Arlo Sheridan
Arlo is a machine learning scientist interested in developing novel approaches for reconstruction of neural circuits using microscopy and barcoded data. In the lab of Jan Funke at HHMI Janelia, he helped develop LSDs (Local Shape Descriptors), a method for neuron reconstruction in large electron microscopy datasets (Sheridan et al., Nature Methods, 2022 | localshapedescriptors.github.io). This approach elevated conventional methods to be on par with the state-of-the-art, while being two orders of magnitude more computationally efficient. He then joined the labs of Uri Manor and Talmo Pereira at the Salk Institute where he worked on new techniques for fast ground truth generation from sparse labels, and generalizable methods for multi-object tracking. Additionally, he contributed to core software stacks, including SLEAP.