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    "154152-21917289":
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        "datetime_modified":"20261027T130831",
        "datetime_start":"20261104T160000",
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        "time_start":"16:00:00",
        "time_end":"17:00:00",
        "time_zone":"America\/Detroit",
        "event_title":"CCMB\/DCMB Weekly Bioinformatics Seminar Series featuring Peter Toogood, PhD (Research Associate Professor and Director of Michigan Drug Discovery (MDD))",
        "occurrence_title":"",
        "combined_title":"CCMB\/DCMB Weekly Bioinformatics Seminar Series featuring Peter Toogood, PhD (Research Associate Professor and Director of Michigan Drug Discovery (MDD)): \"Applications of AI to Hit Finding in Drug Discovery Projects\"",
        "event_subtitle":"\"Applications of AI to Hit Finding in Drug Discovery Projects\"",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"High throughput screening (HTS) is a common starting point for small molecule drug discovery campaigns, especially when the biological target is new. HTS is commonly conducted in the wet lab (e.g. the U-M Center for Chemical Genomics), but also can be conducted in silico. These \"virtual screens\" are either structure-based, requiring a knowledge of the 3D structure of the biological target, or ligand-based, in which case having multiple overlapping ligands makes it easier to define the feature set that new ligands must match. In this talk, I will describe several applications of artificial intelligence\/machine learning to virtual screening in our projects seeking to identify new starting points for cancer drug discovery. In so doing, I will trace the evolution of these methods over the last decade from crude attempts at generative design to the application of frontier models like Claude Science.",
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        "location_name":"Medical Science Unit I",
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        "tags":["Drug Discovery","Basic Science","Artificial Intelligence","Machine Learning","Gen Ai","Bioinformatics"],
        "website":"https:\/\/umich.zoom.us\/j\/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09",
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        "event_title":"Exploratory Optimal Reinsurance under the Mean-Variance Criterion",
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        "combined_title":"Exploratory Optimal Reinsurance under the Mean-Variance Criterion: Austin Riis-Due, Waterloo",
        "event_subtitle":"Austin Riis-Due, Waterloo",
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        "description":"This paper proposes a Reinforcement Learning (RL) approach to the optimal reinsurance problem\nwhen the insurer faces uncertainty about the insurance claim dynamics. To this end, we first formulate\nan exploratory version of the problem as a relaxed stochastic control problem. Within a broad class of\nparametric retention functions and general risk loading functions, we derive the closed-form equilibrium\npolicy under the continuous-time mean-variance criterion. This is achieved through a formal verification\ntheorem and solving classical solutions of a system of exploratory extended Hamilton-Jacobi-Bellman\n(EEHJB) equations. We then establish a policy iteration theorem, showing that starting from any timeand state-homogeneous policy, policy iteration converges to the derived equilibrium policy. Next, we\ndevelop a martingale orthogonality theorem, which serves as the foundation of our RL algorithm. The\nalgorithm is evaluated through simulation studies and real data from the U.S. National Flood Insurance\nProgram. Results demonstrate that the RL approach effectively learns unknown claim distributions,\ntracks unobserved changes in claim dynamics and produces higher insurer surplus trajectories than the\nmaximum likelihood estimation (MLE) approach while simultaneously exhibiting greater robustness to\nthe choice of training window.",
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        "event_title":"German Convo on the Go",
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        "tags":["Language","Humanities","Germanic Languages And Literatures","German Studies","German","European"],
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        "event_title":"Hacking College Life With Chronic Illness and\/or Disability",
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        "occurrence_notes":"Link will be provided upon registration.",
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        "event_title":"Probability and Analysis: Strongly log-concave measures and lattices",
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        "description":"Let L be a lattice in R^n. Suppose that L has the property that the determinant of every non-zero sublattice L' of L is larger than one. We prove that there exists a symmetric random vector X on R^n with a smooth, strictly positive density f on R^n simultaneously enjoying the following two properties: a) f is strongly log-concave; b) X + L is uniform on R^n\/L.\n\nMoreover, the existence of such a density f on R^n characterizes this determinant condition imposed on L. This construction connects several problems in lattice theory to established results in convex geometry. We discuss a range of applications. This is joint work with Victor Reis and Thomas Rothvoss.",
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        "time_zone":"America\/Detroit",
        "event_title":"RTG SEMINAR Topology, Geometry, Dynamics:   Dense subgroups of compact groups: two vignettes.",
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        "combined_title":"RTG SEMINAR Topology, Geometry, Dynamics:   Dense subgroups of compact groups: two vignettes.: David Fisher (Rice)",
        "event_subtitle":"David Fisher (Rice)",
        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"I will talk about two loosely related results about\ndense subgroups of compact groups, one with Sven Sandfeldt,\nthe other with Michael Larsen.  The first concerns local rigidity\nof group actions, the second spectral gaps.  I hope to explain\nsome questions that arise in this context and invite people\nto think about them.",
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        "tags":["Mathematics"],
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