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DTSTART;TZID=Asia/Kolkata:20251117T133000
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SUMMARY:Philosophy Colloquium: Explanatory Strategies in the Egyptian Book of Nut
DESCRIPTION:Title: Explanatory Strategies in the Egyptian Book of Nut \nSpeaker: Tom Davies\, The University of Melbourne \nMonday\, 17 November at 1:30pm\nLocation: AC04-302 \nDescription: The Book of Nut is an Egyptian composition of the New Kingdom (1550–1069 BCE). It contains hieroglyphic texts giving dates for the risings and settings of certain stars\, set within a massive illustration of two anthropomorphic figures: Shu\, the god of air\, raises Nut\, goddess of heaven\, over the earth. At first glance\, it appears to confirm a long-held view that\, while Egyptians were good observers of natural phenomena\, they attributed them to supernatural agents\, checking any theorization of nature we could call philosophical.\nI think this reading is mistaken. The star-dates of the Book of Nut are not\, and could not be\, derived from observation. They are instead a hypothetical construction used to establish a geometrical model of the unseen parts of the sky. This model in turn is the basis of a theory of the causes of stellar motion. In this talk\, I will look at some strategies the composer(s) of this text employ in theorizing the cosmos: what kind of arguments do we see\, and what counts an explanation\, in Egyptian philosophy?
URL:https://www.ashoka.edu.in/event/philosophy-colloquium-explanatory-strategies-in-the-egyptian-book-of-nut/
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DTSTART;TZID=Asia/Kolkata:20251117T190000
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SUMMARY:Computer Science Online Seminar
DESCRIPTION:Abstract: Extracting actionable insights from unstructured text remains a fundamental goal of NLP\, yet conventional approaches often fall short in temporal and causal reasoning and integrating domain-specific insights. This talk introduces innovative research directions to address these challenges across clinical and social science domains. First\, I will introduce GraphTREx\, a model that enhances clinical temporal relation extraction by integrating context-aware representations with graph neural networks. This integration enables improved timeline extraction from clinical notes\, overcoming challenges posed by long documents and specialized jargon\, thereby supporting more accurate patient representations. I will then turn to social data\, illustrating how contextualized representations help quantify between-group polarization and use causal inference to examine when intergroup interactions mitigate it. Finally\, I will present a comprehensive framework to  investigate gender bias in generative-AI-driven recruitment and highlight how behavioral alignment affects fairness. \n\nAbout the Speaker: Rochana Chaturvedi is a Postdoctoral Research Fellow at Argonne National Laboratory\, where she develops large language model (LLM) frameworks for scientific reasoning. Her research lies at the intersection of natural language processing\, graph-based modeling\, and causal machine learning\, with applications spanning healthcare\, computational social science\, AI ethics\, and climate science communication. She received her Ph.D. in Computer Science from the University of Illinois Chicago and previously served as an Assistant Professor at the University of Delhi. Her research has been published in venues such as ACL\, the Web Conference (WWW)\, Political Analysis\, and IEEE BIBM\, and has been covered by major media outlets. \nWe look forward to you your active participation.
URL:https://www.ashoka.edu.in/event/computer-science-online-seminar/
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