- Biomedical Natural Language Processing (BioNLP)
- Deep learning for biomedical text mining: Design of novel and application of existing cutting-edge NLP models (e.g. Transformer, BERT, and GPT-2) for biomedical information retrieval and text summarization. This is an unprecedented opportunity to harness unstructured information from millions of documents, use this information to guide generic drug review, and even drug discovery.
- Using brain activity (e.g. fMRI) to interpret NLP models and guide model development.
- Development of novel and application of existing deep generative models (e.g. GANs, VAEs) to explain the high-dimensional structure and time course of neural population activity. The focus is on the extraction of low-dimensional temporal patterns in high-dimensional spiking and local field potentials datasets in visual perception, visual attention and working memory tasks, and the development of new tools for causal inference (e.g. copula Granger causality).
- Deep learning for brain age prediction using multimodal neuroimaging data (structural MRI, DTI and resting-state fMRI)
- Topological Data Analysis (See our recent work on structure-function topological mapping and persistent homolgy)
For details, visit our previous website.