- Natural Language Processing (NLP)
- Deep learning for biomedical text mining: Design of novel and application of existing cutting-edge NLP models (e.g. Transformer, BERT, GPT-2, XLNet) 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 product development.
- 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.