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        "event_title":"TeachHampton Tuesdays Teacher Opportunities & Licensure Webinars",
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        "description":"Are you interested in the path to become a licensed teacheror in other employment opportunities within the school district and need more information? Hampton City Schools welcomes candidates with diverse backgrounds and a passion for student success to attend The HCS Opportunities Webinars! This is a live info session covering requirements for different positions, the application processes and HCS offerings.\u00a0",
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        "description":"Prof. Tiffany Ng, Vinson Lam, Adam Lenhart, and Sarah Penrose perform music in honor of Veterans Day.\n \nThe Charles Baird Carillon is an instrument of 53 bronze bells located inside the Burton Memorial Tower. The largest bell, which strikes the hour, weighs 12 tons, while the smallest bell, 4\u00bd octaves above, weighs just 15 pounds.\n\nThirty-minute recitals are performed on the Charles Baird Carillon at noon every weekday that classes are in session, followed by visitor Q&A with the carillonist. The bell chamber may be accessed via a combination of elevator and stairs. Take the elevator to the highest floor possible (floor 8), and then climb two flights of stairs (39 steps) to the bell chamber (floor 10). Hearing protection earmuffs are provided for visitors. Be prepared to walk on ice and snow in the bell chamber during winter. Built in 1936, the Charles Baird Carillon is not ADA accessible. Visitors with mobility concerns are invited to visit the Lurie Carillon.",
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        "description":"Can suffering and God coexist?\r\nHave faith questions you're afraid to ask?\r\nFeeling empty but not sure why?\r\nDo any of these questions resonate with you?\r\n\r\nJoin us as we tackle these and other questions every Tuesday together at 12:30 as we read through Can I Say That? by Brenna Blain.",
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        "event_title":"Pause Caf\u00e9: French Conversation Hour",
        "occurrence_title":"",
        "combined_title":"Pause Caf\u00e9: French Conversation Hour",
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        "description":"-Enjoy coffee, tea, and snacks while improving your French skills! \r\n\r\n-Chat for 10 minutes or the entire hour. All language levels are welcome.\r\n\r\nThe RLL Commons is located in the center hallway of the 4th floor of the Modern Languages Building. \r\n\r\nFor more information contact Alan Ames at (alanames@umich.edu).",
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        "event_title":"AmplifyME! Banking Technical Workshop",
        "occurrence_title":"",
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        "event_type":"Careers \/ Jobs",
        "event_type_id":"2",
        "description":"The AmplifyME Banking Technical Workshopgives students the chance to experience life as ajunior GCIB analyst and gain the technical skillsneeded to succeed in investment banking.What to expect:\u00a0Hands-On Training: Work on real-worldtransactions and company case studies.\u2022 Key Technical Skills: Learn Excel modelling,three-statement analysis, and core valuationmethods such as DCF and ComparableCompany Analysis.\u2022 Step-by-Step Support: Expert trainerguidance, easy-to-follow videos, and color codedmaterials make the session accessibleto everyone, no matter your background.\u2022 Career Insights: Understand what recruiterslook for in technical assessments and howto stand outin the Bank of Americarecruitment process.",
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        "datetime_modified":"20251030T101626",
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        "event_title":"Conformally Robust Decision Making",
        "occurrence_title":"",
        "combined_title":"Conformally Robust Decision Making: Yash Patel",
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        "description":"Black-box machine learning models are seeing increasing deployment in safety-critical settings, such as in autonomous vehicles and healthcare settings. This coupling increases the need to have reliable uncertainty quantification. Traditional methods for such estimation, however, require distributional assumptions that are incompatible with these modern black-box estimators. In their place, post-hoc, distribution-free methods of uncertainty quantification have arisen. Among these is ``conformal prediction.'' At its core, conformal prediction performs uncertainty quantification by replacing model point predictions with ``prediction regions,'' subsets of the output space whose shape and size are defined to guarantee coverage of the truth with some user-specified probability.\r\n\r\nDespite such guarantees, these implicitly defined predictions regions do not directly lend themselves to practical use; while researchers professed their supposed utility, their downstream use was not immediately obvious. In this thesis, we propose and develop one such use: model-based decision-making. We demonstrate that conformal prediction can be integrated into a variety of decision-making pipelines, from single-step predict-then-optimize problems to model-based LQR control, and consequently enable guarantees on suboptimality that otherwise cannot be established.\r\n\r\nWe develop this conformal decision-making framework over three works. In the first, we focus on the development of conformal prediction in the space of scientific inquiry: here, decisions are often framed as hypothesis testing of parameter values. Increasingly common in certain domains, such as astrophysics and neuroscience, is the use approximate variational inference to do such parameter estimation, due to the large scale at which such estimation is to be performed. Amortized variational inference produces a posterior approximation that can be rapidly computed given any new observation. Unfortunately, there are few guarantees about the quality of these approximate posteriors. We propose Conformalized Amortized Neural Variational Inference (CANVI), a procedure that is scalable, easily implemented, and provides guaranteed marginal coverage. Given a collection of candidate amortized posterior approximators, CANVI constructs conformalized predictors based on each candidate, compares the predictors using a metric known as predictive efficiency, and returns the most efficient predictor. \r\n\r\nIn the next work, we generalize the setting for such robust decision-making, expanding from scientific parameter testing to a more general space of ``predict-then-optimize'' problems. As in standard decision-making formulations, these problems frame the decision-making task as a parametric optimization problem. The unique aspect here, however, is that the parameters of the problem are not revealed to the decision-maker. As a result, the decision-maker is forced to estimate the unknown parameters and optimize their decision against this surrogate objective, hence the given name: the estimation is performed by ``predicting'' the parameters with an upstream model. In the nominal approach, the parameters predicted by the upstream model are assumed to precisely coincide with the true, unknown parameters; this approach of specification, however, fails to have any formal guarantees on the resulting decision. Towards this end, we develop a robust analog of this nominal problem formulation, called ``Conformal Predict-Then-Optimize'' (CPO), from which suboptimality guarantees can be established. We then demonstrate how a simple, residual-based score results in overly conservative decision-making and propose an alternative score that produces structured, non-convex prediction regions and, in turn, more informative decisions. \r\n\r\nFinally, we demonstrate the generality of the proposed conformal predict-then-optimize decision-making framework. In particular, we demonstrate that CPO can be extended to a recently proposed extension to conformal prediction in which the scalar score function is replaced with an analogous vector score and the quantile threshold by a quantile envelope. We similarly demonstrate that CPO naturally lends itself to extension to model-based robust control applications. We, thus, develop extensions across these two applications and then demonstrate the consistent empirical improvements produced in each.",
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