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    "149579-21906721":
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        "datetime_modified":"20260819T202729",
        "datetime_start":"20261118T160000",
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        "date_start":"2026-11-18",
        "date_end":"2026-11-18",
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        "time_zone":"America\/Detroit",
        "event_title":"A New Approach for the Continuous Time Kyle-Back Strategic Insider Equilibrium Problem",
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        "combined_title":"A New Approach for the Continuous Time Kyle-Back Strategic Insider Equilibrium Problem: Bixing Qiao, UM",
        "event_subtitle":"Bixing Qiao, UM",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"In this talk, we consider a continuous-time Kyle-Back model which is a game between an insider and a market maker. The existing literature typically focuses on constructing equilibria with a PDE approach, which requires certain Markovian structures. We characterize all equilibria through a coupled system of forward-backward SDEs. In particular, when the time duration is small, we show that the FBSDE is well-posed, and therefore the game has a unique equilibrium. Moreover, this unique equilibrium may be non-Markovian and thus not attainable via the PDE approach. We next study the set value of the game, which roughly speaking is the set of insider's values over all equilibria and thus is by nature unique. Finally, we characterize the set value through a level set of a certain standard HJB equation.\nIn the second part of the talk, we apply the new approach to the Kyle-Back model with dynamic legal risk and large numbers of noise traders. In this setting, the insider chooses a strategy that conceals his identity within a large volume of surrounding trades and concentrates on medium-sized trades.  We establish an intensity-based mathematical framework for explaining the interconnections between insider trading and stealth trading. When the number of noise traders becomes large, the price impact of the insider asymptotically vanishes, and consequently the stochastic game becomes deterministic optimizations, which also carry implications for regulatory investigations and sanctions.\n\nThe results above include the joint work with Weixuan Xia and with Jianfeng Zhang.",
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        "time_zone":"America\/Detroit",
        "event_title":"Inverse problems over probability measure space",
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        "combined_title":"Inverse problems over probability measure space: Qin Li, University of Wisconsin-Madison",
        "event_subtitle":"Qin Li, University of Wisconsin-Madison",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"Inverse problems are ubiquitous. Traditionally, the goal is to infer an unknown vector or function. But what if the unknown is a probability measure? Seeking a measure that generates data consistent with given observations leads to an optimization problem over probability space. However, the complex geometric nature of this space prevents the direct use of standard arguments and solvers. We unravel some of the surprises that emerge in this setting and discuss potential solutions.",
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    {
        "datetime_modified":"20260727T104405",
        "datetime_start":"20261209T160000",
        "datetime_end":"20261209T170000",
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        "date_start":"2026-12-09",
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        "time_start":"16:00:00",
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        "time_zone":"America\/Detroit",
        "event_title":"THE HEREDITARY LAWS OF LARGE NUMBERS",
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        "combined_title":"THE HEREDITARY LAWS OF LARGE NUMBERS: Ioannis Karatzas, Columbia University",
        "event_subtitle":"Ioannis Karatzas, Columbia University",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"The celebrated theorem of Komlos (1967) establishes L^1-boundedness as a sufficient condition for a sequence of measurable functions on a probability space to contain a subsequence along which, and along whose every further subsequence (\u201chereditarily\u201d), the Cesaro averages converge to a \u201crandomized mean\u201d in the spirit of the Strong law of Large Numbers. We provide conditions not only sufficient, but also necessary, for this result, as well as for the hereditary analogues of the Weak Law of Large Numbers, of the Hsu-Robbins-Erdos Law of Large Numbers, and of the Law of the Iterated Logarithm. \n\nJoint work with I. Berkes (Budapest) and W. Schachermayer (Vienna).",
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        "tags":["Mathematics","Statistics"],
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        "time_zone":"America\/Detroit",
        "event_title":"TBA",
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        "combined_title":"TBA: Dohyeon Kim, Caltech",
        "event_subtitle":"Dohyeon Kim, Caltech",
        "event_type":"Workshop \/ Seminar",
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        "description":"TBA",
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        "event_title":"Regularization by Common Noise for Mean Field Games and Mean Field Control",
        "occurrence_title":"",
        "combined_title":"Regularization by Common Noise for Mean Field Games and Mean Field Control: Lukas Wessels, Universit\u00e9 C\u00f4te d\u2019Azur",
        "event_subtitle":"Lukas Wessels, Universit\u00e9 C\u00f4te d\u2019Azur",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"A fundamental challenge in the theory of mean field games (MFGs) is that equilibria are generally non-unique in the absence of monotonicity assumptions. In this talk, we establish that an infinite-dimensional multiplicative common noise restores uniqueness for generic MFGs in one spatial dimension. Furthermore, by exploiting the connection between mean field control (MFC) and potential MFGs, we show that this common noise regularizes the MFC value function. Based on this regularity, we then construct optimal feedback controls for the MFC problem. This talk is based on joint work with F. Delarue.",
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        "website":"https:\/\/lukaswessels.org\/",
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        "datetime_modified":"20260807T120756",
        "datetime_start":"20270217T160000",
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        "date_start":"2027-02-17",
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        "time_zone":"America\/Detroit",
        "event_title":"The optimal rate of convergence in mean field control",
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        "combined_title":"The optimal rate of convergence in mean field control: Sebatian Munoz, UCLA",
        "event_subtitle":"Sebatian Munoz, UCLA",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"I will discuss the sharp rate of convergence of the value functions of N-particle stochastic control problems to their mean field limit, for merely Lipschitz mean field costs. For d \u2265 2, the optimal rate is that of the empirical measures of i.i.d. samples in the 1-Wasserstein distance, as conjectured by Daudin, Delarue, and Jackson, and it persists with additive common noise. In dimension one, this benchmark can surprisingly be beaten: cooperating particles outperform independent samples, and the optimal exponent is 4\/7, strictly between the accuracy of independent sampling (N^{-1\/2}) and that of quantization (N^{-1}). The proofs rely on a new control-theoretic technique of recoupled shadow flows and, in dimension one, on a Gibbs law implementing the cooperation and a Schr\u00f6dinger ground-state estimate.",
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        "combined_title":"TBA: Tom Bielecki",
        "event_subtitle":"Tom Bielecki",
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        "combined_title":"TBA: Jose Blanchet, Stanford",
        "event_subtitle":"Jose Blanchet, Stanford",
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        "event_title":"Ph.D. Defense (Title TBA)",
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        "combined_title":"Ph.D. Defense (Title TBA): Hongyi Zhou, University of Michigan",
        "event_subtitle":"Hongyi Zhou, University of Michigan",
        "event_type":"Workshop \/ Seminar",
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        "description":"Title and abstract to be announced.",
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