Super-resolution microscopy (or optical nanoscopy) techniques allow the characterization of molecular interactions inside living cells with unprecedented spatiotemporal resolution. These techniques come with several layers of complexity in their implementation. My research team focuses on transdisciplinary approaches at the interface of molecular neurosciences, multimodal optical nanoscopy, and machine learning to study structure/function relationship of synapses in the brain.

We develop machine learning and deep learning tools to increase the adaptability and accessibility of high-end imaging methods (e.g. optical nanoscopy) to complex experimental paradigms. Recently, we implemented a machine learning assisted optimization framework for optical nanoscopy allowing real-time optimization of multi-modal live-cell imaging of synaptic activity and structure proteins. We also implemented diverse deep learning approaches for high throughput microscopy image analysis, allowing us to characterize activity-dependent remodelling of neuronal proteins. We develop weakly supervised deep learning strategies to reduce the burden of extensive labeling of complex images and evaluate how they can be applied to real-time microscopy image analysis.

We aim at developing new AI-assisted microscopy techniques that will adapt in real-time to the sample, predict changes in the structures and modify the experimental protocol depending on the measured response to a stimuli.