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Publikationen

Monografien

  • CH. Bayer, P.P. Hager, S. Riedel, Optimal Stopping for Non-Markovian Asset Price Processes, Ch. Bayer, G. Dos Reis, B. Horvath, H. Oberhauser, eds., Springer Finance, Springer Nature Link, Cham, pp. 1--424, (Monograph Published), DOI 10.1007/978-3-031-97239-3 .

  • J. Beda, G. Dos Reis, N. Tapia, An introduction to tensors for path signatures, Ch. Bayer, G. Dos Reis, B. Horvath, H. Oberhauser, eds., Signature Methods in Finance, Springer Nature, 2025, pp. 65--83, (Chapter Published), DOI 10.1007/978-3-031-97239-3_2 .

  • P.K. Friz, P.P. Hager , N. Tapia, On expected signatures and signature cumulants in semimartingale models, Ch. Bayer, G. Dos Reis, B. Horvath, H. Oberhauser, eds., Signature Methods in Finance, Springer Nature, 2025, pp. 381--424, (Chapter Published), DOI 10.1007/978-3-031-97239-3_11 .

  • CH. Bayer, G. Dos Reis, B. Horvath, H. Oberhauser, eds., Signature Methods in Finance, Springer Finance, Springer Nature Link, Cham, pp. 1--424, (Monograph Published), DOI 10.1007/978-3-031-97239-3 .

Artikel in Referierten Journalen

  • H. Kremp, N. Perkowski, Fractional Kolmogorov equations with singular paracontrolled terminal conditions, Journal of Theoretical Probability, 38 (2025), published online on 06.03.2025, DOI 10.1007/s10959-025-01408-x .

  • O. Butkovsky, K. Dareiotis, M. Gerencsér, Strong rate of convergence of the Euler scheme for SDEs with irregular drift driven by Lévy noise, Annales de l'Institut Henri Poincare. Probabilites et Statistiques, 61 (2025), pp. 2624--2660, DOI 10.1214/24-AIHP1506 .

  • O. Butkovsky, K. Lê, L. Mytnik, Stochastic equations with singular drift driven by fractional Brownian motion, Probability and Mathematical Physics, 6 (2025), pp. 857--912, DOI 10.2140/pmp.2025.6.857 .

  • O. Butkovsky, M. Scheutzow, Correction to: Couplings via comparison principle and exponential ergodicity of SPDEs in the hypoelliptic setting, Communications in Mathematical Physics, 406 (2025), pp. 1001--1034, DOI 10.1007/s00220-025-05424-0 .

  • A. Djurdjevac, H. Kremp, N. Perkowski, Rough homogenization for Langevin dynamics on fluctuating Helfrich surfaces, Stochastic Analysis and Applications, 43 (2025), pp. 423--445, DOI 10.1080/07362994.2025.2505736 .

  • M. Ghani Varzaneh, S. Riedel, A. Schmeding, N. Tapia, The geometry of controlled rough paths, Stochastic Processes and their Applications, 184 (2025), pp. 104594/1-- 104594/27, DOI 10.1016/j.spa.2025.104594 .
    Abstract
    We prove that the spaces of controlled (branched) rough paths of arbitrary order form a continuous field of Banach spaces. This structure has many similarities to an (infinite-dimensional) vector bundle and allows to define a topology on the total space, the collection of all controlled path spaces, which turns out to be Polish in the geometric case. The construction is intrinsic and based on a new approximation result for controlled rough paths. This framework turns well-known maps such as the rough integration map and the Itô?Lyons map into continuous (structure preserving) mappings. Moreover, it is compatible with previous constructions of interest in the stability theory for rough integration.

  • S. Athreya, O. Butkovsky, K. Lê, L. Mytnik, Analytically weak and mild solutions to stochastic heat equation with irregular drift., Stochastic Partial Differential Equations. Analysis and Computations, (2025), published online on 10.10.2025, DOI 10.1007/s40072-025-00392-x .

  • P. Bank, Ch. Bayer, P.P. Hager, S. Riedel, T. Nauen, Stochastic control with signatures, SIAM Journal on Control and Optimization, 63 (2025), pp. 3189--3218, DOI 10.1137/24M1667671 .

  • P. Bank, Ch. Bayer, P.K. Friz, L. Pelizzari, Rough PDEs for local stochastic volatility models, Mathematical Finance. An International Journal of Mathematics, Statistics and Financial Economics, 35 (2025), pp. 661--681, DOI 10.1111/mafi.12458 .

  • C. Bellingeri, E. Ferrucci, N. Tapia, Branched Itô formula and natural Itô-Stratonovich isomorphism, Advances in Mathematics, (2025), published online on 21.11.2025, DOI 10.1016/j.aim.2025.110687 .

  • D. Belomestny, J.G.M. Schoenmakers, V. Zorina, Weighted mesh algorithms for general Markov decision processes: Convergence and tractability, Journal of Complexity, published online on 26.02.2025, DOI 10.1016/j.jco.2025.101932 .
    Abstract
    We introduce a mesh-type approach for tackling discrete-time, finite-horizon Markov Decision Processes (MDPs) characterized by state and action spaces that are general, encompassing both finite and infinite (yet suitably regular) subsets of Euclidean space. In particular, for bounded state and action spaces, our algorithm achieves a computational complexity that is tractable in the sense of Novak & Wozniakowski, and is polynomial in the time horizon. For unbounded state space the algorithm is “semi-tractable” in the sense that the complexity is proportional to ε -c with some dimension independent c ≥ 2, for achieving an accuracy ε and polynomial in the time horizon with degree linear in the underlying dimension. As such the proposed approach has some flavor of the randomization method by Rust which deals with infinite horizon MDPs and uniform sampling in compact state space. However, the present approach is essentially different due to the finite horizon and a simulation procedure due to general transition distributions, and more general in the sense that it encompasses unbounded state space. To demonstrate the effectiveness of our algorithm, we provide illustrations based on Linear-Quadratic Gaussian (LQG) control problems.

  • B. Bischl , G. Casalicchio, T. Das, M. Feurer, S. Fischer, P. Gijsbers, S. Mukherjee, A.C. Müller, L. Németh, L. Oala, L. Purucker, S. Ravi, J.N. VAN Rijn, P. Singh, J. Vanschoren, J. VAN DER Velde, M. Wever, OpenML: Insights from 10 years and more than a thousand papers, Patterns. A Cell Press open access journal, 6 (2025), pp. 101317/1--101317/19, DOI 10.1016/j.patter.2025.101317 .

  • E. Gladin, A. Gasnikov, P. Dvurechensky, Accuracy certificates for convex minimization with inexact Oracle, Journal of Optimization Theory and Applications, 204 (2025), pp. 1/1--1/23, DOI 10.1007/s10957-024-02599-9 .
    Abstract
    Accuracy certificates for convex minimization problems allow for online verification of the accuracy of approximate solutions and provide a theoretically valid online stopping criterion. When solving the Lagrange dual problem, accuracy certificates produce a simple way to recover an approximate primal solution and estimate its accuracy. In this paper, we generalize accuracy certificates for the setting of inexact first-order oracle, including the setting of primal and Lagrange dual pair of problems. We further propose an explicit way to construct accuracy certificates for a large class of cutting plane methods based on polytopes. As a by-product, we show that the considered cutting plane methods can be efficiently used with a noisy oracle even thought they were originally designed to be equipped with an exact oracle. Finally, we illustrate the work of the proposed certificates in the numerical experiments highlighting that our certificates provide a tight upper bound on the objective residual.

  • N. Kornilov, M. Alkousa, E. Gorbunov, F. Stonyakin, P. Dvurechensky, A. Gasnikov, Intermediate gradient methods with relative inexactness, Journal of Optimization Theory and Applications, 207 (2025), published online on 22.08.2025, DOI 10.1007/s10957-025-02809-y .

  • D. Pasechniuk, P. Dvurechensky, C.A. Uribe, A. Gasnikov, Decentralised convex optimisation with probability-proportional-to-size quantization, EURO Journal on Computational Optimization, 13 (2025), published online on 22.07.2025, DOI 10.1016/j.ejco.2025.100113 .

  • N. Puchkin, F. Noskov, V. Spokoiny, Sharper dimension-free bounds on the Frobenius distance between sample covariance and its expectation, Bernoulli. Official Journal of the Bernoulli Society for Mathematical Statistics and Probability, 31 (2025), pp. 1664--1691, DOI 10.3150/24-BEJ1787 .

  • L. Schmitz, N. Tapia, Free generators and Hoffman's isomorphism for the two-parameter shuffle algebra, Communications in Algebra, (2025), published online: 01.11.2025, DOI 10.1080/00927872.2025.2569445 .
    Abstract
    Signature transforms have recently been extended to data indexed by two and more parameters. With free Lyndon generators, ideas from B∞-algebras and a novel two-parameter Hoffman exponential, we provide three classes of isomorphisms between the underlying two-parameter shuffle and quasi-shuffle algebras. In particular, we provide a Hopf algebraic connection to the (classical, one-parameter) shuffle algebra over the extended alphabet of connected matrix compositions.

  • H. Kremp, N. Perkowski, Rough weak solutions for singular Lévy SDEs, Probability Theory and Related Fields, 193 (2025), pp. 483--537, DOI 10.1007/s00440-025-01371-y .

  • CH. Bayer, B. Horvath, A. Muguruza, B. Stemper, M. Tomas, On deep calibration of (rough) stochastic volatility models, The Journal of FinTech, 5 (2025), pp. 550005/1--550005/37, DOI 10.1142/S2705109925500051 .

  • CH. Bayer, L. Pelizzari, J.G.M. Schoenmakers, Primal and dual optimal stopping with signatures, Finance and Stochastics, 29 (2025), pp. 981--1014,, DOI 10.1007/s00780-025-00570-8 .
    Abstract
    We propose two signature-based methods to solve the optimal stopping problem - that is, to price American options - in non-Markovian frameworks. Both methods rely on a global approximation result for Lp-functionals on rough path-spaces, using linear functionals of robust, rough path signatures. In the primal formulation, we present a non-Markovian generalization of the fa- mous Longstaff--Schwartz algorithm, using linear functionals of the signature as regression basis. For the dual formulation, we parametrize the space of square-integrable martingales using linear functionals of the signature, and apply a sample average approximation. We prove convergence for both methods and present first numerical examples in non-Markovian and non-semimartingale regimes.

  • P. Dvurechensky, G. Iommazzo, S. Shtern, M. Staudigl, A conditional gradient homotopy method with applications to semidefinite programming, IMA Journal of Numerical Analysis, (2025), pp. 8208252/1 -- 8208252/54, DOI 10.1093/imanum/draf059 .

  • P. Dvurechensky, Y. Nesterov, Improved global performance guarantees of second-order methods in convex minimization, Foundations of Computational Mathematics. The Journal of the Society for the Foundations of Computational Mathematics, (2025), published online on 13.08.2025, DOI 10.1007/s10208-025-09726-6 .

  • P.K. Friz, W. Salkeld, Th. Wagenhofer, Weak error estimates for rough volatility models, The Annals of Applied Probability, 35 (2025), pp. 64--98, DOI 10.1214/24-AAP2109 .

  • V. Spokoiny, M. Panov, Accuracy of Gaussian approximation for high-dimensional posterior distributions, Bernoulli. Official Journal of the Bernoulli Society for Mathematical Statistics and Probability, 31 (2025), pp. 843--867, DOI 10.3150/21-BEJ1412 .

  • V. Spokoiny, Deviation bounds for the norm of a random vector under exponential moment conditions with applications, Probability, Uncertainty and Quantitative Risk, 10 (2025), pp. 135--158, DOI 10.3934/puqr.2025007 .

  • V. Spokoiny, Inexact Laplace approximation and the use of posterior mean in Bayesian inference, Bayesian Analysis, 20 (2025), pp. 1303--1330, DOI 10.1214/23-BA1391 .

Beiträge zu Sammelwerken

  • B. Schembera, F. Wübbeling, H. Kleikamp, B. Schmidt, A. Shehu, M. Reidelbach, Ch. Biedinger, J. Fiedler, Th. Koprucki, D. Iglezakis, D. Göddeke, Towards a knowledge graph for models and algorithms in applied mathematics, in: Metadata and Semantics Research, M. Sfakakis, E. Garoufallou, M. Damigos, A. Salaba, Ch. Papatheodorou, eds., Communications in Computer and Information Science, Springer, Cham, 2025, pp. 95--109, DOI 10.1007/978-3-031-81974-2_8 .
    Abstract
    Mathematical models and algorithms are an essential part of mathematical research data, as they are epistemically grounding numerical data. To make this research data FAIR, we present how two previously distinct ontologies, MathAlgoDB for algorithms and MathModDB for models, were merged and extended into a living knowledge graph as the key outcome. This was achieved by connecting the ontologies through computational tasks that correspond to algorithmic tasks. Moreover, we show how models and algorithms can be enriched with subject-specific metadata, such as matrix symmetry or model linearity, essential for defining workflows and determining suitable algorithms. Additionally, we propose controlled vocabularies to be added, along with a new class that differentiates base quantities from specific use case quantities. We illustrate the capabilities of the developed knowledge graph using two detailed examples from different application areas of applied mathematics, having already integrated over 250 research assets into the knowledge graph.

Preprints, Reports, Technical Reports

  • W. Kenmoe Nzali, Ch. Bayer, D. Kreher, M. Landstorfer, Volatile electricity markets and battery storage: A model-based approach for optimal control, Preprint no. 3248, WIAS, Berlin, 2025, DOI 10.20347/WIAS.PREPRINT.3248 .
    Abstract, PDF (1340 kByte)
    Grid connected energy storage systems provide a strategic advantage by exploiting electricity market price fluctuations, thereby significantly reducing energy consumption costs. This paper presents a general framework for minimizing electricity consumption costs by formulating the problem as a stochastic optimal control problem for a stationary battery storage device (SBSD). We propose a realistic model for electricity spot prices calibrated with real data, alongside a detailed model of battery dynamics with practical parameters. The control problem is solved in a discrete time setting by combining dynamic programming with the least squares Monte Carlo method, allowing us to approximate the value function and the optimal policy under both state of charge and voltage constraints. Using the derived optimal policy, we estimate the lower bound of electricity consumption costs across multiple price trajectories. The results demonstrate that the SBSD can substantially reduce consumption costs, with savings increasing with battery duration. After one year, a battery with 12 hours duration achieves approximately 11% cost reduction, while 24 hours battery achieves 21%, compared to a scenario without storage. Finally, we estimate the amortization time (the period required for cumulative savings to offset the initial investment). After 6.7 years for the 12 hours battery and 9.9 years for the 24 hours battery, the amortization time is reached.

  • D. Shmelev, K. Ebrahimi-Fard, N. Tapia, C. Salvi, Explicit and effectively symmetric Runge--Kutta methods, Preprint no. 3233, WIAS, Berlin, 2025, DOI 10.20347/WIAS.PREPRINT.3233 .
    Abstract, PDF (2003 kByte)
    Symmetry is a key property of numerical methods. The geometric properties of symmetric schemes make them an attractive option for integrating Hamiltonian systems, whilst their ability to exactly recover the initial condition without the need to store the entire solution trajectory makes them ideal for the efficient implementation of Neural ODEs. In this work, we present a Hopf algebraic approach to the study of symmetric B-series methods. We show that every B-series method can be written as the composition of a symmetric and sayantisymmetric component, and explore the structure of this decomposition for Runge--Kutta schemes. A major bottleneck of symmetric Runge--Kutta schemes is their implicit nature, which requires solving a nonlinear system at each step. By introducing a new set of order conditions which minimise the antisymmetric component of a scheme, we derive what we call Explicit and Effectively Symmetric (EES) schemes -- a new class of explicit Runge--Kutta schemes with near-symmetric properties. We present examples of second-order EES schemes and demonstrate that, despite their low order, these schemes readily outperform higher-order explicit schemes such as RK4 and RK5, and achieve results comparable to implicit symmetric schemes at a significantly lower computational cost.

  • A. Alphonse, P. Dvurechensky, I. Papadopoulos, C. Sirotenko, LeAP--SSN: A semismooth Newton method with global convergence rates, Preprint no. 3217, WIAS, Berlin, 2025, DOI 10.20347/WIAS.PREPRINT.3217 .
    Abstract, PDF (2618 kByte)
    We propose LeAP-SSN (Levenberg?Marquardt Adaptive Proximal SemismoothNewton method), a semismooth Newton-type method with a simple, parameter-free globalisation strategy that guarantees convergence from arbitrary starting points in nonconvex settings to stationary points, and under a Polyak?Łojasiewicz condition, to a global minimum, in Hilbert spaces. The method employs an adaptive Levenberg?Marquardt regularisation for the Newton steps, combined with backtracking, and does not require knowledge of problem-specific constants. We establish global nonasymptotic rates: O(1/k) for convex problems in terms of objective values, O(1/sqrtk) under nonconvexity in terms of subgradients, and linear convergence under a Polyak?Łojasiewicz condition. The algorithm achieves superlinear convergence under mild semismoothness and Dennis?Moré or partial smoothness conditions, even for non-isolated minimisers. By combining strong global guarantees with superlinear local rates in a fully parameter-agnostic framework, LeAP-SSN bridges the gap between globally convergent algorithms and the fast asymptotics of Newton's method. The practical efficiency of the method is illustrated on representative problems from imaging, contact mechanics, and machine learning.

  • N. Berglund, T. Klose, N. Tapia, Perturbative renormalisation of the $Phi^4_4-eps$ model via generalized Wick maps, Preprint no. 3205, WIAS, Berlin, 2025, DOI 10.20347/WIAS.PREPRINT.3205 .
    Abstract, PDF (373 kByte)
    We consider the perturbative renormalisation of the $Phi^4_d$ model from Euclidean Quantum Field Theory for any, possibly non-integer dimension $d<4$. The so-called BPHZ renormalisation, named after Bogoliubov, Parasiuk, Hepp and Zimmermann, is usually encoded into extraction-contraction operations on Feynman diagrams, which have a complicated combinatorics. We show that the same procedure can be encoded in the much simpler algebra of polynomials in two unknowns $X$ and $Y$, which represent the fourth and second Wick power of the field. In this set- ting, renormalisation takes the form of a “Wick map” which maps monomials into Bell polynomials. The construction makes use of recent results by Bruned and Hou on multiindices, which are algebraic objects of intermediate complexity between Feynman diagrams and polynomials.

  • Z. Amer, A. Avdzhieva, M. Bongarti, P. Dvurechensky, P. Farrell, U. Gotzes, F.M. Hante, A. Karsai, S. Kater, M. Landstorfer, M. Liero, D. Peschka, L. Plato, K. Spreckelsen, J. Taraz, B. Wagner, Modeling hydrogen embrittlement for pricing degradation in gas pipelines, Preprint no. 3201, WIAS, Berlin, 2025, DOI 10.20347/WIAS.PREPRINT.3201 .
    Abstract, PDF (12 MByte)
    This paper addresses aspects of the critical challenge of hydrogen embrittlement in the context of Germany's transition to a sustainable, hydrogen-inclusive energy system. As hydrogen infrastructure expands, estimating and pricing embrittlement become paramount due to safety, operational, and economic concerns. We present a twofold contribution: We discuss hydrogen embrittlement modeling using both continuum models and simplified approximations. Based on these models, we propose optimization-based pricing schemes for market makers, considering simplified cyclic loading and more complex digital twin models. Our approaches leverage widely-used subcritical crack growth models in steel pipelines, with parameters derived from experiments. The study highlights the challenges and potential solutions for incorporating hydrogen embrittlement into gas transportation planning and pricing, ultimately aiming to enhance the safety and economic viability of Germany's future energy infrastructure.

  • J. Beda, G. Dos Reis, N. Tapia, An introduction to tensors for path signatures, Preprint no. 3173, WIAS, Berlin, 2025.
    Abstract, PDF (290 kByte)
    We present a fit-for-purpose introduction to tensors and their operations. It is envisaged to help the reader become acquainted with its underpinning concepts for the study of path signatures. The text includes exercises, solutions and many intuitive explanations. The material discusses direct sums and tensor products as two possible operations that make the Cartesian product of vectors spaces a vector space. The difference lies in linear Vs. multilinear structures -- the latter being the suitable one to deal with path signatures. The presentation is offered to understand tensors in a deeper sense than just a multidimensional array. The text concludes with the prime example of an algebra in relation to path signatures: the tensor algebra. This manuscript is the extended version (with two extra sections) of a chapter to appear in Open Access in a forthcoming Springer volume "Signatures Methods in Finance: An Introduction with Computational Applications". The two additional sections here discuss the factoring of tensor product expressions to a minimal number of terms. This problem is not critical for path signatures theory, but is an elegant way of becoming familiar with the language of tensors and tensor products that are used throughout the forthcoming volume. A GitHub repository is attached.

  • CH. Bayer, L. Pelizzari, J.-J. Zhu, Pricing American options under rough volatility using deep-signatures and signature-kernels, Preprint no. 3172, WIAS, Berlin, 2025, DOI 10.20347/WIAS.PREPRINT.3172 .
    Abstract, PDF (650 kByte)
    We extend the signature-based primal and dual solutions to the optimal stopping problem recently introduced in [Bayer et al.: Primal and dual optimal stopping with signatures, to ap- pear in Finance & Stochastics 2025], by integrating deep-signature and signature-kernel learning methodologies. These approaches are designed for non-Markovian frameworks, in particular en- abling the pricing of American options under rough volatility. We demonstrate and compare the performance within the popular rough Heston and rough Bergomi models.

  • I. Chevyrev, J. Diehl, K. Ebrahimi-Fard, N. Tapia, A multiplicative surface signature through its Magnus expansion, Preprint no. 3171, WIAS, Berlin, 2025, DOI 10.20347/WIAS.PREPRINT.3171 .
    PDF (731 kByte)

  • CH. Bayer, B. Djehiche, E. Rezvanova, R. Tempone, Continuous time stochastic optimal control under discrete time partial observations, Preprint no. 3168, WIAS, Berlin, 2025, DOI 10.20347/WIAS.PREPRINT.3168 .
    Abstract, PDF (1524 kByte)
    This work addresses stochastic optimal control problems where the unknown state evolves in continu- ous time while partial, noisy, and possibly controllable measurements are only available in discrete time. We develop a framework for controlling such systems, focusing on the measure-valued process of the system's state and the control actions that depend on noisy and incomplete data. Our approach uses a stochastic optimal control framework with a probability measure-valued state, which accommodates noisy measure- ments and integrates them into control decisions through a Bayesian update mechanism. We characterize the control optimality in terms of a sequence of interlaced Hamilton Jacobi Bellman (HJB) equations coupled with controlled impulse steps at the measurement times. For the case of Gaussian-controlled processes, we derive an equivalent HJB equation whose state variable is finite-dimensional, namely the state's mean and covariance. We demonstrate the effectiveness of our methods through numerical examples. These include control under perfect observations, control under no observations, and control under noisy observa- tions. Our numerical results highlight significant differences in the control strategies and their performance, emphasizing the challenges and computational demands of dealing with uncertainty in state observation.

  • CH. Bayer, M. Redmann, Dimension reduction for path signatures, Preprint no. 3163, WIAS, Berlin, 2025, DOI 10.20347/WIAS.PREPRINT.3163 .
    Abstract, PDF (740 kByte)
    This paper focuses on the mathematical framework for reducing the complexity of models using path signatures. The structure of these signatures, which can be interpreted as collections of iterated integrals along paths, is discussed and their applications in areas such as stochastic differential equations (SDEs) and financial modeling are pointed out. In particular, exploiting the rough paths view, solutions of SDEs continuously depend on the lift of the driver. Such continuous mappings can be approximated using (truncated) signatures, which are solutions of high-dimensional linear systems. In order to lower the complexity of these models, this paper presents methods for reducing the order of high-dimensional truncated signature models while retaining essential characteristics. The derivation of reduced models and the universal approxi- mation property of (truncated) signatures are treated in detail. Numerical examples, including applications to the (rough) Bergomi model in financial markets, illustrate the proposed reduction techniques and highlight their effectiveness.

  • E. Abi Jaber, Ch. Bayer, S. Breneis, State spaces of multifactor approximations of nonnegative Volterra processes, Preprint no. 3162, WIAS, Berlin, 2025.
    Abstract, PDF (594 kByte)
    We show that the state spaces of multifactor Markovian processes, coming from approximations of nonnegative Volterra processes, are given by explicit linear transformation of the nonnegative orthant. We demonstrate the usefulness of this result for applications, including simulation schemes and PDE methods for nonnegative Volterra processes.

Vorträge, Poster

  • H. Kremp, Higher order approximation of nonlinear SPDEs with additive space-time white noise, YMCN Spring School: Recent Advances in SPDEs, March 26 - 28, 2025, Universität Münster, Exzellenzcluster Mathematik, March 27, 2025.

  • H. Kremp, Optimal Transport meets Rough Analysis, CRC TRR 388 Retreat: Rough Analysis, Stochastic Dynamics & Related Fields, September 11 - 13, 2025, Döllnsee.

  • H. Kremp, Overcoming the order barrier for nonlinear SPDEs with addititve space-time white noise, Recent Developments in SPDEs and BSDE's meet harmonic and functional analysis, November 16 - 21, 2025, Mathematisches Forschungsinstitut Oberwolfach, November 18, 2025.

  • H. Kremp, Overcoming the spatial order barrier for approximations of SPDEs with additive space time white noise, Stochastic Numerics and Inverse Problems in Sweden (SNIPS 2025), August 25 - 29, 2025, Linnaeus University, Växjö,, Sweden, August 27, 2025.

  • H. Kremp, Rough Analysis, 44th Conference on Stochastic Processes and their Applications (SPA), July 14 - 18, 2025, Wrocław University of Science and Technology (Politechnika Wrocławska), Poland, July 17, 2025.

  • H. Kremp, Transportation cost inequalities on Heisenberg path space, TRR Analysis Day, Max Planck Institute for Mathematics in the Sciences, Leipzig, July 7, 2025.

  • A. Shehu, FAIR representation of mathematical research data: MathModBB and m athAlgoDB as knowledge graphs for mathematical models and numerical algorithms, DiHMa.Lab, October 14 - 15, 2025, FU Berlin, October 15, 2025.

  • A. Shehu, MathModDB: An ontology and knowledge graph for mathematical models, Vom Beweis zum Bibliotheksregal: Workshop rund um Forschungsdatenmanagement für die Mathematik, March 18, 2025, MPI für Mathematik in den Naturwissenschaften Leipzig, March 18, 2025.

  • O. Butkovsky, Mini-course: Stochastic sewing with applications (Part 1), PDF Seminar, Beijing Institute of Technology, School of Mathematics and Statistics, China, July 1, 2025.

  • O. Butkovsky, Mini-course: Stochastic sewing with applications (Part 2), PDF Seminar, Beijing Institute of Technology, School of Mathematics and Statistics, China, July 2, 2025.

  • O. Butkovsky, New developments in regularization by noise for stochastic differential equations, Probability seminar, University of Pau and Adour Region, Laboratory of Mathematics and its Applications of Pau, France, February 6, 2025.

  • O. Butkovsky, Recent challenges in stochastic and rough analysis arising from finance, WIAS Days, Humboldt-Universität zu Berlin, March 25, 2025.

  • O. Butkovsky, Statistical inference and approximation for nonlinear SPDEs, CRC TRR 388 Retreat: Rough Analysis, Stochastic Dynamics & Related Fields, September 11 - 12, 2025, Döllnsee, September 11, 2025.

  • O. Butkovsky, Towards the Krylov-Röckner condition for SDEs driven by fractional Brownian motion, Workshop on Regularisation by Noise, April 14 - 18, 2025, Technische Universität Wien, Institut für Analysis und Scientific Computing, April 14, 2025.

  • O. Butkovsky, Weak and strong well-posedness for SPDEs with singular drifts, Simons Laufer Mathematical Sciences Institute (SLMath), Berkeley, CA, USA, October 31, 2025.

  • O. Butkovsky, Weak uniqueness and small mass limits for stochastic equations with singular drifts, Stochastic & Rough Analysis (SRA25), April 7 - 11, 2025, TU Berlin, Institut für Mathematik, April 7, 2025.

  • O. Butkovsky, Weak uniqueness for singular stochastic equations driven by fractional Brownian motion, Oberseminar Stochastische Analysis, Universität Konstanz, Fachbereich Mathematik und Statistik, January 28, 2025.

  • O. Butkovsky, Weak uniqueness for singular stochastic equations driven by fractional Brownian motion, Séminaire de Probabilités, Université Paul Sabatier, Institut de Mathématiques de Toulouse, France, February 4, 2025.

  • O. Butkovsky, Weak uniqueness for singular stochastic equations driven by fractional Brownian motion, PDF Seminar, Beijing Institute of Technology, School of Mathematics and Statistics, China, July 3, 2025.

  • O. Butkovsky, Weak uniqueness for stochastic equations with singular drifts, North-East and Midlands Stochastic Analysis (NEMSA)., March 11 - 12, 2025, University of York, Department of Mathematics, UK, March 11, 2025.

  • D. Gogolashvili, Modelling quantifier use with beta distributions (joint work with Aarti Joshi (ZAS Berlin)), Leibniz MMS Days 2025, Leibniz-Institut für Ostseeforschung Warnemünde (IOW), March 27, 2025.

  • W. Kenmoe Nzali, Stochastic optimal control of stationary battery storage devices on volatile electricity markets, Mathematics for Smart Energy, November 4 - 6, 2025, WIAS Berlin, December 4, 2025.

  • W. Kenmoe Nzali, D. Kreher, Ch. Bayer, M. Landstorfer, Volatile electricity market and battery storage, Vienna Congress on Mathematical Finance (VCMF 2025), Austria, July 9 - 11, 2025.

  • W. Kenmoe Nzali, D. Kreher, Ch. Bayer, M. Landstorfer, Volatile electricity market and battery storage, Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025, Thuwal, Saudi Arabia, May 18 - 25, 2025.

  • W. Kenmoe Nzali, D. Kreher, Ch. Bayer, M. Landstorfer, Math+ Day, November 17, 2025.

  • W. Kenmoe Nzali, Volatile electricity market and battery storage, Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025, Thuwal, Saudi Arabia, May 18 - 25, 2025.

  • W. Kenmoe Nzali, Volatile electricity market and battery storage, Vienna Congress on Mathematical Finance (VCMF 2025), July 9 - 11, 2025, TU Wiena (Vienna University of Economics and Business), Institute for Statistics and Mathematics, Austria, July 9, 2025.

  • W. Kenmoe Nzali, Volatile electricity market and battery storage, 20th Doktorand:innentreffen der Stochastik, August 27 - 29, 2025, Universität Mannheim, August 28, 2025.

  • A. Kroshnin, Accelerated gradient methods for optimizing locally smooth convex functions, Conference on Mathematics of Machine Learning 2025, September 22 - 25, 2025.

  • L. Pelizzari, On the Volterra signature, Signatures in 1 and 2 dimensions, June 16 - 20, 2025, BI Norwegian Business School, Department of Economics, Oslo, Norway, June 16, 2025.

  • L. Pelizzari, Optimal stopping with signatures for premia, Premia Meeting, Institut national de recherche en informatique et en automatique (INRIA), MathRisk, Paris, France, June 2, 2025.

  • L. Pelizzari, Project B03, CRC TRR 388 Retreat: Rough Analysis, Stochastic Dynamics & Related Fields, September 11 - 13, 2025, Döllnsee, September 11, 2025.

  • L. Pelizzari, Signature-methods for non-Markovian optimal stopping, 2025 SIAM Financial Mathematics and Engineering, July 14 - 18, 2025, Society for Industrial and Applied Mathematics, Miami, FL, USA, July 15, 2025.

  • L. Pelizzari, Volterra signatures, IRTG-CDT Summer School 2025, September 8 - 11, 2025, Döllnsee, September 11, 2025.

  • N. Tapia, A multiplicative surface signature through its Magnus expansion, Recent Advances rough path and signature theory, September 15 - 19, 2025, ShanghaiTech University, Institute of Mathematical Sciences, China, September 19, 2025.

  • N. Tapia, Algebraic aspects of path signatures and applications, Signatures and Rough Paths: From Stochastics, Geometry and Algebra to Machine Learning, May 19 - 23, 2025, University of Edinburgh, Edinburgh Futures Institute, UK, May 19, 2025.

  • N. Tapia, Shifted substitution in non-commutative multivariate power series with a view toward free probability, Recent Perspectives on Non-Crossing Partitions Through Algebra, Combinatorics, and Probability, February 17 - 21, 2025, Universität Wien, Fakultät für Mathematik, Austria, February 20, 2025.

  • N. Tapia, Stability of deep ResNets via discrete rough paths, Signatures in 1 and 2 dimensions, June 16 - 20, 2025, BI Norwegian Business School, Department of Economics, Oslo, Norway, June 20, 2025.

  • J. Geuter, G. Kornhardt, I. Tomasson, V. Laschos, Universal neural optimal transport, ICML 2025 Forty-Second International Conference on Machine Learning, Vancouver, Canada, July 13 - 19, 2025.

  • V. Spokoiny, Ranking from pairwise comparison: Breaking of dimension, AI & Digital Assets (AIDA) Workshop, September 19 - 20, 2025, University of Edinburgh, Business School, UK, September 19, 2025.

  • CH. Bayer, A kernel regression approach to local stochastic volatility models, Milstein's method: 50 years on, June 30 - July 3, 2025, University of Nottingham, School of Mathematical Sciences, UK, June 30, 2025.

  • CH. Bayer, A kernel regression approach to solving singular local volatility models, Advances in Stochastic Processes and Numerical Analysis -A Marcus Wallenberg Symposium, August 19 - 20, 2025, KTH Royal Institute of Technology, Sweden, August 19, 2025.

  • CH. Bayer, American option pricing in rough volatility models, Stochastics & Computational Finance 2025, September 2 - 5, 2025, Universidade de Lisboa, ISEG Lisbon School of Economics & Management, September 2, 2025.

  • CH. Bayer, An introduction to signatures with applications in finance, Barcelona Summer School of Stochastic Analysis and Quantitative Finance 2025, July 21 - 25, 2025, Centre de Recerca Matemàtica, Barcelona, Spain.

  • CH. Bayer, Global and local regression: A signature approach with applications, Recent Advances rough path and signature theory, September 15 - 19, 2025, ShanghaiTech University, China, September 18, 2025.

  • CH. Bayer, Global and local regression: A signature approach with applications, Mathematical and Computational Finance Seminar, University of Oxford, Mathematical Institute, UK, November 20, 2025.

  • CH. Bayer, Introduction to path signatures, WIAS Days, Humboldt-Universität zu Berlin, March 24, 2025.

  • CH. Bayer, Markovian approximations of rough volatility models, Mathematics for Uncertainty Quantification, August 14 - 15, 2025, RWTH Aachen University, Department of Mathematics, August 14, 2025.

  • CH. Bayer, Memory in Quantitative Finance (Part 1 - 3), 2025 SIAM Financial Mathematics and Engineering, July 14 - 18, 2025, Society for Industrial and Applied Mathematics, Miami, FL, USA, July 15, 2025.

  • CH. Bayer, Path signature methods for pricing of Bermudan options, Signatures in 1 and 2 dimensions, June 16 - 20, 2025, BI Norwegian Business School, Department of Economics, Oslo, Norway, June 16, 2025.

  • CH. Bayer, Path signature methods for pricing of Bermudan options, rt-matrisk2025 : The Conference of the Thematic Network Matrisk, June 10 - 13, 2025, Sorbonne Université, Laboratoire de Probabilités, Statistique et Modélisation, Paris, France, June 11, 2025.

  • CH. Bayer, Pricing American options under rough volatility, Workshop on Neural Dynamical Systems for Time-Series Data, April 23 - 25, 2025, University of Vienna, Department of Statistics, Austria, April 24, 2025.

  • CH. Bayer, Pricing american options under rough volatility using deep signatures and signatures kernels, Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025, May 18 - 25, 2025, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, May 18, 2025.

  • CH. Bayer, Primal and dual optimal stopping with signatures, Universität Mannheim, Institut für Mathematik, February 26, 2025.

  • CH. Bayer, Signatures for stochastic optimal control, Mathematical Finance Seminar, Columbia University, Department of Mathematics, USA, March 6, 2025.

  • O. Butkovsky, Stochastic sewing with applications (mini-course), University of Edinburgh, Maxwell Institute, UK, March 19, 2025.

  • P. Dvurechensky, Minimization involving self-concordant functions and barriers, Physics of AI Algorithms, PHAIA 2025, January 13 - 17, 2025, Université Grenoble Alpes, École de Physique des Houches, Les Houches, France, January 17, 2025.

  • P. Dvurechensky , Computational optimal transport, WIAS Days, WIAS Berlin, February 25, 2025.

  • P. Friz, Rough SDEs and Applications, Data Science Seminar, Southern University of Science and Technology, Mathematics Department, Shenzhen, China, January 10, 2025.

  • P.K. Friz, Rough stochastic analysis, 12th International Conference on Stochastic Analysis and its Applications (ICSAA 2025), September 8 - 12, 2025, University Politehnica Bucharest, Institut of Mathematics, Bukarest, Romania.

  • P.K. Friz, Rough stochastic analysis, The 10th International Symposium on Backward Stochastic Differential Equations, June 26 - July 1, 2025, Shandong University, School of Mathematics and Statistics, Qingdao, China, June 28, 2025.

  • P.K. Friz, Rough stochastic differential equations, Probabilités, finance et signall: conférence en l'honneur de René Carmona, May 19 - 23, 2025, Centre National de la Recherche Scientifique (CNRS), Luminy, France, May 19, 2025.

  • P.K. Friz, Rough stochastic differential equations and applications, Stochastic Equations and Stochastic Dynamics, February 9 - 14, 2025, SwissMAP Research Station (SRS), Les Diablerets, Switzerland.

  • P.K. Friz, What fractional Brownian motion can teach us about renormalization, Renormalisation and Randomness, September 21 - December 26, 2025, Mathematisches Forschungsinstitut Oberwolfach, September 22, 2025.

  • P.K. Friz , Rough and stochastic filtering (online talk), Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025, May 18 - 25, 2025, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, May 21, 2025.

  • V. Spokoiny, Estimation and Inference for smooth DNNs. Blessing of dimension., UoE Statistics Seminar, University of Edinburgh, School of Mathematics, UK, September 15, 2025.

  • V. Spokoiny, Estimation and classification for DNN: Bless of dimension, Forschungsseminar Mathematische Statistik, WIAS Berlin, May 14, 2025.

  • V. Spokoiny, Estimation and inference for deep neuronal network: Blessing of dimension, Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025, May 18 - 25, 2025, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, May 19, 2025.

  • V. Spokoiny, Perturbed optimization and its applications to high dimensional logistic regression, ICOMP 2025 International Conference on Computational Optimization, October 17 - 19, 2025, Abu Dhabi, United Arab Emirates, October 17, 2025.

  • V. Spokoiny, Semiparametric plug-in estimation and inference for the BTL model, Statistic Seminar, Universität von Glasgow, School of Mathematics & Statistics, UK, September 17, 2025.

  • V. Spokoiny, Statistical inference for deep neuronal networks, Heidelberg - Paris Workshop on Mathematical Statistics, January 27 - 29, 2025, Universität Heidelberg, January 27, 2025.

  • K. Tabelow, On some math for functional magnetic resonance imaging, Final RTG 2224 Event - Summer School 2025, September 25 - 26, 2025, Universität Bremen, Fachbereich 3 - Mathematik und Informatik, September 25, 2025.

Preprints im Fremdverlag

  • O. Butkovsky, Lectures on stochastic sewing with applications, Preprint no. arXiv:2510.12165, Cornell University, 2025, DOI 10.48550/arXiv.2510.12165 .

  • E. Gladin, A. Kroshnin, J.-J. Zhu, P. Dvurechensky, Improved stochastic optimization of LogSumExp, Preprint no. arXiv:2509.24894, Cornell University, 2025, DOI 10.48550/arXiv.2509.24894 .

  • I. Latypov, A. Suvorikova, A. Kroshnin, A. Gasnikov, Y. Dorn, UCB-type algorithm for budget-constrained expert learning, Preprint no. arXiv:2510.22654, Cornell University, 2025, DOI 10.48550/arXiv.2510.22654 .

  • D. Pasechniuk, P. Dvurechensky, C.A. Uribe, A. Gasnikov, Decentralised convex optimisation with probability-proportional-to-size quantization, Preprint no. arXiv:2501.18312, Cornell University, , DOI 10.48550/arXiv.2501.18312 .

  • A. Vasin, V. Krivchenko, D. Kovalev, F. Stonyakin, N. Tupitsa, P. Dvurechensky, M. Alkousa, N. Kornilov, A. Gasnikov, On solving minimization and min-max problems by first-order methods with relative error in gradients, Preprint no. arXiv:2503.06628, Cornell University, , DOI 10.48550/arXiv.2503.06628 .

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