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        "time_zone":"America\/Detroit",
        "event_title":"Digital Accessibility Office Hours",
        "occurrence_title":"",
        "combined_title":"Digital Accessibility Office Hours: Co-hosted by ITS Digital Accessibility and the Disability Equity Office",
        "event_subtitle":"Co-hosted by ITS Digital Accessibility and the Disability Equity Office",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"Got questions about digital accessibility, Title II compliance, or how to make digital content accessible? \n\n-Come to virtual drop-in office hours!\n-Every 2nd and 4th Friday of the month\n-1:30 - 3:00pm \n-Zoom Meeting ID 935 9909 5960\n-Digital accessibility experts available to help you\n\nOpen to everyone from all U-M campuses (Ann Arbor, Dearborn, Flint, Michigan Medicine).\n\nCan\u2019t make it to Office Hours but have a question? Contact us (https:\/\/accessibility.umich.edu\/contact-services)!\n\nIf you need accommodations to participate in office hours, let us know by emailing ADAcoordinator@umich.edu.",
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        "date_end":"2026-10-23",
        "time_start":"14:00:00",
        "time_end":"14:45:00",
        "time_zone":"America\/Detroit",
        "event_title":"Tales of the Maya Skies",
        "occurrence_title":"",
        "combined_title":"Tales of the Maya Skies",
        "event_subtitle":"",
        "event_type":"Film Screening",
        "event_type_id":"11",
        "description":"Tales of the Maya Skies immerses viewers in the wonders of Maya science, cosmology and myth. This beautifully illustrated story takes us back in time to the jungles of Mexico to discover how Maya scholars developed a sophisticated understanding of astronomy, architecture, and mathematics that enabled them to predict solstices, solar eclipses, weather patterns and planetary movements.",
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        "building_name":"Museum of Natural History",
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        "room":"Planetarium & Dome Theater",
        "location_name":"Museum of Natural History",
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        "cost":"Tickets are $9 and available in the museum store on the day of the show.",
        "tags":["Space","Science","natural history museum","Museum"],
        "website":"https:\/\/lsa.umich.edu\/ummnh\/visitors\/planetarium-dome-theater.html",
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        "time_end":"16:00:00",
        "time_zone":"America\/Detroit",
        "event_title":"Topics in Classical Intersectionalities",
        "occurrence_title":"Topics in Classical Intersectionalities (TiCI) October Meeting",
        "combined_title":"Topics in Classical Intersectionalities: Topics in Classical Intersectionalities October Meeting: Topics in Classical Intersectionalities (TiCI) October Meeting",
        "event_subtitle":"Topics in Classical Intersectionalities October Meeting",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"October Meeting for (New) Topics in Classical Intersectionalities RIW.",
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                "guid":"153100-21915098@events.umich.edu",
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        "tags":["Sessions"],
        "website":"https:\/\/sessions.studentlife.umich.edu\/p\/track\/18151",
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                "group_id":"4791",
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    {
        "datetime_modified":"20261002T131533",
        "datetime_start":"20261023T143000",
        "datetime_end":"20261023T163000",
        "has_end_time":1,
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        "time_start":"14:30:00",
        "time_end":"16:30:00",
        "time_zone":"America\/Detroit",
        "event_title":"Online Learning and Decision-Making for Dynamic Pricing and Performative Prediction",
        "occurrence_title":"",
        "combined_title":"Online Learning and Decision-Making for Dynamic Pricing and Performative Prediction: Daniele Bracale",
        "event_subtitle":"Daniele Bracale",
        "event_type":"Lecture \/ Discussion",
        "event_type_id":"13",
        "description":"Many modern decision systems are not passive observers of the world they measure: the decisions they make change the data they subsequently receive. For example, a seller that posts a price influences the decisions of potential buyers, and hence the sales data it goes on to col-\nlect; a lender that deploys a credit-scoring rule changes the nature of the loan applications it receives. In such settings, the classical separation\nbetween estimation and decision-making breaks down, and statistical guarantees must be derived jointly with the policy that generates the\ndata. This dissertation develops estimation theory and online algorithms for two such feedback settings --- dynamic pricing and performative\nprediction --- with a unifying theme: shape-constrained and microfounded structure can replace the tuning parameters, smoothness assump-\ntions, and parametric models on which existing methods rely.\nThe first part concerns dynamic pricing. We first consider a monopolistic setting with censored demand, in which the seller posts a price and\nobserves only whether a sale occurred. In the linear valuation model, the customer\u2019s valuation is linear in the features of the item offered,\nup to market noise with unknown distribution F. Existing methods estimate F by kernel smoothing, bandit techniques, or upper confidence\nbounds; all of them impose Lipschitz or stronger conditions on F, and all of them leave the analyst to choose bandwidths, confidence widths,\nor regularization parameters. We instead exploit the fact that F is monotone and estimate it by isotonic regression. The resulting policy car-\nries no tuning parameters whatsoever, assumes only that F is H\u00f6lder continuous, and attains sublinear regret. It also outperforms existing\nmethods in simulations and on transaction data from Welltower Inc. We then move to a competitive setting in which N sellers post prices\nover T periods, and each seller\u2019s demand follows a single-index model with an unknown monotone, s-concave link. We prove existence and\nuniqueness of the Nash equilibrium, and give a semiparametric least-squares policy under which prices converge to the Nash equilibrium,\nwhile each seller incurs sublinear regret.\nThe second part concerns performative prediction, which involves a learner and a population of agents whose actions follow a distribution\nP. The learner deploys a model M that shifts this distribution from P to P(M), thereby changing the data the learner observes; the map P(M)\nis called the distribution map. Much of the literature assumes that the distribution map is known, whereas we learn it from agent-response\ndata. We study two settings: a discrete-action framework, in which agents choose from a finite set of alternatives, and a continuous-action\nframework, in which an action may be any real number. For discrete actions, we give a nonparametric estimator of the distribution map\ntogether with an exploration--exploitation scheme that we show to be effective in minimizing the performative risk. For continuous actions,\nwe model agents\u2019 responses as a cost-adjusted utility maximization problem and propose an estimator of the cost. Our approach leverages\noptimal transport to align the pre-deployment distribution P with the post-deployment distribution P(M). We provide a rate of convergence\nfor the proposed estimator and assess its quality through empirical demonstrations on a credit-scoring dataset.",
        "occurrence_notes":"https:\/\/umich.zoom.us\/j\/94865452955?jst=2",
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        "tags":["Dissertation"],
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                "group_name":"Department of Statistics Dissertation Defenses",
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        "datetime_modified":"20261014T095112",
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        "date_end":"2026-10-23",
        "time_start":"15:00:00",
        "time_end":"16:00:00",
        "time_zone":"America\/Detroit",
        "event_title":"AIM Seminar: How voting goes wrong, and how it can go right: the evolution of collective intelligence",
        "occurrence_title":"",
        "combined_title":"AIM Seminar: How voting goes wrong, and how it can go right: the evolution of collective intelligence: Joshua B. Plotkin (University of Pennsylvania)",
        "event_subtitle":"Joshua B. Plotkin (University of Pennsylvania)",
        "event_type":"Lecture \/ Discussion",
        "event_type_id":"13",
        "description":"Abstract: Voting sometimes works well. A crowd's average guess is often better than any individual guess. But voting can also fail, producing a biased outcome or no decision at all. I will discuss natural mechanisms that cause each of these outcomes, in groups whose members observe other voters and learn by imitating one another.\n\nI will first describe a voter game, played online by 2,520 people, in which two parties of equal size compete to reach a supermajority. The structure of who sees whose voting intentions, which we call information gerrymandering, can bias the outcome toward one party, even when both parties are the same size and every player has the same influence. When members of both parties see mainly their own party's intentions, most groups end in deadlock.\n\nI will then ask what individual reward scheme allows a population of self-interested imitators, each observing only part of a problem, to evolve accurate collective decisions. For linear prediction problems, rewarding individuals for their own accuracy eliminates the diversity of opinions a group needs. Rewarding those whose opinions reduce the collective error provably increases accuracy, until the group predicts perfectly. For more realistic non-linear decisions, a single majority vote cannot produce accurate decisions under any reward scheme. Voting in layers, with rewards paid only to individuals who are pivotal at every layer above them, provably allows a population to evolve accurate decisions. The evolved population is equivalent to a multi-layer perceptron in machine learning, and the reward for pivotality corresponds to credit assignment that machines compute through backpropagation. These results therefore provide a naturalistic, evolutionary account of collective in intelligence in humans and machines.\n\nContact: Naoki Masuda",
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        "permalink":"http:\/\/events.umich.edu\/event\/148879",
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        "building_name":"East Hall",
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        "room":"1084",
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        "tags":["Mathematics","Applied Mathematics"],
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        "sponsors":[
             {
                "group_name":"Applied Interdisciplinary Mathematics (AIM) Seminar - Department of Mathematics",
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                "website":""                },             {
                "group_name":"Department of Mathematics",
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        "datetime_modified":"20260807T132324",
        "datetime_start":"20261023T150000",
        "datetime_end":"20261023T163000",
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        "time_start":"15:00:00",
        "time_end":"16:30:00",
        "time_zone":"America\/Detroit",
        "event_title":"Faculty Workshop on Holistic Graduate Admissions for Excellence and Diversity",
        "occurrence_title":"Faculty Workshop on Holistic Graduate Admissions for Excellence and Diversity (CENTRAL CAMPUS)",
        "combined_title":"Faculty Workshop on Holistic Graduate Admissions for Excellence and Diversity: Faculty Workshop on Holistic Graduate Admissions for Excellence and Diversity (CENTRAL CAMPUS)",
        "event_subtitle":"",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"Holistic admissions practices are an integral part of achieving both diversity and excellence when admitting graduate student cohorts. This workshop is designed to provide concrete strategies and evidence-based practices to support faculty and program goals in admitting diverse and excellent cohorts of graduate students. This workshop will provide members of graduate admissions committees an opportunity to develop their own holistic admissions tools.",
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        "location_name":"Rackham Building, 4th Floor",
        "has_livestream":0,
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        "tags":["Sessions","Rgs Events","Rgs-events"],
        "website":"https:\/\/sessions.studentlife.umich.edu\/p\/track\/17539",
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                "website":"https:\/\/sessions.studentlife.umich.edu\/"                },             {
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