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        "time_start":"16:00:00",
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        "event_title":"Nam Center Colloquium Series | Homo juluensis: A Pan-Eastern Asian Middle Pleistocene Hominin",
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        "combined_title":"Nam Center Colloquium Series | Homo juluensis: A Pan-Eastern Asian Middle Pleistocene Hominin: Christopher Bae, Professor, University of Hawai'i",
        "event_subtitle":"Christopher Bae, Professor, University of Hawai'i",
        "event_type":"Lecture \/ Discussion",
        "event_type_id":"13",
        "description":"Attend in person or via Zoom: https:\/\/myumi.ch\/w9kqG.\r\n\r\nMiddle Pleistocene (~780,000-~127,000 years BP) fossils that cannot be easily assigned to Homo erectus, H. neanderthalensis, or H. sapiens have traditionally been lumped into a generic category like \u201carchaic H. sapiens\u201d (or H. heidelbergensis). How to classify these fossils has been the subject of intense debate over the past several decades, a debate that is sometimes referred to simply as \u201cThe Muddle in the Middle.\" What is becoming increasingly clear is that multiple species of hominins were present across Eurasia and Africa during this time period. Here, Bae will introduce Homo juluensis, a new species of hominin that was present across eastern Asia that appeared as far north as southern Siberia, as far south as Laos, and even present in high altitudes (Qinghai-Tibet). In addition, gaps in the paleoanthropological record (e.g., North Korea) will also be discussed as there is a good chance different types of hominins, including H. juluensis, were present in the region.\r\n   \r\nChristopher J. Bae is a professor of anthropology at the University of Hawai\u2019i at Manoa. Bae has spent the past three decades researching the paleoanthropological record of eastern Asia, particularly Korea, China, and Japan. He takes a multidisciplinary approach to his research working closely with paleontologists, archaeologists, geochronologists, and paleoclimatologists. Bae is the recipient of the University of Hawaii Board of Regents\u2019 Research Excellence Award and his research was recently acknowledged as one of the top ten University of Hawaii at Manoa stories for 2024.\r\n\r\nArticle related to this talk: https:\/\/myumi.ch\/n1gG3\r\n\r\nIf there is anything we can do to make this event accessible to you, please contact us at ncks.info@umich.edu. Please be aware that advance notice is necessary as some accommodations may require more time for the university to arrange.",
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        "tags":["Asian Languages And Cultures","History","Korea","Korean Studies"],
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        "event_title":"Statistics Department Seminar Series: Ethan Xingyuan Fang, Associate Professor, Department of Biostatistics & Bioinformatics, Duke University",
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        "combined_title":"Statistics Department Seminar Series: Ethan Xingyuan Fang, Associate Professor, Department of Biostatistics & Bioinformatics, Duke University: \"Offline Data-Driven Decision Making with Applications to Assortment Optimization\"",
        "event_subtitle":"\"Offline Data-Driven Decision Making with Applications to Assortment Optimization\"",
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        "description":"Abstract: We present a unified offline decision-making framework. In the first part, we consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline dataset consisting of the historically offered assortment set, customer choice, and revenue. The objective is to use the offline dataset to find an optimal assortment. Due to the combinatorial nature of assortment optimization, the problem of insufficient data coverage is likely to occur in the offline dataset. Therefore, designing a provably efficient offline learning algorithm becomes a significant challenge. To this end, we propose an algorithm referred to as Pessimistic ASsortment opTimizAtion (PASTA) following the spirit of pessimism. We show that the algorithm identifies the optimal assortment by only requiring the offline data to cover the optimal assortment under general settings. In particular, we establish a regret bound for the offline assortment optimization problem under the celebrated multinomial logit model and its generalizations, where the regret is shown to be minimax optimal. We will also discuss other novel combinatorial uncertainty quantification problems of assortment optimization.",
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