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        "event_title":"Building Trust and Strengthening Social Connections",
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        "description":"Bold Challenges Pollination Workshops bring together diverse researchers from all three U-M campuses to spark innovative solutions to complex societal problems. These events provide opportunities to ideate and collaborate around the 2024 Bold Challenges themes.\r\n\r\nDuring the two-hour, in-person workshops, attendees will mix and match to meet potential collaborators and partners, brainstorm research problems and solutions, have opportunities to get advice from U-M experts on how to increase the probability of successful team development and grantseeking, and then take the next steps on a wide range of ideas.\r\n\r\nOne of these steps is to apply for Bold Challenges\u2019 Boost program, which offers research development consulting, project management support and up to $75,000 to advance research ideas. Boost applications for the 2024-2025 cohort are due on May 13.\r\nRegistration Link: https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLSeZSIAPCLBakMGj-qeyvRBAK2ZaHD9tqyr0Rr0RCy9jNobYIw\/viewform?usp=sf_link",
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        "event_title":"Bundling to save: Estimating package size choices in South African grocery stores",
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        "combined_title":"Bundling to save: Estimating package size choices in South African grocery stores: Andrea Szabo, University of Houston",
        "event_subtitle":"Andrea Szabo, University of Houston",
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        "description":"Storable goods such as laundry detergent come in different package sizes with different associated unit prices. Buying larger packages is an opportunity to save, but low-income consumers in African countries often appear to forego this opportunity and buy small packages instead. I investigate the determinants of these choices by estimating a model of dynamic consumer demand using scanner data from all stores of South Africa's leading grocery chain. The estimation accounts for \u201cbundling\u201d: due to temporary sales and non-linear pricing of the product, consumers sometimes find it less expensive to purchase multiple small packages instead of a large package. The results show that this phenomenon is quantitatively important in explaining observed patterns in the data. Counterfactual simulations use the model's findings to evaluate the impact of different package sizes, which is a relevant consideration for the current expansion of small-format chain stores to low-income areas.\r\n\r\nThis talk is presented by the Applied Microeconomics\/Industrial Organization Seminar, sponsored by the Department of Economics with generous gifts given through the Jean Coven Speakers Fund in Economics and the Economics Strategic Fund.",
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        "event_title":"ESC symposium: Residues or What Remains",
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        "event_title":"Michael Woodroofe Lecture Series: Kathryn Roeder, UPMC Professor of Statistics and Life Sciences, Statistics & Data Science Dietrich College of Humanities and Social Sciences, Carnegie Mellon University",
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        "combined_title":"Michael Woodroofe Lecture Series: Kathryn Roeder, UPMC Professor of Statistics and Life Sciences, Statistics & Data Science Dietrich College of Humanities and Social Sciences, Carnegie Mellon University: \u201cTesting for differential expression in single-cell data with unmeasured confounders\u201d",
        "event_subtitle":"\u201cTesting for differential expression in single-cell data with unmeasured confounders\u201d",
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        "description":"When aiming to identify differentially expressed genes, thousands of simultaneous hypothesis tests are performed, which could be biased by the presence of unmeasured confounders. In the context of linear models, surrogate variable models and related approaches have been developed to control for the effect of confounding factors with considerable success. However, in recent years, differential expression testing has been dramatically expanded to include a variety of genomic readouts for which the linear model rarely holds. A Poisson, negative binomial or Bernoulli model is likely more appropriate. Inspired by this advancement we develop a solution for multivariate generalized linear models in the presence of arbitrary confounding effects. We establish consistency and asymptotic normality of our proposed test statistic. Numerical experiments demonstrate that the proposed method controls the false discovery rate and is more powerful than alternative methods. By comparing single-cell RNA- seq counts from Lupus and control samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model. If time permits we will explore how proximal causing learning methods could provide an alternative approach for removing the effects of unmeasured confounders.",
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