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        "event_title":"\"Let's Talk\": Informal, Drop-In Mental Health Counseling",
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        "combined_title":"\"Let's Talk\": Informal, Drop-In Mental Health Counseling",
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        "description":"Trained mental health counselors are now available for drop-in conversations at different times and locations across campus, including at Trotter, the Spectrum Center, South Quad, the International Center, and Bursley.\r\n\r\nThis informal, confidential \u201coffice hours\u201d style can be a great fit for students unsure about formal counseling; for those with a specific, time-limited concern they\u2019d like to talk through; or those seeking information on campus resources. Please note: this is not meant for crisis or emergency support.\r\n\r\n\"Let's Talk\" will run from January 20th 2025 to April 25th 2025. There will be no drop-ins the week of Spring Break (March 3rd - 7th). \r\n\r\nMonday: 11:00 am - 1:00 pm with Markie Silverman, Ph.D., LP, Room 2035 in Trotter Multicultural Center\r\nTuesday: 1:00 pm - 3:00 pm with Marcella A. Beaumont, Ph.D., Room 3032 in The Spectrum Center (Michigan Union)\r\nWednesday: 1:00 pm - 3:00 pm with Emily Malinowski, LMSW, Room 1721A in South Quad Housing\r\nThursday: 2:00 pm - 4:00 pm with Ling Liu, Ph.D. & Chunyu Xu, M.Ed., M.S.Ed., Conference Room in the International Center\r\nFriday: 10:00 am - 12:00 pm with Kayla Douglas, LMSW, and Emily Powers, LLMSW, Room 2329B in Bursley Housing",
        "occurrence_notes":"With Marcella A. Beaumont, Ph.D.",
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        "event_title":"Contributions to Distributed Learning and Selective Inference",
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        "combined_title":"Contributions to Distributed Learning and Selective Inference: Yumeng Wang",
        "event_subtitle":"Yumeng Wang",
        "event_type":"Lecture \/ Discussion",
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        "description":"The rapid growth and distributed nature of modern datasets pose significant challenges in statistical learning and inference. Privacy concerns often prohibit direct data sharing across sites, while distributional heterogeneity complicates accurate modeling and inference. Moreover, high-dimensional data and model selection procedures necessitate statistical methods for valid inference post-selection. This dissertation addresses these challenges by developing methodologies in distributed statistical learning, inference with heterogeneous data, and selective inference.\r\n\r\nIn the first part of the dissertation, we propose a novel one-shot distributed learning algorithm via refitting bootstrap samples. We demonstrate that the proposed estimator achieves full-sample statistical rates with only one round of communication of subsample-based statistics in generalized linear models and noisy phase retrieval. We further extend this approach to an iterative algorithm and apply it to convolutional neural networks (CNNs), which exhibits superior performances over existing methods in simulation studies.\r\n\r\nIn the second part of the dissertation, we develop a novel one-shot distributed learning algorithm to address cross-site heterogeneity. The proposed method effectively accommodates heterogeneity by allowing nuisance parameters to vary across sites. We show that the proposed estimator attains the full-sample statistical error rate and efficiency with only a single round of communication of local estimators. Our simulation studies support these theoretical findings.\r\n\r\nIn the third part of the dissertation, we introduce an asymptotic pivot to infer about the effects of selected variables on conditional quantile functions. Utilizing estimators from smoothed quantile regression, our proposed pivot is easy to compute and yields asymptotically-exact selective inference without making strict distributional assumptions about the response variable. By employing external randomization, our approach fully utilizes the data for both selection and inference, outperforming traditional methods like data splitting by consistently delivering shorter and more reliable confidence intervals. Simulation studies and an empirical application analyzing risk factors for low birth weight validate the practical efficacy of our method.",
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        "datetime_modified":"20250310T173745",
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        "time_zone":"America\/Detroit",
        "event_title":"ICPSR Webinar: Death and Taxes: We Can't Help You Avoid Either, But We've Got Data!",
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        "combined_title":"ICPSR Webinar: Death and Taxes: We Can't Help You Avoid Either, But We've Got Data!",
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        "event_type":"Presentation",
        "event_type_id":"16",
        "description":"This 30-minute ICPSR webinar will add some levity to your Tax Day as we highlight data about death and taxes that you probably never knew we had! We've got data about taxes from the early days of America to the present, taxes on soda and hazardous wastes, even data about the people who do our taxes. And you don't have to be at death's door to be curious about causes of death or attitudes about aid-in-dying and life after death. We'll also include a brief introduction to applying for restricted death and tax data from the federal government. The topics may be heavy, but the mood will be light!\r\n\r\nThis webinar is free and open to the public. This webinar will be recorded and the recording will be sent to all registrants. Zoom FAQ for Attendees: http:\/\/myumi.ch\/kx2oo",
        "occurrence_notes":"Please register at https:\/\/myumi.ch\/2r53J",
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        "tags":["Free","Data Analysis","Data Curation","Data Management","Data Science","Discussion","Economics","Data","Research","Social Sciences","Statistics","Survey Research","Virtual","Webinar"],
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