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Usman Roshan |
Research Areas: Machine learning, medical informatics We work on several medical AI problems involving images and DNA. In collaboration with neuroscientists, we are creating new models for tumor identification in brain MRI images and are studying them across different data sources. With vascular surgeons we are proposing novel models for identifying vessel and plaque in vascular ultrasound images from real patients. We are studying simple random networks for the classification of histopathology slide images and find them to be highly accurate there. |
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Zhi Wei
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Research Areas: Machine learning, statistical modeling, bioinformatics The broad long-term objective of the project concerns the development of novel machine learning methods and computational tools for modeling genomic data motivated by important biological questions and experiments. The analysis of single-cell RNAseq (scRNAseq) data presents substantial computational and bioinformatics challenges. The specific aim of the project is to develop novel model-based deep learning methods with prior biological information considered for modeling scRNAseq data. These problems are all motivated by the collaborations with biomedical investigators. The proposed approaches are designed to integrate biological information for improving both analytical performance and biological interpretability. The methods hinge on novel integration of biological insights and deep learning methods for analysis of the noisy, sparse, and over-dispersed scRNAseq data, including zero-inflated negative binominal model, autoencoder, deep embedding, hyperbolic embedding and reversed graph embedding.
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