Currently a member of the scientific computing team at the Allen Institute for Neural Dynamics, Tom has worked on several projects since he joined AIBS in 2018, with understanding the diverse cross-modality properties of neuronal cell types as a common theme. He initially focused on optimization and simulation of biophysically detailed single-neuron models for neurons recorded by the Human Cell Types team. He also analyzed morphology, electrophysiology, and transcriptomics data from the same cells to demonstrate signatures of cell types across modalities and across species, with a focus on interneurons in cortical Layer 1.

Before joining the Allen Institute, Tom obtained a Ph.D. in Applied Mathematics at University of California, Davis, working with Professors Tim Lewis and Mark Goldman. For his dissertation research, he used computational and mathematical modeling to explore the effects of electrical synapses (gap junctions) on the synchronization of rhythmic neural activity, both spiking and subthreshold, showing how synchronization can depend critically on intrinsic cellular properties and vary across different cell types. Tom first became interested in modeling the brain during his undergraduate studies at Reed College, where he obtained a B.A. in mathematics and physics in 2009.