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        "event_title":"Statistical Modelling of Spatially and Spatio-Temporally Dependent Data: Some Theoretical Results and an Application",
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        "combined_title":"Statistical Modelling of Spatially and Spatio-Temporally Dependent Data: Some Theoretical Results and an Application: Moritz Korte-Stapff",
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        "description":"This dissertation is concerned with data exhibiting spatial and spatio-temporal dependence. It is based on three separate research works.\r\n\r\nOne chapter is concerned with the biogeochemical Argo data in the Southern Ocean, which aims to collect measurements of oxygen, temperature and salinity as well as other variables at varying depths in the ocean. The biogeochemical Argo data is important to improve our understanding of vital biogeochemical processes such as the biological carbon pump and air-sea CO2 exchanges, monitor changes such as ocean deoxygenation and acidification, and improve estimates of the carbon budget. We introduce and estimate a functional regression model for oxygen, temperature, and salinity data. Our model elucidates important aspects of the joint distribution of temperature, salinity, and oxygen across the entire ocean depth covered by the Argo data and improves location estimates of so-called oceanographic fronts, which are of significant scientific interest in their own right. In addition, it enables us to use the more pervasively available temperature and salinity data to recover biogeochemical data at locations where it is not observed.\r\n\r\nAnother chapter, motivated by the work on the Argo data, contributes to the solution of an open problem in the spatial statistics literature. Concretely, we study the smoothness estimation of so-called Whittle-Mat\u00e9rn kernels on closed Riemannian manifolds. The smoothness of Mat\u00e9rn kernels controls, for example, optimal error bounds for kriging and posterior contraction rates in Gaussian process regression. However, it has been an open problem whether their smoothness can always be consistently estimated. On closed Riemannian manifolds, we show that their smoothness can be consistently estimated from the maximizer(s) of the Gaussian likelihood when the underlying data stem from point evaluations of a Gaussian process and, perhaps surprisingly, even  when the data comprise evaluations of a non-Gaussian process. Moreover, we generalize a well-known equivalence of measures phenomenon related to Mat\u00e9rn kernels to the non-Gaussian case by using Kakutani's theorem.\r\n\r\nThe remaining chapter extends this work to processes observed on the vertices of graphs. Due to increased tractability of the problem is this setting, we are able to provide more complete results. In addition, we establish connections to processes observed on smooth domains such as Riemannian manifolds. In this way, we believe that our results for processes on graphs provide additional insights for such cases as well.",
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        "event_title":"SMBC Information Session: Women's Colleges Edition",
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        "description":"SMBC Information Session: Women\u2019s Colleges EditionWe invite all sophomores, juniors, and seniors who are attending a Women\u2019s College to our SMBC Information Session: Women\u2019s Colleges Edition. We understand how the recruitment process can be overwhelming at times. This session was\u00a0designed with your best interests in mind. From learning about our SMBC brand, our businesses,\u00a0and intern and analyst programs to gaining insights into the interview process, we are confident that you will be better prepared for your career journey.Candidates from all backgrounds and majors are invited to join!Come get to know usAt SMBC, we are growing and transforming alongside our clients. This means we need experienced collaborators tohelp us continue on our path of globalization, diversification, and expansion. We are developing teams of dynamic seekers who are looking to build something great and lasting for themselves, our clients, and our company.We invite sophomores and juniors\u00a0to meet us virtually,4:00 p.m. to 5:00 p.m. ET, August 14.Date: Wednesday, August 14Time: 4:00PM to 5:00PM ETLocation: WebexMeeting Number:\u00a02631 439 4583Meeting Password:\u00a0Fall2024\u00a0",
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