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1 SIAG/FME virtual seminars series

[edit] SIAG/FME virtual seminars series

The series of virtual talks, started by the SIAM Activity Group on Financial Mathematics and Engineering (SIAG/FME), aims at keeping the mathematical finance community connected worldwide beyond traditional formats. The goal is to host a diverse, across all dimensions, lineup of prominent speakers that will present the latest developments in the area of financial mathematics and engineering.

The talks will be once a month, usually on the second Thursday of the month.
The talks will alternate with those set up by the Bachelier Finance Society
All talks will be delivered remotely using Zoom.
The talks are open to the public. Due to security reasons, all attendees have to register.
The registration link will be posted on this web-site, next to the each seminar date below. The detailed information about each talk, and the registration link will be also distributed via SIAM-Engage platform.
The registration is quick (asks only for your name and email), and once registered, you will receive an email with the link to the meeting(s), which is unique to you, so please do not share that email. The registration is usually valid for multiple future talks.

SIAG/FME Seminar Series Committee:

Chair: Samuel Cohen,
Vice Chair: Christa Cuchiero,
Program Director: Luitgard A. M. Veraart,
Secretary: Ibrahim Ekren

The committee is in charge of the scientific component of the seminar, including selecting the speakers and the format of the events. Suggestions from the public on potential speakers, covered topics as well as general recommendation on how to improve the series are welcome and can be addressed to any committee member.

[edit] Forthcoming Talks

We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is

June 11, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Sergio Pulido, ensIIE

Title: Boundary attainment conditions for stochastic Volterra equations

Abstract: In this presentation, I will discuss boundary attainment conditions for one-dimensional stochastic Volterra equations (SVEs) of convolution type. In the first part of the talk, I will present an Osgood-type test for explosion to infinity of SVEs driven by additive noise, featuring kernels from a family that includes the fractional kernel. I will also investigate stability results for explosion times with respect to the kernels, including the case of an Euler-Maruyama approximation scheme. In the second part, I will present a Feller-type test that establishes, on a general open interval of the real line, necessary and sufficient conditions for boundary attainment of solutions to SVEs with possibly multiplicative noise. Here, I will consider dynamics governed by nonsingular kernels, which preserve the semimartingale property of the processes while introducing memory effects through a path-dependent drift. I will also show an application of these results to the Volterra square-root diffusion. The talk is based on joint works with Alessandro Bondi.

Bio: Sergio Pulido is an Associate Professor (Maître de conférences HDR) at the École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ensIIE) and a permanent member of the Laboratoire de Mathématiques et Modélisation d'Évry (LaMME), a joint research unit of the Centre National de la Recherche Scientifique (CNRS), Université Évry Paris-Saclay, and the ensIIE.

He currently serves as Head of the International Relations Office at the ensIIE and co-manages the M1 in Applied Mathematics (Évry site) at Université Paris-Saclay. He is also an Associate Researcher in the Mathematical Finance group at the Centre de Mathématiques Appliquées (CMAP) of École Polytechnique.

Before joining ensIIE, he was a Postdoctoral Researcher at the Swissquote Chair in Quantitative Finance at the École Polytechnique Fédérale de Lausanne (EPFL), and a Postdoctoral Associate in Applied Probability and Mathematical Finance at Carnegie Mellon University. He received a PhD in Mathematics from Cornell University, an M.S. in Mathematics from Universidad de los Andes, and a B.S. in Mathematics from Universidad Nacional de Colombia.

Sergio Pulido’s recent research focuses on stochastic models with rough trajectories and their applications in finance. From a more theoretical perspective, he has studied stochastic processes solving stochastic convolution equations, namely Stochastic Volterra Equations (SVEs).

[edit] Past Talks

May 14, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Ruimeng Hu, University of California, Santa Barbara

Title: Machine Learning for Stochastic Control and Games: From Foundations to Mean-Field Learning

Abstract: Machine learning has become an increasingly useful tool for solving high-dimensional stochastic control and game problems that are difficult to handle with classical numerical methods. In this talk, I will begin with a general overview of several learning-based approaches for stochastic control and games, including direct policy parameterization, PDE-based methods, and BSDE-based methods, and discuss how these ideas extend to multi-agent and mean-field settings. I will then focus on recent joint work on a new learning framework for mean-field games, called mean-field actor-critic flow. The method combines actor-critic ideas from reinforcement learning with an optimal transport-based update of the population distribution, leading to a coupled learning dynamic for the value function, policy, and mean-field law. I will describe the main algorithmic ideas, discuss a global exponential convergence result under suitable time-scale separation, and present numerical examples illustrating the method.

Bio: Ruimeng Hu is an Associate Professor in the Department of Mathematics and the Department of Statistics and Applied Probability at the University of California, Santa Barbara. Her research interests include stochastic control, mean-field games, machine learning, and their applications in finance, economics, and multi-agent systems. Before joining UCSB, she was a Term Assistant Professor in the Department of Industrial Engineering and Operations Research at Columbia University. Her research is supported by grants from the National Science Foundation and the Office of Naval Research. She also serves as an Associate Editor for SIAM Journal on Financial Mathematics and Digital Finance.

April 9, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Yufei Zhang, Imperial College London

Title: An alpha-potential game framework for dynamic N-player games

Abstract: Game theory has a long history, yet identifying Nash equilibria in dynamic non-cooperative games remains a fundamental challenge with significant computational and conceptual complexity. Over the past decade, mean field game theory has emerged as a pivotal framework, offering important theoretical insights and computational advances for the analysis of large-scale stochastic games. However, mean field games require homogeneity and weak interactions among players and focus only on the limiting behavior when the number of players goes to infinity.

In this talk, we present a new paradigm for dynamic N-player games, called alpha-potential games, where the change of a player's objective function resulting from a unilateral deviation of her strategy is equal to the change of an alpha-potential function up to an error alpha. Within this framework, the problem of computing approximate Nash equilibria reduces to a stochastic control problem for the alpha-potential function, significantly simplifying both analysis and computation. The parameter alpha also reveals important structural properties of the game, such as the population size, the intensity of player interactions, and the degree of heterogeneity across players. We will discuss through simple examples some recent theoretical and algorithmic developments, along with a few open problems for this new game framework.

Bio: Yufei Zhang is an Associate Professor in Mathematical Finance and Machine Learning in the Department of Mathematics at Imperial College London, where he also serves as Co-Director of the MSc in Mathematics and Finance program. Before joining Imperial, he was an Assistant Professor in the Department of Statistics at the London School of Economics and Political Science. He earned his PhD in Mathematics from the University of Oxford in 2021. Yufei was awarded the J.P. MorganChase Faculty Research Award in 2025 for his work on the mathematics of artificial intelligence.

Yufei’s research lies at the intersection of stochastic control, game theory, machine learning, and mathematical finance, with a particular emphasis on developing theoretical foundations and algorithmic frameworks for complex decision-making in dynamic and uncertain environments.

March 12, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Eduardo Abi Jaber, Ecole Polytechnique

Title: Path-Signatures: Memory and Stationarity

Abstract: We explore the interplay between path-signatures, memory, and stationarity, highlighting their implications for machine learning, representation of stochastic processes and applications in mathematical finance. In a first part, we provide explicit series expansions to certain stochastic path-dependent integral equations in terms of the path signature of the time augmented driving Brownian motion. Our framework encompasses a large class of stochastic linear Volterra and delay equations and in particular the fractional Brownian motion with a Hurst index H in (0, 1). Our expressions allow to disentangle an infinite dimensional Markovian structure. In addition they open the door to: (i) straightforward and simple approximation schemes that we illustrate numerically, (ii) representations of certain Fourier-Laplace transforms in terms of a non-standard infinite dimensional Riccati equation with important applications for pricing and hedging in quantitative finance. In a second part, we introduce a time-invariant version of the signature: the fading-memory signature, with powerful algebraic, analytic and probabilistic properties and applications to learning stationary relationships in time series. This is based on joint works with Paul Gassiat, Louis-Amand Gérard, Yuxing Huang, Dimitri Sotnikov.

Bio: Eduardo Abi Jaber is a Professor of Applied Mathematics at Ecole Polytechnique. He defended his Habilitation à Diriger des Recherches in 2024 and his PhD in 2018.

His research investigates the role of memory in quantitative finance, advancing the mathematical foundations of sophisticated tools such as Volterra processes and path signatures. Beyond theory, his work translates into practical solutions to key challenges in the field, including volatility modeling and portfolio optimization. Positioned at the crossroads of mathematics and finance, his research combines rigorous analysis, advanced modeling, bespoke numerical methods, and systematic validation against real-world data.

Author of more than 40 papers, with publications in leading journals in applied probability and quantitative finance, Eduardo’s contributions have been recognized with several prestigious awards, including the Amies Prize for the best CIFRE PhD thesis in applied mathematics (2019) and the Junior Scholar Award of the Bachelier Finance Society (2018). He has delivered over 100 invited talks worldwide. He serves as an Associate Editor for Mathematical Finance and the International Journal of Theoretical and Applied Finance, and co-organizes the internationally recognized Bachelier Seminar in Paris. Over the years, he has led a research group comprising more than 10 PhD students and postdoctoral researchers.

February 12, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Christian Bayer, WIAS Berlin

Title: Global and local regression: a signature approach with applications

Abstract: The path signature is a powerful tool for solving regression problems on path space, i.e., for computing conditional expectations $\mathbb{E}[Y | X]$ when the random variable $X$ is a stochastic process -- or a time-series. We provide new theoretical convergence guarantees for two different, complementary approaches to regression using signature methods. In the context of global regression, we show that linear functionals of the robust signature are universal in the $L^p$ sense in a wide class of examples. In addition, we present a local regression method based on signature semi-metrics, and show universality as well as rates of convergence. Based on joint works with Davit Gogolashvili, Luca Pelizzari, and John Schoenmakers.

Bio: Christian Bayer obtained his PhD at the TU Vienna on numerical methods for stochastic differential equations. He is working as a Senior Researcher at the Weierstrass Institute of Applied Analysis and Stochastics in Berlin. His research interests are in rough volatility, computational finance, stochastic numerics, and stochastic optimal control.

December 11, 2025, 1PM-2.30PM (EST) Registration link:

Speaker: Erhan Bayraktar, University of Michigan

Title: A Mean-Field Approach to DeFi Currency Exchanges

Abstract: We investigate the behavior of liquidity providers (LPs) by modeling a decentralized cryptocurrency exchange (DEX) based on Uniswap v3. LPs with heterogeneous characteristics choose optimal liquidity positions subject to uncertainty regarding the size of exogenous incoming transactions and the prices of assets in the wider market. They engage in a game among themselves, and the resulting liquidity distribution determines the exchange rate dynamics and potential arbitrage opportunities of the pool. We calibrate the distribution of LP characteristics based on Uniswap data and the equilibrium strategy resulting from this mean-field game produces pool exchange rate dynamics and liquidity evolution consistent with observed pool behavior. We subsequently introduce Maximal Extractable Value bots who perform Just-In-Time liquidity attacks, and develop a Stackelberg game between LPs and bots. This addition results in more accurate simulated pool exchange rate dynamics and stronger predictive power regarding the evolution of the pool liquidity distribution.

Bio: Erhan Bayraktar, the Susan Smith Chair holder, is a full professor of Mathematics at the University of Michigan, where he has taught since 2004. His research spans stochastic analysis, control, applied probability, mean field games, machine learning, and mathematical finance, with applications in financial risk management. He serves as a corresponding editor for the SIAM Journal on Control and Optimization and sits on the editorial boards of Applied Mathematics and Optimization, Frontiers in Mathematical Finance, Mathematics of Operations Research, and Mathematical Finance. Bayraktar has secured continuous funding from the National Science Foundation, including a prestigious CAREER grant. Since 2015, he has directed the Risk Management and Quantitative Finance Masters program, shaping its development. He has mentored 17 Ph.D. students and over 40 post-docs, many now leading in academia and industry.

November 13, 2025, 1PM-2.30PM (EST) Registration link:

Speaker: Luciano Campi, University of Milan

Title: Optimal coarse correlated equilibria in mean field games

Abstract: We will consider coarse correlated equilibria (CCE) in continuous time mean field games. CCEs are generalizations of Nash equilibria, when a moderator (correlation device) recommend strategies to the players that are not convenient to unilaterally reject. We will first address existence and approximations results when the number of players goes to infinity. Second, we will provide a linear programming approach through the notion of relaxed strategies in the same spirit as the works by Kurtz and Stockbridge, which have been recently extended to mean field games in several papers by Bouveret, Dumitrescu, Leutscher and Tankov. Within such a linear programming setting and under some regularity assumptions, we will show existence of an optimal CCE with respect to a fixed criterion for the moderator. Finally, we will propose an equivalent Lagrangian formulation and a primal-dual algorithm to compute an optimal CCE numerically. This talk is based on joint papers with F. Cannerozzi, F. Cartellier, M. Fischer and I. Tzouanas.

Bio: Luciano Campi is currently Full Professor of Probability and Mathematical Statistics at the Department of Mathematics "Federigo Enriques", University of Milan. His recent research focuses on stochastic control, stochastic differential games, mean field games, and their applications to energy markets. Before joining the University of Milan, he held academic positions at the London School of Economics, University Paris 13, and University Paris Dauphine. He earned his PhD in Mathematics from the University of Paris 6 and in Computational Mathematics from the University of Padua. Luciano Campi is an Associate Editor for the IMA Journal of Applied Mathematics and Decisions in Economics and Finance.

October 9, 2025, 1PM-2.30PM (EST) Registration link:

Speaker: Nicole Bäuerle, Karlsruhe Institute of Technology

Title: Competitive portfolio optimization

Abstract: Within a common arbitrage-free semimartingale financial market we consider the problem of determining all Nash equilibrium investment strategies for n agents who try to maximize the expected utility of their relative wealth. The utility function can be rather general here. Exploiting the linearity of the stochastic integral and making use of the classical pricing theory we are able to express all Nash equilibrium investment strategies in terms of the optimal strategies for the classical one agent expected utility problems. We give applications to specific financial markets and compare our results with those given in the literature. A more specific model with price impacts is also discussed. Moreover, we consider the problem of determining all Nash equilibrium investment strategies for n agents who try to maximize the expected utility of their wealth under the constraint that with certain probability the own wealth exceeds a linear combination of the others. We compare the investment strategy to the optimal one without competition. (Joint work with T. Göll)

Bio: Nicole Bäuerle received the Ph.D. degree in mathematics from Ulm University, Ulm, Germany, in 1996. Since 2005, she has been a Professor of probability with the Karlsruhe Institute of Technology, Karlsruhe, Germany. From 2002 to 2005, she was a Professor of insurance mathematics with the University of Hannover, Hannover, Germany. She has authored or coauthored more than 80 papers and a book jointly with Ulrich Rieder on Markov Decision Processes with Applications to Finance. Her research interests include stochastic processes and control with applications to finance, insurance, and stochastic networks. Dr. Bäuerle has served on the editorial board of many journals and is currently Deputy Editor in Chief of the Journal of Applied Probability and an Associate Editor of Statistics and Risk Modeling.

September 11, 2025, 1PM-2.30PM (EST) Registration link and this edition will feature two speakers:

Speaker: Chiara Amorino, Universitat Pompeu Fabra

Title: Minimax rate for multivariate data under componentwise local differential privacy constraints

Abstract: Our research analyses the trade-off between maintaining privacy and preserving statistical accuracy when dealing with multivariate data subject to componentwise local differential privacy (CLDP). Under CLDP, each component of the private data is released through a separate privacy channel. This allows for varying levels of privacy protection for different components or for the privatization of each component by different entities, each with their own distinct privacy policies. It also covers practical situations where it is impossible to privatize all components of the raw data jointly.

We develop general techniques for establishing minimax bounds that quantify the statistical cost of privacy as a function of the privacy levels \alpha_1,…,\alpha_d of the d components. The versatility and efficiency of these techniques are demonstrated through various statistical applications. Specifically, we examine nonparametric density estimation and joint moments estimation under CLDP, providing upper and lower bounds that match up to constant factors, along with an associated data-driven adaptive procedure. We also conduct a detailed analysis of the effective privacy level, exploring how information about a private characteristic of an individual may be inferred from the publicly visible characteristics of the same individual.

Bio: Chiara Amorino is currently an Assistant Professor in the Statistics Group at Universitat Pompeu Fabra in Barcelona, a position she has held since April 2024. In 2025, she was awarded the prestigious Ramón y Cajal Fellowship in Mathematics, a five-year individual grant from the Spanish Ministry of Economy, Industry and Competitiveness. Since 2024, she has also served as Chair of the Bernoulli Young Researchers Committee for Europe.

Before joining UPF, she was a postdoctoral researcher at the University of Luxembourg in the group of Prof. Mark Podolskij. She earned her PhD in Mathematics from Université Paris-Saclay (LaMME) under the supervision of Prof. Arnaud Gloter, defending her thesis in July 2020.

Her research interests include statistical inference for stochastic differential equations, interacting particle systems, Hawkes processes, fractional processes, and local differential privacy.

Speaker: Fayçal Drissi, University of Oxford

Title: Equilibrium Liquidity Provision in Concentrated Liquidity Automated Market Makers

Abstract: Automated market makers (AMMs) with concentrated liquidity (CL) are the most widely used decentralised exchanges, with daily trading volumes around $4 billion. In CL markets, liquidity providers (LPs) strategically choose price ranges to balance fee revenues against adverse selection losses. We develop a model of competition among LPs and characterise the equilibrium distribution of liquidity across ranges. The analysis shows how equilibrium outcomes depend on the number of competing LPs, the ratio of informed to uninformed trading flow, and wealth heterogeneity among liquidity providers. Finally, we examine the role of “noise” liquidity provision and show how it affects equilibrium allocations and execution costs.

Bio: Fayçal Drissi is currently a postdoctoral researcher at the Oxford-Man Institute, University of Oxford. He obtained a Ph.D. in Mathematics from Université Paris 1 Panthéon-Sorbonne in 2023. His thesis focused on the microstructure of traditional electronic markets and decentralised exchanges that use Automated Market Makers (AMMs). Prior to his doctoral studies, he spent five years in the hedge fund industry doing research and development related to systematic trading and global macro.

June 12, 2025, 1PM-2.30PM (EST) Registration link and this edition will feature two speakers:

Speaker: Valentin Tissot-Daguette, Bloomberg

Title: Pathwise Superhedging of Asian Claims

Abstract: The talk unveils pathwise superhedging strategies for convex Asian claims using a dynamic hedge in the underlying and a static position in vanilla options. For an Asian call, where the seller is long the matching vanilla contract, the dynamic hedge may involve the time spent by the asset - or its running average - above the strike. The validity of average-based strategies stems from a mysterious identity relating the Asian call payoff to a strip of binary options across maturities.

The strategies are then tested on synthetic data, where we compare the variance of their P&Ls and hedging turnover. We finally connect these findings with Martingale Optimal Transport and derive robust price bounds for forward start (convex) Asian claims.

Special thanks to Bruno Dupire, Hélyette Geman, Julien Guyon, Bryan Liang, Marcel Nutz, and Nizar Touzi.

Bio: Valentin Tissot-Daguette is a quantitative researcher at Bloomberg. He recently obtained his PhD degree from Princeton University, under the co-supervision of Prof. Mete Soner and Bruno Dupire. Previously, Valentin studied at EPFL and ETH Zurich where he completed a Bachelor's degree in Mathematics and a Master's degree in Financial Engineering. His research interests include exotic derivatives, free boundary problems, and stochastic control.

Speaker: Purba Das, King's College London

Title: Invariance of Stochastic integral with respect to the choice of partitions

Abstract: We study the concept of quadratic variation of a continuous path along a sequence of partitions and its dependence with respect to the choice of the partition sequence. We introduce the concept of quadratic roughness of a path along a partition sequence and show that for Hölder-continuous paths satisfying this roughness condition, the quadratic variation along balanced partitions is invariant with respect to the choice of the partition sequence. We further present several constructions of paths and processes with finite quadratic variation along a refining sequence of partitions and, in particular, study the dependence of quadratic variation with respect to the sequence of partitions for these constructions.

Bio: Purba Das is a Lecturer (Assistant Professor) in Financial Mathematics in the Department of Mathematics at King’s College London. Before KCL, she was a Byrne Research Assistant Professor of Mathematics at the University of Michigan for one year. She completed her DPhil in Mathematics at the University of Oxford under the supervision of Professor Rama Cont.

May 8, 2025, 1PM-2.30PM (EST) Registration link

Speaker: Julio Backhoff, University of Vienna

Title: Of ‘most exciting’ games and the specific relative entropy between martingales

Abstract: The laws of two continuous martingales will typically be singular to each other and hence have infinite relative entropy. But this does not need to happen in discrete time. This suggests defining a new object, the specific relative entropy, as a scaled limit of the relative entropy between the discretized laws of the martingales. This definition goes back to Nina Gantert’s PhD thesis, and in recent time Hans Foellmer has rekindled the study of this object. Independently, this object has made sporadic appearances in finance over the years, for instance in works by Avellaneda et al. and more recently Dolinsky and Cohen.

In this talk we will first discuss the existence of a closed-form formula for the specific relative entropy, depending on the quadratic variation of the involved martingales. Next we will describe an application of this object to prediction markets. Concretely, David Aldous asked in an open question to determine the ‘most exciting’ game, i.e. the prediction market with the highest entropy. With M. Beiglböck we give an answer to this question by solving a stochastic control problem whose cost criterion is the specific relative entropy. Finally we will discuss an application to the ‘most exciting’ game with multiple outcomes, based on joint work with Wang and Zhang, highlighting a novel connection to the field of Monge-Ampere equations.

Bio: Julio Backhoff obtained his PhD in Mathematics from Humboldt-Universität zu Berlin, in 2015, under the supervision of Prof. Ulrich Horst. From 2015 to 2019, he held various postdoc and university assistant positions in the group of Prof. Mathias Beiglböck at the University of Vienna and the Vienna University of Technology. In 2020, he became an assistant professor at the University of Twente. Since 2021, he has been an assistant professor at the University of Vienna (Mathematics Faculty, Mathematical Finance and Probability group). His research lies at the intersection of Mathematical Finance, Stochastic control / analysis, and optimal transport theory. For the most part, he is interested in problems coming from model-free finance and model calibration, with an emphasis on adapted and martingale transport.

April 10, 2025, 1PM-2.30PM (EST) Registration link

Speaker: Sara Svaluto-Ferro, University of Verona

Title: Signature-based models: theory, calibration, and expansions

Abstract: Signature methods provide a non-parametric approach to extracting features from trajectories, offering versatile applications in finance. The structure of signature components enables the use of advanced mathematical tools and the construction of highly general models capable of capturing diverse behaviors.

In this talk, we introduce the concept of the signature and its key properties, illustrating its potential through two financial applications.

The first application focuses on a stochastic volatility model where volatility dynamics are described by linear functions of the (time-extended) signature of a primary process. When this process is of polynomial type, its truncated signature retains this structure, allowing for closed-form expressions of the squared VIX. By incorporating the Brownian motion driving the stock price, both the log-price and the squared VIX can be expressed linearly in terms of the signature of the augmented process, achieving highly accurate calibration results for SPX and VIX options.

The second application examines the local-in-time expansion of a continuous-time process and its conditional moments, including the characteristic function. By leveraging the time-extended Itô signature—composed of iterated integrals of deterministic and stochastic signals (time, multiple correlated Brownian motions, and compound Poisson processes)—we derive automated expansions to any order with explicit coefficients. This provides stochastic representations suitable for asymptotic analysis in the short-time limit.

Bio: Sara Svaluto-Ferro is an Associate Professor at the University of Verona, Department of Economics, specializing in mathematical methods for economics and finance. Previously, she was an Assistant Professor (RTDB) at the same department and a postdoctoral researcher at the University of Vienna in the research group of Prof. C. Cuchiero. She earned her PhD in Mathematics from ETH Zurich under the supervision of Prof. M. Larsson.

Her research focuses on stochastic processes, including affine and polynomial models, stochastic representations of PDEs, and optimization in infinite-dimensional spaces, with applications in financial mathematics, systemic risk, and rough volatility modeling. In the last years she dedicated particular attention to applications of signature processes in finance.

March 20, 2025, 1PM-2.30PM (EST) Registration link

and this edition will feature two speakers:

Speaker: Terry Lyons, University of Oxford

Title: The Mathematics of Complex Streamed Data

Abstract: Multimodal streamed data is essentially different to unimodal streamed data. Consider this:

‘A commuter arrives at a bus stop before the bus’ – they catch it;

‘The bus arrives first’ – they miss it.

These are the same two events, but the order changes everything. Yet most models treat these as identical: ‘A bus and a person arrived’ They ignore timing and relationships.

This simplification isn’t harmless. A timed series gives no information about order within sampling intervals. As a result, the sampling rate has to come from the bottom up if it is to preserve this order information. Rough path theory makes a radical change and describes the stream over an interval using a group element. According to the choice of group it is possible to capture order information and to allow a top down description of the data stream without using essential information about the order of events.

This approach to describing streamed data is important to data science because it reduces the dimension needed for descriptive feature sets and so reduces the size of the data set needed to train. There are numerous prize winning illustrations of the methodology in use and the impact can be measured in the hundreds of millions of US dollars.

Bio: Terry Lyons FLSW FRSE FRS is the Wallis Professor Emeritus and Professor of Mathematics at the University of Oxford, a fellow of St Anne's College, Oxford and a Faculty Fellow at The Alan Turing Institute. He is currently PI of the DataSıg and of the complementary research programme CIMDA-Oxford. He was the President of the London Mathematical Society (2013-2015), the Director (2011-2015) of the Oxford Man Institute of Quantitative Finance and the Director of the Wales Institute of Mathematical and Computational Sciences (2008-2011). He came to Oxford in 2000 having previously been Professor of Mathematics at Imperial College London (1993-2000), and before that he held the Colin Maclaurin Chair at Edinburgh (1985-93).

Professor Lyons’s long-term research interests are all focused on Rough Paths, Stochastic Analysis, and applications – particularly to Finance and more generally to the summarising of large complex data. More specifically, his interests are in developing mathematical tools that can be used to effectively model and describe high dimensional systems that exhibit randomness as well as the complex multimodal data streams that arise in human activity. Professor Lyons is involved in a wide range of problems from pure mathematical ones to questions of efficient numerical calculation. He is, in particular, recognised for developing what is now known as the theory of rough paths.

Speaker: Luhao Zhang, Johns Hopkins University

Title: A Class of Interpretable and Decomposable Multi-period Convex Risk Measures

Abstract: Multi-period risk measures evaluate the risk of a stochastic process by assigning it a scalar value. A desirable property of these measures is dynamic decomposition, which allows the risk evaluation to be expressed as a dynamic program. However, many widely used risk measures, such as Conditional Value-at-Risk, do not possess this property. In this work, we introduce a novel class of multi-period convex risk measures that do admit dynamic decomposition.

Our proposed risk measure evaluates the worst-case expectation of a random outcome across all possible stochastic processes, penalized by their deviations from a nominal process in terms of both the likelihood ratio and the outcome. We show that this risk measure can be reformulated as a dynamic program, where, at each time period, it assesses the worst-case expectation of future costs, adjusting by reweighting and relocating the conditional nominal distribution. This recursive structure enables more efficient computation and clearer interpretation of risk over multiple periods.

Bio: Luhao Zhang is an assistant professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University. Before joining JHU, she was a postdoctoral research scientist in the Department of Industrial Engineering and Operations Research at Columbia University from 2023 to 2024. She completed her Ph.D. in Mathematics at the University of Texas at Austin in 2023.

Her research lies on interdisciplinary topics that integrate stochastic analysis, mathematical finance, and robust optimization, with an emphasis on how to exploit information optimally for decision-making in stochastic and uncertain environments from modelling and quantitative aspects. Another recent research interest of hers is the mathematical foundation of generative AI, human-AI interactions, and continuous-time reinforcement learning.

December 12, 2024, 1PM-2PM (EST) Registration link

Speaker:Jose Blanchet, Stanford University

Title: Inference in Stochastic Optimization with Heavy Tailed Input

Abstract: We will start the talk by discussing empirical evidence from a wide range of areas (including insurance, health care, machine learning, among others) suggesting that often infinite variance models are well-suited for inference, particularly in online data-driven decision making. We will argue that infinite variance estimators can be considered appropriate depending on easy-to-monitor features of historical data and on the spatial and temporal scales over which an online algorithm will be deployed (even if the underlying dynamics have finite variance gradients “in theory”). We will then discuss inference tools that can be applied to monitor the quality of solutions of infinite-variance stochastic gradient descent (SGD) based on several asymptotic statistics. Our results extend classical finite-variance weak-convergence analysis of SGD and state-of-the-art infinite variance asymptotic statistics derived under homogeneity conditions which limit the applicability of SGD in typical online optimization tasks. Based on joint work with Aleks Mijatovic, Wenhao Yang.

Bio: Jose Blanchet is a faculty member in the Management Science and Engineering Department at Stanford University – where he earned his Ph.D. in 2004. Prior to joining the Stanford faculty, Jose was a professor in the IEOR and Statistics Departments at Columbia University (2008-2017) and before that he was faculty member in the Statistics Department at Harvard University (2004-2008). Jose is a recipient of the 2009 Best Publication Award given by the INFORMS Applied Probability Society and of the 2010 Erlang Prize. He also received a PECASE award given by NSF in 2010. He worked as an analyst in Protego Financial Advisors, a leading investment bank in Mexico. He has research interests in applied probability and Monte Carlo methods. He serves in the editorial board of ALEA, Advances in Applied Probability, Extremes, Insurance: Mathematics and Economics, Journal of Applied Probability, Mathematics of Operations Research, and Stochastic Systems.

November 14, 2024, 1PM-2PM (EST) Registration link

Speaker:Ben Hambly, University of Oxford

Title: Systemic risk, endogenous contagion and McKean-Vlasov control

Abstract: We consider some particle system models for systemic risk. The particles represent the health of financial institutions and we incorporate common noise and contagion into their dynamics. Defaults within the system reduce the financial health of other institutions, causing contagion. By taking a mean field limit we derive a McKean-Vlasov equation for the financial system as a whole. The task of a central planner, who wishes to control the system to prevent systemic events at minimal cost, leads to a novel McKean-Vlasov control problem. We discuss the mathematical issues and illustrate the results numerically.

Bio: Ben Hambly received his PhD in 1990 from the University of Cambridge and held lectureships at the Universities of Edinburgh and Bristol before moving to Oxford in 2000. He has interests in stochastic PDEs, rough paths, random processes in random and fractal environments, reinforcement learning ,modelling order books, systemic risk and electricity markets.

October 10, 2024, 1PM-2PM (EST)' Registration link

Speaker: Maxim Bichuch, University at Buffalo

Title: A Deep Learning Scheme for Solving Fully Nonlinear Partial Differential Equation

Abstract: We study the convergence of a deep learning algorithm applied to a general class of fully nonlinear second order Partial Differential Equations. By using a suitable finite difference approximation to the loss function of the deep learning scheme we show the convergence of the numerical solution to the unique viscosity solution. We apply our results and illustrate this convergence to the finite horizon optimal investment problem with proportional transaction costs in single and multi-asset settings.

Bio: Maxim Bichuch holds a M.S. from NYU and a Ph.D. from CMU both in Financial Mathematics. He has been a Postdoctoral Research Associate & Lecturer in the ORFE department in Princeton, and an Assistant Professor at WPI and JHU, before joining the department of Mathematics at UB. Prior to obtaining his Ph.D. He has also gained corporate experience working for Citigroup and Bear Stearns. His research interests include optimal portfolio selection, optimal investment and consumption, optimal control with transaction costs, viscosity solutions, stochastic volatility, credit, funding and counterparty risks, and most recently electricity markets, machine learning, decentralized finance and fintech.

June 13, 2024, 1PM-2PM (EST)

Speaker: Miklós Rásonyi, HUN-REN Alfréd Rényi Institute of Mathematics

Title: Portolio choice for exponential investors when prices are mean-reverting

Abstract: Several asset classes show mean-reverting features, e.g. commodities, commodity futures, long-term safe assets (gold). We investigate the portfolio choice problem for investors with exponential utilities (=high risk aversion) as the investment horizon T tends to infinity. It turns out that the optimal equivalent safe rate grows in a superlinear way, depending on the strength of the mean-reversion effect. We cannot find the exact optimisers but construct a family of simple, explicit strategies that are optimal asymptotically (they generate equivalent safe rates of the optimal order). Interestingly, the presence or absence of a drift leads to entirely different conclusions, the nonzero drift case spectacularly outperforming the driftless one. Time permitting, we also review some

[edit] Past steering committees

2021-2022

\quad Agostino Capponi (SIAG/FME Chair, Columbia University)

\quad Igor Cialenco (SIAG/FME Program Director, Illinois Institute of Technology)

\quad Sebastian Jaimungal (University of Toronto)

\quad Ronnie Sircar (Princeton University)

Retrieved from "http://wiki.siam.org/siag-fm/index.php/Current_events"

 We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is
-'''February 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
+'''June 11, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
-''Speaker:'' [https://www.wias-berlin.de/people/bayerc/ Christian Bayer], WIAS Berlin
+''Speaker:'' [https://sites.google.com/site/sergiopulidonino/home Sergio Pulido], ensIIE
-[[Image:christian_bayer.jpg|200px|Image: 200 pixels]]
+[[Image:sergio_pulido.jpeg|200px|Image: 200 pixels]]
-''Title:'' Global and local regression: a signature approach with applications
+''Title:''  Boundary attainment conditions for stochastic Volterra equations
-''Abstract:'' The path signature is a powerful tool for solving regression problems on path space, i.e., for computing conditional expectations $\mathbb{E}[Y | X]$ when the random variable $X$ is a stochastic process -- or a time-series. We provide new theoretical convergence guarantees for two different, complementary approaches to regression using signature methods. In the context of global regression, we show that linear functionals of the robust signature are universal in the $L^p$ sense in a wide class of examples. In addition, we present a local regression method based on signature semi-metrics, and show universality as well as rates of convergence. Based on joint works with Davit Gogolashvili, Luca Pelizzari, and John Schoenmakers.
+''Abstract:'' In this presentation, I will discuss boundary attainment conditions for one-dimensional stochastic Volterra equations (SVEs) of convolution type. In the first part of the talk, I will present an Osgood-type test for explosion to infinity of SVEs driven by additive noise, featuring kernels from a family that includes the fractional kernel. I will also investigate stability results for explosion times with respect to the kernels, including the case of an Euler-Maruyama approximation scheme. In the second part, I will present a Feller-type test that establishes, on a general open interval of the real line, necessary and sufficient conditions for boundary attainment of solutions to SVEs with possibly multiplicative noise. Here, I will consider dynamics governed by nonsingular kernels, which preserve the semimartingale property of the processes while introducing memory effects through a path-dependent drift. I will also show an application of these results to the Volterra square-root diffusion. The talk is based on joint works with Alessandro Bondi.
+''Bio:''  Sergio Pulido is an Associate Professor (Maître de conférences HDR) at the École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ensIIE) and a permanent member of the Laboratoire de Mathématiques et Modélisation d'Évry (LaMME), a joint research unit of the Centre National de la Recherche Scientifique (CNRS), Université Évry Paris-Saclay, and the ensIIE.
+He currently serves as Head of the International Relations Office at the ensIIE and co-manages the M1 in Applied Mathematics (Évry site) at Université Paris-Saclay. He is also an Associate Researcher in the Mathematical Finance group at the Centre de Mathématiques Appliquées (CMAP) of École Polytechnique.
+Before joining ensIIE, he was a Postdoctoral Researcher at the Swissquote Chair in Quantitative Finance at the École Polytechnique Fédérale de Lausanne (EPFL), and a Postdoctoral Associate in Applied Probability and Mathematical Finance at Carnegie Mellon University. He received a PhD in Mathematics from Cornell University, an M.S. in Mathematics from Universidad de los Andes, and a B.S. in Mathematics from Universidad Nacional de Colombia.
+Sergio Pulido’s recent research focuses on stochastic models with rough trajectories and their applications in finance. From a more theoretical perspective, he has studied stochastic processes solving stochastic convolution equations, namely Stochastic Volterra Equations (SVEs).
+----
+----
+=== Past Talks ===
+'''May 14, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
+''Speaker:'' [https://sites.google.com/site/ruimenghu1/ Ruimeng Hu], University of California, Santa Barbara
+[[Image:ruimeng_hu.jpeg|200px|Image: 200 pixels]]
+''Title:'' Machine Learning for Stochastic Control and Games: From Foundations to Mean-Field Learning
+''Abstract:'' Machine learning has become an increasingly useful tool for solving high-dimensional stochastic control and game problems that are difficult to handle with classical numerical methods. In this talk, I will begin with a general overview of several learning-based approaches for stochastic control and games, including direct policy parameterization, PDE-based methods, and BSDE-based methods, and discuss how these ideas extend to multi-agent and mean-field settings. I will then focus on recent joint work on a new learning framework for mean-field games, called mean-field actor-critic flow. The method combines actor-critic ideas from reinforcement learning with an optimal transport-based update of the population distribution, leading to a coupled learning dynamic for the value function, policy, and mean-field law. I will describe the main algorithmic ideas, discuss a global exponential convergence result under suitable time-scale separation, and present numerical examples illustrating the method.
+''Bio:''  Ruimeng Hu is an Associate Professor in the Department of Mathematics and the Department of Statistics and Applied Probability at the University of California, Santa Barbara. Her research interests include stochastic control, mean-field games, machine learning, and their applications in finance, economics, and multi-agent systems. Before joining UCSB, she was a Term Assistant Professor in the Department of Industrial Engineering and Operations Research at Columbia University.  Her research is supported by grants from the National Science Foundation and the Office of Naval Research. She also serves as an Associate Editor for SIAM Journal on Financial Mathematics and Digital Finance.
+----
+'''April 9, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
+''Speaker:'' [https://yufei-zhang.github.io/ Yufei Zhang], Imperial College London
+[[Image:yufei_zhang.jpeg|200px|Image: 200 pixels]]
+''Title:'' An alpha-potential game framework for dynamic N-player games
+''Abstract:'' Game theory has a long history, yet identifying Nash equilibria in dynamic non-cooperative games remains a fundamental challenge with significant computational and conceptual complexity. Over the past decade, mean field game theory has emerged as a pivotal framework, offering important theoretical insights and computational advances for the analysis of large-scale stochastic games. However, mean field games require homogeneity and weak interactions among players and focus only on the limiting behavior when the number of players goes to infinity.
+In this talk, we present a new paradigm for dynamic N-player games, called alpha-potential games, where the change of a player's objective function resulting from a unilateral deviation of her strategy is equal to the change of an alpha-potential function up to an error alpha. Within this framework, the problem of computing approximate Nash equilibria reduces to a stochastic control problem for the alpha-potential function, significantly simplifying both analysis and computation. The parameter alpha also reveals important structural properties of the game, such as the population size, the intensity of player interactions, and the degree of heterogeneity across players. We will discuss through simple examples some recent theoretical and algorithmic developments, along with a few open problems for this new game framework.
+''Bio:''  Yufei Zhang is an Associate Professor in Mathematical Finance and Machine Learning in the Department of Mathematics at Imperial College London, where he also serves as Co-Director of the MSc in Mathematics and Finance program. Before joining Imperial, he was an Assistant Professor in the Department of Statistics at the London School of Economics and Political Science. He earned his PhD in Mathematics from the University of Oxford in 2021. Yufei was awarded the J.P. MorganChase Faculty Research Award in 2025 for his work on the mathematics of artificial intelligence.
+Yufei’s research lies at the intersection of stochastic control, game theory, machine learning, and mathematical finance, with a particular emphasis on developing theoretical foundations and algorithmic frameworks for complex decision-making in dynamic and uncertain environments.
 ''Speaker:'' [https://sites.google.com/view/abijabereduardo/ Eduardo Abi Jaber], Ecole Polytechnique
-[[Image:eduardo_abi_jaber.jpg|200px|Image: 200 pixels]]
+[[Image:eduardo_abijaber.jpg|200px|Image: 200 pixels]]
 ''Abstract:'' We explore the interplay between path-signatures, memory, and stationarity, highlighting their implications for machine learning, representation of stochastic processes and applications in mathematical finance. In a first part, we provide explicit series expansions to certain stochastic path-dependent integral equations in terms of the path signature of the time augmented driving Brownian motion. Our framework encompasses a large class of stochastic linear Volterra and delay equations and in particular the fractional Brownian motion with a Hurst index H in (0, 1). Our expressions allow to disentangle an infinite dimensional Markovian structure. In addition they open the door to: (i) straightforward and simple approximation schemes that we illustrate numerically, (ii) representations of certain Fourier-Laplace transforms in terms of a non-standard infinite dimensional Riccati equation with important applications for pricing and hedging in quantitative finance. In a second part, we introduce a time-invariant version of the signature: the fading-memory signature, with powerful algebraic, analytic and probabilistic properties and applications to learning stationary relationships in time series. This is based on joint works with Paul Gassiat, Louis-Amand Gérard, Yuxing Huang, Dimitri Sotnikov.
+''Bio:''  Eduardo Abi Jaber is a Professor of Applied Mathematics at Ecole Polytechnique. He defended his Habilitation à Diriger des Recherches in 2024 and his PhD in 2018.
+His research investigates the role of memory in quantitative finance, advancing the mathematical foundations of sophisticated tools such as Volterra processes and path signatures. Beyond theory, his work translates into practical solutions to key challenges in the field, including volatility modeling and portfolio optimization. Positioned at the crossroads of mathematics and finance, his research combines rigorous analysis, advanced modeling, bespoke numerical methods, and systematic validation against real-world data.
+Author of more than 40 papers, with publications in leading journals in applied probability and quantitative finance, Eduardo’s contributions have been recognized with several prestigious awards, including the Amies Prize for the best CIFRE PhD thesis in applied mathematics (2019) and the Junior Scholar Award of the Bachelier Finance Society (2018).  He has delivered over 100 invited talks worldwide. He serves as an Associate Editor for Mathematical Finance and the International Journal of Theoretical and Applied Finance, and co-organizes the internationally recognized Bachelier Seminar in Paris. Over the years, he has led a research group comprising more than 10 PhD students and postdoctoral researchers.
 ----
+'''February 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
+''Speaker:'' [https://www.wias-berlin.de/people/bayerc/ Christian Bayer], WIAS Berlin
+[[Image:christian_bayer.jpg|300px|Image: 300 pixels]]
+''Title:'' Global and local regression: a signature approach with applications
+''Abstract:'' The path signature is a powerful tool for solving regression problems on path space, i.e., for computing conditional expectations $\mathbb{E}[Y | X]$ when the random variable $X$ is a stochastic process -- or a time-series. We provide new theoretical convergence guarantees for two different, complementary approaches to regression using signature methods. In the context of global regression, we show that linear functionals of the robust signature are universal in the $L^p$ sense in a wide class of examples. In addition, we present a local regression method based on signature semi-metrics, and show universality as well as rates of convergence. Based on joint works with Davit Gogolashvili, Luca Pelizzari, and John Schoenmakers.
+''Bio:'' Christian Bayer obtained his PhD at the TU Vienna on numerical methods for stochastic differential equations. He is working as a Senior Researcher at the Weierstrass Institute of Applied Analysis and Stochastics in Berlin. His research interests are in rough volatility, computational finance, stochastic numerics, and stochastic optimal control.
 ----
-=== Past Talks ===
 '''December 11, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:

Current events

From SIAG-FM

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Contents

[edit] SIAG/FME virtual seminars series

[edit] Forthcoming Talks

[edit] Past Talks

[edit] Past steering committees

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