The lab is actively developing data analysis methods for learning cytoarchitectonics (layers), mapping brain areas, and distributed segmentation and analysis of large-scale neuroimaging data.
Low-dimensional signal models
Unions of subspaces (UoS) are a generalization of single subspace models that approximate data points as living on multiple subspaces, rather than assuming a global low-dimensional model (as in PCA). Modeling data with mixtures of subspaces provides a more compact and simple representation of the data, and thus can lead to better partitioning (clustering) of the data and help in compression and denoising.
Analyzing the activity of neuronal populations
Advances in monitoring the activity of large populations of neurons has provided new insights into the collective dynamics of neurons. The lab is working on methods that learn and exploit low-dimensional structure in neural activity for decoding, classification, denoising, and deconvolution.
Optimization problems are ubiquitous in machine learning and neuroscience. The lab works on a few different topics in the areas of non-convex optimization and distributed machine learning.