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DTSTAMP:20261027T130831
DTSTART;TZID=America/Detroit:20261104T160000
DTEND;TZID=America/Detroit:20261104T170000
SUMMARY:Workshop / Seminar:CCMB/DCMB Weekly Bioinformatics Seminar Series featuring Peter Toogood\, PhD (Research Associate Professor and Director of Michigan Drug Discovery (MDD))
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.
UID:154152-21917289@events.umich.edu
URL:https://events.umich.edu/event/154152
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Machine Learning,Gen Ai,Drug Discovery,Bioinformatics,Basic Science,Artificial Intelligence
LOCATION:Medical Science Unit I - 4B700
CONTACT:
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DTSTAMP:20260807T155844
DTSTART;TZID=America/Detroit:20261104T160000
DTEND;TZID=America/Detroit:20261104T170000
SUMMARY:Workshop / Seminar:Exploratory Optimal Reinsurance under the Mean-Variance Criterion
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.
UID:150012-21907810@events.umich.edu
URL:https://events.umich.edu/event/150012
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 1360
CONTACT:
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