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incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
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, recurrent memory, Bayesian modelling, uncertainty quantification and machine learning systems. Emphasis will be on methods that design and implement new architectures for (auto-regressive) sequence modelling
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cutting-edge research is carried out on a wide range of topics ranging across programming language (especially Bayesian statistical probabilistic programming), statistical machine learning, generative AI
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device-relevant properties Design active learning, Bayesian optimization, uncertainty-aware modeling, and other adaptive experimental design workflows to guide experiments and improve data efficiency in
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transcriptomics. He is the lead developer of widely used open-source software including BayesPrism, a Bayesian deconvolution framework selected as a Nature Cancer 2022 highlight. Dr. Chu's research has been
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https