Ben Pedigo
Scientist I
Ben is a Scientist in the Neural Coding group, working with Forrest Collman. His research focuses on analysis methods for large maps of neural wiring collected via electron microscopy — termed connectomes. Ben is particularly interested in developing tools for extracting generalities of neural wiring rules from connectomes.
Ben joined the Institute in 2023. Before this, Ben did his PhD in Biomedical Engineering at Johns Hopkins University, working with Joshua Vogelstein and Carey Priebe. His research focused on developing computational and statistical tools for helping to understand connectome data. In particular, he collaborated with Michael Winding, Marta Zlatic, and Albert Cardona on analyzing a Drosophila larva brain connectome. During his PhD, Ben also interned at Microsoft Research, where he applied his skills in network data science to organizational communication patterns. Ben’s undergraduate studies were in Bioengineering at the University of Washington. During this time, he was also an intern at the Allen Institute, working with Nuno da Costa.
research focus
Connectomes are a window into how the structure of the brain’s network of neurons and non-neuronal cells is created by development and experience, and how this structure contributes to function. The field of connectomics has made great strides in our ability to collect these large datasets. However, extracting useful, generalizable principles from these connectivity maps remains a challenge. First, the massive size of these datasets requires analysis methods which scale but also provide interpretable summaries of connectivity patterns. Second, biological variability (e.g., region-to-region or animal-to-animal differences) and technological variability (e.g., errors in connectome reconstruction) can be difficult to disentangle, especially with few connectome samples. Third, analysis often requires relating connectivity to complex biological properties, such as neural morphology or subcellular features. Ben is interested in developing connectome analysis techniques which deal with these issues, as well as creating open-source software to facilitate this research.
