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        "event_title":"LSA Workshop: Hired-In",
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        "combined_title":"LSA Workshop: Hired-In: Jessica Garcia",
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        "description":"In this session, participants will:\u00a0\u00a0\r\nIncrease awareness of how implicit bias can show up during the hiring process\u00a0\r\nGain an awareness of the importance of consistent guidelines, evaluation and candidate experience\u00a0\r\nDiscuss equitable hiring conventions\u00a0Increase knowledge regarding affirmative action goals\u00a0\r\nLearn about resources that exist in LSA and on campus\r\n\r\nAudience:\r\nThis workshop is required for all staff who are involved in the staff recruiting and selection process for LSA.\u00a0All LSA staff, faculty, graduate students, and undergraduates currently employed in LSA are welcome to attend. External guests may request to join as room allows.",
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        "time_zone":"America\/Detroit",
        "event_title":"Science Success Series- Mindful Mondays",
        "occurrence_title":"Mindful Monday 5",
        "combined_title":"Science Success Series- Mindful Mondays: Mindful Monday 5",
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        "description":"Give your brain some rejuvenation by taking a mindful study break. Come join us for an hour of connection, conversation, and crafts with fellow students. The WISE Mentors will be available to answer any questions you may have. Need a resume review, advice for picking classes, help making a study schedule? We've got you covered! \r\n\r\nThis is a drop-in style event where you can come and go as your schedule allows. Light snacks will be provided.",
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        "datetime_modified":"20241004T123644",
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        "time_zone":"America\/Detroit",
        "event_title":"Student Logic and History of Math Seminar (Invited Address): Provability Logic",
        "occurrence_title":"",
        "combined_title":"Student Logic and History of Math Seminar (Invited Address): Provability Logic: Professor Andreas Blass",
        "event_subtitle":"Professor Andreas Blass",
        "event_type":"Lecture \/ Discussion",
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        "description":"Provability logic seeks to efficiently describe, by suitable axioms and rules of inference, what a reasonable theory can prove about its own provability predicate. Here \u201creasonable\u201d means that the theory can prove basic facts about the natural numbers; Peano arithmetic is more than enough.\r\n\r\nThe notation used in provability logic (meaning in the particular provability logic that I\u2019ll concentrate on) is the same as in most modal logics, namely ordinary propositional logic plus a modal operator, written as a box. But the box operator has the unusual interpretation \u201cit is provable that\u201d.\u00a0It turns out that this provability logic can be axiomatized by adding to ordinary propositional logic just one axiom (schema) and one inference rule. The rule is very simple, but the axiom expresses L\u00f6b\u2019s theorem, a far from obvious fact about provability.\r\n\r\nI\u2019ll begin by explaining L\u00f6b\u2019s theorem. Then I\u2019ll discuss the axiomatic system of provability logic and a few proofs in that system. Next, I\u2019ll describe the Kripke models of provability logic.\u00a0Finally, I\u2019ll discuss two completeness theorems for this logic, one saying that Kripke models provide a complete semantics, and the other saying that provability logic exactly captures what a reasonable theory can prove about its own provability.",
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        "event_title":"Tiffany Ng, carillon",
        "occurrence_title":"",
        "combined_title":"Tiffany Ng, carillon",
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        "description":"University Carillonist Tiffany Ng performs on the Ann & Robert H. Lurie Carillon, an instrument of 60 bells with the lowest bell (bourdon) weighing 6 tons.\n\nThirty-minute recitals are performed on the Lurie Carillon every weekday that classes are in session. During these recitals, visitors may take the elevator to level 2 to view the largest bells, or to level 3 to see the carillonist performing. (Visitors subject to acrophobia are recommended to visit level 2 only.) An optional spiral stairway between levels 2 and 3 allows for up-close views of some of the largest bells.",
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        "event_title":"Coffee & Bagels with MEEBS",
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        "event_title":"Chiacchiere e Caff\u00e8: Italian Speaking Hour",
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        "description":"Presentation Abstract\r\nFace processing plays a central role in everyday human life. We investigate the nature of face representation and processing in the brain, by adapting and developing appropriate machine learning and computer vision methods. In the Foundations portion of the lecture, I will be giving a review of the technical methods utilised in our modeling work, including novel methods for combining image-computable machine vision algorithms with more classical psychological methods, such as multidimensional scaling, that take human similarity judgments into account.\r\n\r\nIn the Frontiers portion of the lecture, I will describe our work using a neurobiologically supported and psychologically informed face representation, to computational model and quantify human perception and judgment of faces, including a computational explanations of the provenance of certain racial and gender biases. Furthermore, we will examine some implications of these findings for the broader domains of psychiatry, social psychology, economics, and political science. \r\n------\r\nSchedule\r\n2:00-2:30 pm Foundations Presentation\r\n2:30-2:40 pm Q & A\r\n\u201410 minute break\u2014\r\n2:50-3:20 pm Frontiers Presentation\r\n3:20-3:30 pm Q & A\r\n-----\r\nDr. Yu conducts research at the intersection of natural and artificial intelligence. Her group uses mathematically rigorous and algorithmically diverse tools to understand the nature of representation and computations that give rise to intelligent behavior, with particular regard to the challenges posed by inferential uncertainty and the opportunities afforded by volitional control. Using diverse machine learning and statistical tools, e.g. Bayesian statistical modeling, control theory, reinforcement learning, and information theory, theoretical frameworks and mathematical models are developed to explain disparate aspects of cognition importation for intelligence: perception, attention, decision-making, learning, cognitive control, active sensing, economic behavior, and social interactions. After completing her bachelor\u2019s at MIT, PhD at UCL Gatsby Computational Neuroscience Unit, and postdoc training at Princeton University, Dr. Yu was faculty at UCSD Cognitive Science for 14 years before joining TU Darmstadt in Germany as an Alexander von Humboldt AI Professor in late 2022. With her interdisciplinary expertise and interest, Dr. Yu\u2019s arrival strengthens and complements research at the Centre for Cognitive Science at TU Darmstadt and at hessian.AI, the Centre for Artificial Intelligence of Hesse based in Darmstadt. Her new group currently has positions open for postdocs, PhD students, master\u2019s students, research interns, and visiting scholars.",
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