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Abstract: The study of the geometric organization of biological cells and tissue has a rich history. The organization of cells into tissues and organs has often been described qualitatively and with visual illustrations. However, advances in imaging technology coupled with modern computer vision (CV) and machine learning (ML) techniques have progressively allowed speculation on structure-function relationships in biology and medicine to be formalized quantitatively.
Astrocytes are non-neuronal cells of the central nervous system (CNS) that have complex structural, molecular, and physiological properties enabling them to regulate ion homeostasis, neurotransmitter levels, synaptic function/plasticity, and brain energetics.
These cells are an interesting case study for both computer scientists as well as neuroscientists. Firstly, their structure-function relationships have yet to be fully elucidated, and thus analysis of their geometry, shape, and the principles by which their structure is organized will lead to new theories of how they participate in brain function. Secondly, due to their morphological complexity, astrocytes push the limits of both microscopy and CV and ML algorithms. Innovative approaches are therefore necessary to understand their shape and function.
In this talk I'll briefly highlight some unresolved questions in the field of astrocyte biology that computational methods are uniquely positioned to address and  present my own past and ongoing work that leverages CV and ML techniques to tackle some of these questions shedding new light on the organization of these cells.

About the Speaker: Tabish Syed, is a Postdoctoral Fellow at the Centre for Research Neuroscience at McGill University Health Center-Research Institute. He earned his PhD in Computer Science from McGill University with the shape analysis group, specializing in biological image computing. His research interest lies in developing computational methods to answer biological questions, with a particular interest in geometry and math-based approaches for biological image analysis. Currently, his work centers on understanding the role of astrocytes in modulating behavior in mice, leveraging computer vision and machine learning techniques to explore astrocytic function.

We look forward to your active participation.