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        "event_title":"Algebraic Geometry Seminar:  Nontautological Cycles on Moduli Spaces of Smooth Curves",
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        "combined_title":"Algebraic Geometry Seminar:  Nontautological Cycles on Moduli Spaces of Smooth Curves: Emily Clader (San Francisco State University)",
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        "description":"The cohomology of the moduli space of stable curves has been widely studied, but in general, understanding the full cohomology ring of this space is too much to ask.  Instead, one generally settles for studying the tautological ring, a subring of the cohomology that is simultaneously tractable to study and yet rich enough to contain most cohomology classes of geometric interest.  The first known example of an algebraic cohomology class that is *not* tautological was discovered by Graber and Pandharipande, in work that was later significantly generalized by van Zelm to produce an infinite family of non-tautological classes on the moduli space of stable curves.  A similar study can be undertaken on the moduli space of smooth curves, but in this case, almost no non-tautological classes were previously known. I will report on joint work with V. Arena, S. Canning, R. Haburcak, A. Li, S.C. Mok, and C. Tamborini (from the 2023 AGNES Summer School), in which we produce non-tautological algebraic classes on the moduli space of smooth curves in an infinite family of cases, including on M_g for all g>15.",
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        "event_title":"Adaptive Optimal Market Making Strategies with Inventory Liquidation Cost",
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        "combined_title":"Adaptive Optimal Market Making Strategies with Inventory Liquidation Cost: Jose Figureoa-Lopez\/ WashU",
        "event_subtitle":"Jose Figureoa-Lopez\/ WashU",
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        "description":"A novel high-frequency market-making approach in discrete time is proposed that admits closed-form solutions. By taking advantage of demand functions that are linear in the quoted bid and ask spreads with random coefficients, we model the variability of the partial filling of limit orders posted in a limit order book (LOB). The most important feature of our optimal placement strategy is that it can react or adapt to the behavior of market orders online. Using LOB data, we train our model and reproduce the anticipated final profit and loss of the optimal strategy on a given testing date using real LOB data. Our adaptive optimal strategies outperform the non-adaptive strategy and those that quote limit orders at a fixed distance from the midprice. We proceed to explore other extensions of the proposed approach.",
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        "event_title":"Baseball vs Oakland",
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        "description":"Baseball vs Oakland",
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        "building_name":"Ray Fisher Baseball Stadium",
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        "location_name":"Ray Fisher Baseball Stadium",
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        "cost":"See MGoblue.com",
        "tags":["Athletics","Athletics - Baseball"],
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