber tracking Industrial vision systems for laser welding process control His publication record shows consistent innovation from 2006 to 2014, with recent work emphasizing digital fabrication and practical optimization for consumer-level manufacturing. Notable scientific contributions include: Efficient algorithms for shortest path-concavity computation in 3D meshes Interactive volume-based visualization for diffusion MRI data Rationalization methods reducing fabrication costs by over 90% Novel approaches to melt pool monitoring in industrial welding Zimmer's collaborative work with Leif Kobbelt demonstrates strong industry-academia connections, particularly in translating geometric algorithms to manufacturing applications. His research spans both theoretical computer graphics (SIGGRAPH, Eurographics) and industrial applications (laser welding process control), reflecting a unique interdisciplinary approach. Current work focuses on making advanced geometric modeling accessible through personal fabrication technologies.

• Computer Graphics
• Geometry Processing
• 3D Modeling
• +5 more

Dong An is an Assistant Professor at the Beijing International Center for Mathematical Research (BICMR) , Peking University, since September 2024. He previously held a QuICS Hartree Postdoctoral Fellowship at the University of Maryland (2021-2024). He earned his Ph.D. in Applied Mathematics (2021) from UC Berkeley under Professor Lin Lin and a B.S. in Computational Mathematics (2016) from Peking University. Research Focus: Quantum algorithms for classical scientific computing problems and classical algorithms for quantum many-body problems. Key Areas: Quantum simulation, Hamiltonian dynamics, quantum linear systems, differential equations, and adiabatic quantum computing. Publications highlight advancements in quantum differential equation solvers, linear combinations of Hamiltonian simulations, and multi-product methods with explicit commutator scaling. His work bridges theoretical quantum computing and practical scientific computation, emphasizing optimal parameter dependence and error bounds. Scientific Awards: Co-Principal Investigator of NSF Robust Quantum Simulation Seed Grant QuICS Hartree Fellowship Bernard Friedman Memorial Prize in Applied Mathematics Multiple honors from Peking University Teaching: Currently instructs graduate-level quantum algorithms courses at Peking University. Previously served as a teaching assistant for differential equations and calculus at UC Berkeley. Service: Organizer for workshops on quantum differential equations and SIAM special sessions. Active reviewer for journals like PRX Quantum and conferences including QIP and TQC.

• Quantum Computing
• Applied Mathematics
• Quantum Algorithms
• +3 more

Prof. Dr. Kurt Helmes is a Professor of Operations Research at the Humboldt University of Berlin , Faculty of Economics. He holds the SAP Endowed Professorship for Organizational Economics – Future of Work and has been affiliated with the Economic Society at HU e.V. His office is located in Room 325, and he can be contacted via helmes@wiwi.hu-berlin.de . Education: Dipl.-Math. (Mathematics), Dr. rer. nat. (Doctorate in Natural Sciences, 1976), Habilitation (1982) Prof. Helmes specializes in stochastic models of operations research and adaptive stochastic control theory . His work bridges mathematical rigor with applications in finance, economics, and dynamic systems. Key research themes include exit time problems, Lévy processes, minimax estimation, and linear programming techniques for stochastic optimization. His publications span journals like Annals of Statistics , IEEE Transactions on Automatic Control , and Mathematics of Operations Research , reflecting interdisciplinary contributions. Articles focus on stochastic calculus, control theory, and probabilistic optimization, often addressing uncertainty in dynamic environments. Prof. Helmes collaborated with researchers such as N. Christopeit, R. Rishel, V. Beneš, and R. Stockbridge, contributing to fields like Markov processes, robust estimation, and stochastic control.

• Operations Research
• Stochastic Processes
• Control Theory
• +3 more