Course Name: NeuroData Design

Extracellular electrophysiological simulations, and comparative analysis of various spike sorting algorithms with ground truth validation.

The Neural Anatomy and Optical Microscopy (NAOMi) is a first-in-class approach in that it is the only existing framework for...

Prostate cancer remains the second most common cause of death among men worldwide. It is challenging to treat, with early...

Developing a visualisation method to observe the internal dynamics of recurrent neural networks.

Activity recognition is one of the most essential and challenging tasks in computer vision. The development of a precise activity...

Our group leverages advances in generative learning to develop more statistically accurate, computationally feasible post-hoc Shapley-based explanations for medical computer...

Understanding the structure of the brain can lead to advances in treating neurological disorders. Our team is developing Brainlit, a...

Inherent variability within a single network or  populations of networks is an increasingly desirable phenomenon to characterize. Implications of this...

Connectomes are brain mappings that allow us to understand the variability in brain connectivity and brain function. Currently, there is...

The connectome is a comprehensive map of the structural and functional connections of the brain, derived from multiple neuroimaging modalities....

In many datasets, there are multiple measurement modalities of the same subject, i.e. multiple X matrices (views) for the same...

Random Forest (RF) is an interpretable and robust machine learning algorithm for classification and regression. However, existing RF methods are...

Lifelong learning is a paradigm proposed to address catastrophic forgetting as new tasks are introduced and learned by a model....

Random forests (RF) and deep networks (DN) are two of the primary machine learning methods in current literature, yet they...