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职业迁徙
个人简介
I’m focused on developing reinforcement learning and natural language processing methods which are robust enough for real-world applications and keep strong theoretical guarantees. I focus on healthcare and biomedical applications which usually involve challenging and unstructured data such as electronic health records (EHR) which suffers from sampling bias, partially observed rewards, or strong distribution shifts between different hospital sites. My PhD dissertation deals with learning optimal dynamic treatment regimes, in particular I work on the following topics:
Semi-supervised reinforcement learning and doubly robust value function estimation
Learning domain-specific safe and interpretable policies using hypothesis testing
Using a surrogate convex loss function to optimize dynamic treatment regimes
Other topics I enjoy working on are 1) developing natural language processing methods for phenotyping, automatic diagnosis, and for building a medical knowledge graph from clinical data in settings where labels are not available. This research is centered around the fact that patient label data in EHR data is often unavailable. 2) Scalable methods for semi-parametric Gaussian process regression with provable convergence for detecting air pollution effects on cognitive development of newborns.
Semi-supervised reinforcement learning and doubly robust value function estimation
Learning domain-specific safe and interpretable policies using hypothesis testing
Using a surrogate convex loss function to optimize dynamic treatment regimes
Other topics I enjoy working on are 1) developing natural language processing methods for phenotyping, automatic diagnosis, and for building a medical knowledge graph from clinical data in settings where labels are not available. This research is centered around the fact that patient label data in EHR data is often unavailable. 2) Scalable methods for semi-parametric Gaussian process regression with provable convergence for detecting air pollution effects on cognitive development of newborns.
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crossref(2023)
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