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        "description":"The Tennessee Valley Authority provides electricity for 153 local power companies serving 10 million people in Tennessee and parts of six surrounding states, as well as directly to 57 large industrial customers and federal installations. We don't get taxpayer funding; rather our revenues come from sales of electricity. TVA also provides flood control, navigation and land management for the Tennessee River system, and assistslocal power companies and regional governments with their economic development efforts.\n\nOur mission is just as relevant today as it was nearly nine decades ago when Congress created TVA: harness the potential of the rich resources in the Tennessee Valley region to make life better for the people who call it home. We are inspired daily to pursue new ideas and innovative solutions to improve our service to you. The 10,000 employees of TVA form an experienced, talented team committed \u2014 each and every day \u2014to improving the quality of life for the approximately 10 million people we serve.",
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        "event_title":"\"Hepato-Biliary-Pancreatic Organogenesis in a Dish\"",
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        "combined_title":"\"Hepato-Biliary-Pancreatic Organogenesis in a Dish\": Takanori Takebe, MD, PhD",
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        "event_title":"Asset Management Recruiting Uncovered",
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        "combined_title":"Asset Management Recruiting Uncovered",
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        "event_title":"Bright chiral light from small molecules",
        "occurrence_title":"",
        "combined_title":"Bright chiral light from small molecules: Gael Ung (University of Connecticut)",
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                "website":"https:\/\/lsa.umich.edu\/chem"                },             {
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        "time_zone":"America\/Detroit",
        "event_title":"Jump-start your job search",
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        "event_subtitle":"",
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        "description":"Join us to connect with PwC recruiters for an overview of our recruiting process and application FAQ. You\u2019ll hear details about available student development programs, internship & full-time opportunities this spring! Note: this is one of two duplicative sessions being offered this spring.\n\nVisit our entry level careers site and apply by February 22,2022 @ 11:59 pm EST!",
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        "event_title":"Statistics Department Seminar Series: Weibin Mo, Applied Scientist, Supply Chain Optimization and Technologies, Amazon",
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        "combined_title":"Statistics Department Seminar Series: Weibin Mo, Applied Scientist, Supply Chain Optimization and Technologies, Amazon: \"Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models\"",
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        "description":"Abstract: Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, researchers can search for the optimal individualized treatment rule (ITR) that maximizes the expected outcome. Existing methods typically require initial estimation of some nuisance models. The double robustness property that can protect from misspecification of either the treatment-free effect or the propensity score has been widely advocated. However, when model misspecification exists, a doubly robust estimate can be consistent but may suffer from downgraded efficiency. Other than potential misspecified nuisance models, most existing methods do not account for the potential problem when the variance of outcome is heterogeneous among covariates and treatment. We observe that such heteroscedasticity can greatly affect the estimation efficiency of the optimal ITR. In this presentation, we demonstrate that the consequences of misspecified treatment-free effect and heteroscedasticity can be unified as a covariate-treatment dependent variance of residuals. To improve efficiency of the estimated ITR, we propose an Efficient Learning (E-Learning) framework for finding an optimal ITR in the multi-armed treatment setting. We show that the proposed E-Learning is optimal among a regular class of semiparametric estimates that can allow treatment-free effect misspecification. In our simulation study, E-Learning demonstrates its effectiveness if one of or both misspecified treatment-free effect and heteroscedasticity exist. Our analysis of aType 2 Diabetes Mellitus observational study also suggests the improved efficiency of E-Learning.\r\n\r\nhttps:\/\/sites.google.com\/view\/weibin-mo",
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        "website":"https:\/\/lsa.umich.edu\/stats",
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