| Date: | September 22, 2023 |
| Time: | 09:00am |
| Location: | ZOOM |
| Speaker: | Pengfei Song, Xi’an Jiaotong University, China |
| Title: | Coupling of transmission dynamics and deep learning techniques |
| Abstract: | Neural networks, though called as black-box uniform approximator and difficult to interpret, have an unreasonable effectiveness in learning unknown mechanisms with bless of dimensionality, and have lots of applications. In this talk, I will introduce a recent state-of-the-art universal differential equation method that embeds neural networks into differential equations. Three applications will be shown. (1) Using deep learning techniques to estimate effective reproduction number and compared with EpiEstim and EpiNow2 method. (2) Discovering unknown human behavior change mechanisms in transmission dynamics. (3) Using deep learning techniques to solve optimal control problems by representing optimal control function as neural networks, and compared with traditional direct, indirect, and dynamic programming methods.
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| Date: | November 20, 2023 |
| Time: | 4:00pm |
| Location: | BLOC 117 |
| Speaker: | Toryn Shafer, Texas A&M University |
| Title: | Bayesian Inverse Reinforcement Learning For Collective Animal Movement |
| Abstract: | Agent-based methods allow for defining simple rules that generate com- plex group behaviors. The governing rules of such models are typically set a priori and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the self propelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.
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