Current events - Revision history

Ccuchiero: /* Past Talks */

2026-05-18T10:16:22Z

Past Talks

←Older revision		Revision as of 10:16, 18 May 2026
Line 54:		Line 54:

	=== Past Talks ===		=== 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]:		'''April 9, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:

Ccuchiero: /* Forthcoming Talks */

2026-05-18T10:15:57Z

Forthcoming Talks

←Older revision		Revision as of 10:15, 18 May 2026
Line 28:
	We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is		We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is

-	'''May 14, 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://sites.google.com/site/ruimenghu1/ Ruimeng Hu], University of California, Santa Barbara	+	''Speaker:'' [https://sites.google.com/site/sergiopulidonino/home Sergio Pulido], ensIIE

-	[[Image:ruimeng_hu.jpeg\|200px\|Image: 200 pixels]]	+	[[Image:sergio_pulido.jpeg\|200px\|Image: 200 pixels]]


-	''Title:'' Machine Learning for Stochastic Control and Games: From Foundations to Mean-Field Learning	+	''Title:'' Boundary attainment conditions for stochastic Volterra equations


-	''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.	+	''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:'' 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.	+	''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).

-	'''June 11, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
-
-	''Speaker:'' [https://sites.google.com/site/sergiopulidonino/home Sergio Pulido], ensIIE
-
-	Details TBA.

	----		----

Ccuchiero: /* Forthcoming Talks */

2026-04-21T21:02:56Z

Forthcoming Talks

←Older revision		Revision as of 21:02, 21 April 2026
Line 28:		Line 28:
	We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is		We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is

-	'''April 9, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:	+	'''May 14, 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	+	''Speaker:'' [https://sites.google.com/site/ruimenghu1/ Ruimeng Hu], University of California, Santa Barbara

-	[[Image:yufei_zhang.jpeg\|200px\|Image: 200 pixels]]	+	[[Image:ruimeng_hu.jpeg\|200px\|Image: 200 pixels]]


-	''Title:'' An alpha-potential game framework for dynamic N-player games	+	''Title:'' Machine Learning for Stochastic Control and Games: From Foundations to Mean-Field Learning


-	''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.	+	''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.

-	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:'' 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.

-	''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.	+	----

		+
		+	'''June 11, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
		+
		+	''Speaker:'' [https://sites.google.com/site/sergiopulidonino/home Sergio Pulido], ensIIE
		+
		+	Details TBA.

	----		----

Ccuchiero: /* Past Talks */

2026-04-21T20:58:19Z

Past Talks

←Older revision		Revision as of 20:58, 21 April 2026
Line 52:		Line 52:

	=== Past Talks ===		=== Past Talks ===
		+
		+	'''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.
		+
		+
		+	----
		+

	'''March 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:		'''March 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:

Ccuchiero: /* Forthcoming Talks */

2026-03-31T22:50:02Z

Forthcoming Talks

←Older revision		Revision as of 22:50, 31 March 2026
Line 28:		Line 28:
	We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is		We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is

-	'''March 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:	+	'''April 9, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:

-	''Speaker:'' [https://sites.google.com/view/abijabereduardo/ Eduardo Abi Jaber], Ecole Polytechnique	+	''Speaker:'' [https://yufei-zhang.github.io/ Yufei Zhang], Imperial College London

-	[[Image:eduardo_abijaber.jpg\|200px\|Image: 200 pixels]]	+	[[Image:yufei_zhang.jpeg\|200px\|Image: 200 pixels]]


-	''Title:'' Path-Signatures: Memory and Stationarity	+	''Title:'' An alpha-potential game framework for dynamic N-player games


-	''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.	+	''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:'' 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.	+	''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.

-	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.	+	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.

Ccuchiero: /* Past Talks */

2026-03-31T22:45:31Z

Past Talks

←Older revision		Revision as of 22:45, 31 March 2026
Line 52:		Line 52:

	=== Past Talks ===		=== Past Talks ===
		+
		+	'''March 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
		+
		+	''Speaker:'' [https://sites.google.com/view/abijabereduardo/ Eduardo Abi Jaber], Ecole Polytechnique
		+
		+	[[Image:eduardo_abijaber.jpg\|200px\|Image: 200 pixels]]
		+
		+
		+	''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)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:		'''February 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:

Ccuchiero: /* Forthcoming Talks */

2026-02-16T16:31:38Z

Forthcoming Talks

←Older revision		Revision as of 16:31, 16 February 2026
Line 32:		Line 32:
	''Speaker:'' [https://sites.google.com/view/abijabereduardo/ Eduardo Abi Jaber], Ecole Polytechnique		''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]]

Ccuchiero: /* Forthcoming Talks */

2026-02-16T16:28:30Z

Forthcoming Talks

←Older revision		Revision as of 16:28, 16 February 2026
Line 39:		Line 39:

	''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.		''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.

Ccuchiero: /* Past Talks */

2026-02-16T16:27:15Z

Past Talks

←Older revision		Revision as of 16:27, 16 February 2026
Line 45:		Line 45:

	=== Past Talks ===		=== Past Talks ===
		+
		+	'''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.
		+
		+
		+	----
		+

	'''December 11, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:		'''December 11, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:

Ccuchiero: /* Forthcoming Talks */

2026-02-16T16:26:56Z

Forthcoming Talks

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	We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is		We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is
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-	'''February 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
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-	''Speaker:'' [https://www.wias-berlin.de/people/bayerc/ Christian Bayer], WIAS Berlin
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-	[[Image:christian_bayer.jpg\|300px\|Image: 300 pixels]]
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-	''Title:'' Global and local regression: a signature approach with applications
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-	''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.
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-	''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.
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	'''March 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:		'''March 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]: